What if the most important question in education is not “What should students learn?” but “What conditions allow meaningful learning to emerge?”

That, I believe, is the central insight of Ron Burnett’s remarkable A Biography of Learning.

I recently submitted an eight-page review of the book to Interchange: A Quarterly Review of Education. This article summarizes the main argument of my review while I await the publication process.

Reading A Biography of Learning was a pleasure because it challenged me in unexpected ways. My own work has focused heavily on cognitive productivity, artificial intelligence, and the cognitive science of learning. Burnett’s book led me to reflect more deeply on something that can easily be overlooked: the educational environments, relationships, and cultural conditions that make meaningful learning possible in the first place.

Burnett offers something unusual: neither a conventional education textbook nor a technical treatise on cognitive science, but a deeply reflective exploration of learning as a lifelong, relational, and often unpredictable process. Rather than presenting learning as a linear progression toward predetermined outcomes, he invites us to see it as an evolving biography shaped by curiosity, relationships, uncertainty, culture, technology, and serendipity.

The book is especially timely. At a moment when education is being transformed by artificial intelligence, analytics, and growing demands for measurable outcomes, Burnett reminds us that not everything of educational value can be planned, standardized, or optimized. Genuine learning frequently emerges through exploration, conversation, unexpected connections, and serendipitous encounters.

One of the book’s great strengths is its resistance to false dichotomies. Burnett is not arguing against structure, assessment, or technology. Rather, he argues for balance—between structure and emergence, linearity and non-linearity, analytics and reflection, technological innovation and human relationships, and efficiency and exploration. Throughout the book, he returns to the idea that educators should devote as much attention to cultivating the conditions under which learning can flourish as to specifying the knowledge students are expected to acquire.

My review is overwhelmingly positive. At the same time, I offer one gentle critique. Given the book’s breadth and ambition, I would have welcomed greater engagement with research in educational psychology, cognitive science, and artificial intelligence—fields that increasingly illuminate how memory, motivation, retrieval, expertise, emotion, and learning environments interact. This omission is understandable. Burnett had already undertaken an ambitious project, and incorporating these literatures in depth might have required another volume. My review therefore points to several areas of educational psychology that are compatible with, and could further enrich, Burnett’s central vision.

Readers familiar with my own work will notice considerable common ground with ideas I develop in Cognitive Productivity. Burnett focuses primarily on the conditions and environments in which meaningful learning can emerge. Cognitive Productivity asks how individuals can use knowledge, tools, and relationships more effectively to solve problems, create valuable products, and improve themselves. It draws explicitly on cognitive psychology, educational psychology, affective science, and artificial intelligence. I see the two books as complementary: Burnett helps us think about the environments in which learning flourishes, while Cognitive Productivity examines how people can act effectively within those environments.

Ultimately, A Biography of Learning is a hopeful book. It reminds us that education is not merely the transmission of information, nor simply the production of measurable outcomes. It is the cultivation of people capable of continued growth, reflection, exploration, and participation in a changing world. In an era increasingly captivated by metrics and algorithms, Burnett makes a compelling case for preserving the complexity, humanity, and openness that make learning worth pursuing.

Readers interested in the practical implications of these ideas may also enjoy my more applied book, Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge, which examines how people can use contemporary knowledge tools more effectively. My forthcoming book, Discontinuities: Love, Art, Mind, extends related ideas into the domains of literature, film, and other forms of art.

If you are interested in education, cognitive science, artificial intelligence, or the future of higher education, I highly recommend reading Ron Burnett’s A Biography of Learning.

This essay develops one such perspective. It is not a survey of every theory of intelligence. Rather, it draws on an integrative design-oriented tradition in cognitive science and AI—associated with Herbert Simon, Allen Newell, Merlin Donald, Aaron Sloman, Keith Stanovich, Robert White, Ortony, Clore and Collins, and others—to ask how we should think about artificial intelligence today. My own contribution to that tradition began with my 1994 doctoral dissertation, Goal Processing in Autonomous Agents, and has continued through work on motivation, perturbance, cognitive productivity, and knowledge technologies.

I often encounter someone confidently asserting that artificial intelligence either is intelligent or isn’t really intelligent. The discussion rarely lasts long before both sides begin talking past each other. One person points to AI systems solving difficult problems, writing software, composing prose, or passing examinations. Another replies that they merely predict tokens, lack consciousness, have no values, or do not truly understand anything. These are not trivial objections. But they are usually made before the more basic scientific question has been addressed.

Whenever someone tells me that AI isn’t really intelligent, I ask two questions. First: how are you defining intelligence? Second: what theory of intelligence are you using? The first question sometimes produces an answer. The second almost never does. That is understandable. Outside cognitive science, we use the word intelligence without needing an explicit theory. We readily say that Einstein was more intelligent than the average person, that a raven is more intelligent than a pigeon, or that a dog is more intelligent than a worm. Ordinary language works well enough for ordinary purposes. But scientific concepts do not become adequate merely because we sharpen their dictionary definitions.

Much public discussion is concerned with verbal adequacy: finding the “correct” definition of intelligence. Cognitive science seeks theoretical adequacy: explanatory frameworks that organize observations, generate predictions, guide research, and inform design. Definitions summarize theories; they do not replace them. (Richard Feynman famously observed that knowing the name of a bird in every language tells us almost nothing about the bird itself. Likewise, defining “intelligence” more precisely does not, by itself, explain intelligent systems. Scientific understanding comes from theories that explain how they work. Definitions summarize theories; they do not replace them. Naming a phenomenon—even defining it carefully—is not the same as explaining it. One’s definition must appeal to other theoretical constructs.) Asking whether AI is intelligent without first specifying a theory of intelligence is rather like asking whether an animal is healthy without first having a theory of physiology. The question is not meaningless, but it is scientifically underdetermined.

The argument of this essay goes one step further. Even a theory of intelligence is not the deepest explanatory framework. Human-like intelligence is best understood within a broader computational theory of human-like autonomous agency.

Take-away

Before asking whether AI is intelligent, we need to define intelligence within the context of a scientific theory of intelligence. More fundamentally, we need a theory of human-like autonomous agency within which intelligence can be understood. As discussed at the end of this essay, the same considerations hold for “consciousness.”

Why “intelligence” became the wrong starting point

It is understandable that public discussion has gravitated toward intelligence. After all, the field itself is called Artificial Intelligence. The name naturally directs attention toward one aspect of minds while leaving others in the background. Had the founders instead chosen a name such as Artificial Autonomous Agents or Computational Agency, public discussion might well have developed differently. We might spend less time debating whether AI is “really intelligent” and more time asking what kinds of autonomous agents current systems are becoming, what motivational architectures they possess, what regions of the space of possible minds they occupy, and what new discontinuities they may eventually cross.

This is not merely a semantic point. Names encourage questions, and questions guide research. The historical label Artificial Intelligence has survived enormous changes in AI itself: from symbolic problem solving, theorem proving, expert systems, connectionism, reinforcement learning, robotics, and cognitive-affective architectures to today’s foundation models and increasingly autonomous software agents. The label still has practical value. But it can also mislead us into treating intelligence as a single property that systems either possess or lack.

Today’s AI landscape includes systems that converse fluently, prove theorems, control robots, generate software, diagnose diseases, compose music, retrieve information, plan actions, and coordinate activities over extended periods of time. These systems differ profoundly from one another. Some exhibit extraordinary linguistic competence but little endogenous motivation. Others pursue complex objectives but have relatively modest reasoning abilities. Some operate almost entirely reactively; others deliberate extensively before acting.

The more interesting scientific question is whether these systems instantiate different kinds of computational architectures. By a computational architecture, cognitive scientists mean the organization of interacting mechanisms that make intelligent or autonomous behaviour possible: mechanisms for perception, memory, learning, motivation, planning, action, communication, reflection, and so forth. Just as the architecture of a building concerns the organization of its parts, not merely the materials from which it is made, a computational architecture concerns how the components of a mind or artificial agent are organized and how they interact.

Once we adopt this perspective, it becomes less useful to ask whether “AI” as a whole is intelligent. “AI” is now an umbrella term for a rapidly expanding family of systems. The better question is what kinds of computational architectures these systems embody, and how those architectures compare with those of animals, humans, organizations, and possible future machines.

Take-away

The name Artificial Intelligence encourages us to focus on intelligence. A more productive scientific question asks what computational architectures current AI systems instantiate, and what kinds of autonomous agents they are becoming.

Autonomous agency: the deeper scientific question

A system may be impressive without being very autonomous. A calculator can outperform most humans at arithmetic. A chess engine can defeat grandmasters. A search engine can retrieve information faster than any person. These systems exhibit capabilities that matter. But a human-like autonomous agent is not merely a device that produces excellent outputs when prompted. It is a system that must regulate its own activity in a changing world under constraints of limited time, limited information, limited working memory, and limited resources including other people and computational resources.

My interest in these questions is not recent. More than thirty years ago, my doctoral dissertation, Goal Processing in Autonomous Agents, which I believe is even more relevant today than it was then, examined the computational requirements for systems capable of generating, managing, and pursuing top-level and derived goals under severe constraints of time, information, and computational resources. A top-level goal is not simply a subgoal produced by planning. It arises from the agent’s motivational architecture. Derived goals, by contrast, are generated in the service of other goals. Planning a trip, for example, may produce the derived goals of buying a ticket, packing a bag, and arranging transport to the airport. But the top-level motivation to travel—to visit a loved one, attend a conference, escape danger, or explore a new place—comes from elsewhere in the architecture.

This distinction is crucial for AI. Many AI systems can derive subgoals. Some can decompose tasks, make plans, seek information, revise intermediate steps, and monitor progress. That is significant. But deriving subgoals from an externally supplied task is not the same as generating, regulating, and revising one’s own top-level motivations. One of the most important discontinuities in the space of possible minds separates systems that merely pursue externally assigned goals from systems capable of generating, managing, revising, suspending, and abandoning their own top-level motives.

Notice how the question changes once we adopt this perspective. Instead of asking whether a system is intelligent, we ask what sort of autonomous agent it is. For example:

• Can it generate and regulate its own top-level goals or motivators, or does it merely pursue goals supplied by others?

• Can it derive, revise, suspend, and abandon subordinate goals as circumstances change?

• Can it revise priorities when new information arrives?

• Can it interrupt one activity because another has become more urgent?

• Can it improve its own competence over time?

• Can it reflect upon its own reasoning and modify it?

• Can it coordinate all this while operating under severe constraints of time, information, working memory, and computational resources?

These are not merely behavioural questions. They are architectural questions. They ask what kinds of mechanisms must exist inside a system, how those mechanisms interact, and what forms of control they make possible. Within such a framework, human-like intelligence may, to a first approximation, be characterized as the capacity to acquire, represent, assess, integrate, and apply knowledge in pursuit of multiple top level and derived motivators across changing environments with limited resources (time, knowledge, money, other people, etc.). This is not intended as a complete definition. It is a working characterization within a broader computational theory of human-like autonomous agency.

The explanatory order matters. Intelligence does not explain autonomous agency. Rather, autonomous agency explains why intelligent capacities are needed and how they must be integrated with perception, action, memory, motivation, executive control, affect, communication, and reflection. A human-like autonomous agent is not a disembodied head solving puzzles. It is a system situated in a world, contending with resources, opportunities, interruptions, needs, values, and competing motives.

Take-away

Human-like intelligence should be characterized within a theory of human-like autonomous agents. Such agents do not merely solve problems; they generate, manage, and pursue top-level and derived goals under resource constraints.

Figure 1. Levels of explanation and the space of possible minds:

Figure take-away

The question “Is AI intelligent?” belongs near the top of the hierarchy. Its answer depends on deeper theories of human-like autonomous agency, computational architecture, and the space of possible minds.

The space of possible minds

The preceding section moved the question from intelligence to human-like autonomous agency. That move is necessary, but not sufficient. We also need to resist another tempting simplification: the idea that minds can be arranged along a single scale from less intelligent to more intelligent. That picture is sometimes useful in ordinary life. It lets us say that one person solved a problem more intelligently than another, or that one animal has greater problem-solving ability than another. But as a scientific picture it is too flat.

Aaron Sloman has long argued that cognitive science and AI should explore the space of possible minds: a space of possible computational architectures, possible niches, and possible mappings between them. This is not merely a poetic phrase. It is a methodological warning. If there are many possible kinds of minds, then the task of cognitive science is not to find a single essence called intelligence, but to understand the requirements, architectures, trade-offs, and discontinuities that define different regions of that space. This is illustrated by the right side of Figure 1 above.

(My book in progress Discontinuities: Love, Art, Mind and already for sale will end with a chapter on discontinuities.)

This changes the AI question. The usual public debate asks whether AI has crossed a threshold into intelligence. A more sophisticated version asks how intelligent AI is. But the design-space perspective asks a different question: what kind of mind is this? That question does not assume a single ladder. It assumes a structured landscape containing many kinds of minds, many kinds of computational architectures, and many important discontinuities.

Some discontinuities are obvious once we notice them. A purely reactive system differs qualitatively from one capable of deliberation. A system that merely pursues externally supplied goals differs qualitatively from one that can generate and regulate its own top-level motivators. A system that can plan differs qualitatively from one that can monitor and modify its own planning. These are not merely differences in “amount of intelligence.” They are differences in architecture.

Merlin Donald’s work is especially important here. Donald did not treat human intelligence as merely more of the same animal intelligence. In A Mind So Rare: The Evolution of Human Consciousness, Donald described major transitions in human cognitive evolution, including new forms of consciousness and new ways of governing cognition over time. His distinction among sensory binding, short-term control, and intermediate or long-term governance is particularly useful for our purposes. It reminds us that human consciousness is not merely momentary awareness; it is a multilevel control system capable of sustaining projects, meanings, and symbolic structures over extended periods.

Donald’s point also helps explain why external symbolic systems matter. Writing, diagrams, maps, mathematical notation, books, databases, hyperlinks, and other knowledge technologies do more than store information. Properly integrated into human activity, they help sustain context, guide attention, coordinate long-term projects, support cognition over extended periods of time, and, in that sense, extend the functional reach of human consciousness.

External symbolic systems, such as writing, diagrams, maps, mathematical notation, and contextual information retrieval systems, do not replace working memory. Rather, they augment executive function by reducing the need to retain arbitrary contextual information internally and by making relevant information rapidly retrievable when needed. Our Hookmark Mac and iPhone app, for example, provides bidirectional links that help users rapidly recover the context surrounding digital resources, thereby supporting planning, problem solving, and other executive functions. This extends the intermediate and long-term awareness of human consciousness, concepts developed by Merlin Donald.

This Donaldian point matters for AI because it reminds us that human-like intelligence is not merely a matter of solving more difficult problems. Human cognition acquired new architectural possibilities through gesture, imitation, language, external symbolic storage, and culture. These were not simply increments on a scale. They changed what kinds of minds humans could have, introducing successive discontinuities in the space of natural minds.

Take-away

Minds do not occupy a single scale from unintelligent to intelligent. They occupy a structured space of possible computational architectures, with important discontinuities between reactive, deliberative, motivational, reflective, culturally scaffolded, and future artificial forms of mind.

Computational architectures for human-like autonomous agents

To speak of computational architectures is not to indulge in metaphor. It is to ask what kinds of organized mechanisms are required for human-like autonomous agency. Such agents must perceive, act, learn, remember, generate motivators, choose among competing demands, deliberate, monitor themselves, interact with their environments, and develop over time. No single mechanism—language modelling, reinforcement learning, planning, memory retrieval, symbolic inference, neural pattern completion—can by itself explain such an agent.

This was one of Sloman’s central points. Work in AI and cognitive science often studies components: vision, language, learning, planning, motor control, memory, or reasoning. Those studies are valuable. But the deeper problem is how such components can be assembled into a coherent working system. Sloman emphasized that the most important artificial and evolutionary “design” choices for human-like agents concern the overall architecture, because detailed questions about mechanisms and representations are best addressed in the context of a global design.

A useful first approximation is the distinction between reactive processes, management processes, and meta-management processes (see chapter 4 of my thesis). A reactive subsystem responds quickly, often automatically, to internal or external conditions. Management processes, including deliberative processes, construct, compare, and evaluate possible actions or plans before committing to them. Meta-management processes, including reflective processes, monitor and evaluate management processes themselves. Reflection adds another level: the ability to notice that one’s own thinking is going poorly, that one is wasting time, that a strategy is biased, that a problem should be postponed, or that a previously adopted goal should be questioned.

Cognitive psychologists often use the term executive functions to refer to the family of processes responsible for regulating thought and action. In the present framework, executive functions are implemented primarily by management processes and meta-management processes. They include, among others, evaluative functions such as assessing the importance, urgency, insistence, intensity, relevance, and expected consequences of competing motivators; deliberative functions such as planning, scheduling, prioritization, conflict resolution, commitment to action, and the scheduling of deliberative and reflective activity; executive control functions such as directing attention, inhibiting inappropriate thoughts or actions, regulating behaviour, allocating computational resources, and selecting when to engage in Type 1 or Type 2 reasoning (see Dual-Process Theories of Higher Cognition - Perspectives on Psychological Science - APS); reasoning, problem-solving, and explanatory functions such as inference, hypothesis generation, explanation, analogical reasoning, diagnosis, and planning under uncertainty; meta-management (reflective) functions such as monitoring one’s own thinking, detecting errors, recognizing bias, revising strategies, changing mental sets, and deciding when to continue, interrupt, postpone, or abandon ongoing cognitive activity; and ambiguity-management functions such as recognizing uncertainty, tolerating ambiguity, gathering additional evidence, maintaining multiple competing interpretations, and delaying commitment until sufficient evidence is available.

Most of these executive functions rely heavily on working memory: the limited-capacity workspace in which information is actively maintained, manipulated, integrated, and evaluated. Executive functions do not operate in isolation. They continuously interact with working memory, long-term memory, perception, motivator generators, insistence-based filters, and external symbolic systems.

These distinctions should not be mistaken for a rigid pipeline. (Illustrated in chapter 4 of my thesis). The mind is not a factory line in which perception hands a package to motivation, which hands it to deliberation, which hands it to action. A computational architecture specifies possible interactions among mechanisms, not a single mandatory sequence of processing. Perception, memory, internal monitoring, communication, motivator generation, deliberation, and reflection can operate asynchronously and influence one another in multiple directions. Human-like cognition is event-driven, interruptible, opportunistic, and often messy.

That messiness is not a defect in the theory. It is one of the requirements any serious theory must explain. A system that waits for a tidy sequence of central decisions before responding to the world will not be very human-like. Human-like autonomous agents need fast reactive responses, slower management processes, meta-management processes that can inspect and regulate management, working memory to support active cognition, and multiple mechanisms that can generate potential motivators and interrupt ongoing activity when something more urgent or relevant arises.

These distinctions are directly relevant to AI. A system that produces fluent answers may lack robust deliberative management. A planning agent may lack reflective self-monitoring. A reinforcement-learning system may learn policies without being able to articulate, evaluate, or revise its own reasons. A chatbot may simulate reflection linguistically without possessing a stable architecture for self-monitoring across time. Conversely, future AI systems may combine language, planning, memory, perception, action, tool use, working memory, and self-monitoring in ways that cross new architectural discontinuities.

The integrative design-oriented question is therefore not “Does it seem intelligent?” but “What computational requirements for human-like autonomous agency does this system satisfy, and what architecture would explain both its capabilities and its limitations?” Once we ask that question, successes and failures become informative. Hallucinations, brittle planning, perseveration, lack of initiative, overconfidence, susceptibility to misleading prompts, and inability to manage long-term projects are not merely performance glitches. They are clues about architecture.

Take-away

Human-like intelligent behaviour must be explained in relation to the computational architecture that produces it. Such an architecture is not a rigid pipeline, but an interacting system of reactive, motivational, executive, reflective, affective, memory, learning, and action processes.

Figure 2. Sketch of an integrative design-oriented computational architecture for human-like autonomous agency :

This figure depicts interacting computational processes rather than a processing pipeline. It updates and extends a related architecture, H-CogAff, presented in Part II of Cognitive Productivity: Using Knowledge to Become Profoundly Effective and in several papers by Aaron Sloman (example: Sloman 2006, “How many separately evolved emotional beasties live within us?”).

Where do goals come from?

We can now ask a question that has received surprisingly little attention—not only in public discussions of AI, but also within cognitive science itself.

Where do goals come from?

The question sounds almost naïve until one tries to answer it. Much of cognitive science has concentrated on perception, memory, language, learning, reasoning, and planning. AI has likewise devoted enormous effort to algorithms for search, optimization, reinforcement learning, and, more recently, foundation models and autonomous software agents. Yet all of these capabilities presuppose that the agent has something to perceive, remember, reason about, or plan for.

The challenge is deeper than explaining how an agent pursues goals. It is to explain how a human-like autonomous agent generates, organizes, prioritizes, transforms, and sometimes abandons goals in the first place. That, I believe, remains one of the central scientific challenges for both AI and cognitive science.

One influential tradition has sought to explain motivation in terms of reward maximization or utility. Such ideas have proved enormously fruitful, particularly in economics and reinforcement learning. But they provide only part of the story. Human motivation is far richer than the pursuit of a single utility function. We pursue knowledge, beauty, friendship, justice, truth, curiosity, craftsmanship, status, duty, exploration, love, and countless other ends. These motivations often cooperate, but they also conflict, evolve, and reorganize throughout a lifetime. (See Psychological Hedonism Meets Value Pluralism: An Integrative Design-oriented Perspective)

From the perspective developed in this essay, this diversity is not an inconvenience to be abstracted away. It is a clue about architecture.

One thing is certain: the human mind-brain contains multiple motivator generators (generators of goals, motives, projects, standards, and attitudes).

Take-away

The central problem is not merely how intelligent systems pursue goals. It is how human-like autonomous agents generate, organize, regulate, and transform the motivations that make goal pursuit possible.

Architecture-based motivation

Aaron Sloman introduced the profound idea of architecture-based motivation to capture precisely this point. Motivation should not be viewed merely as the output of a single decision-making process, nor as the consequence of one global reward signal. Rather, motivation is itself produced by the architecture.

This becomes clearer if we consider ordinary human life. A person may suddenly remember an unanswered email, become curious about an unexpected observation, worry about a child, notice an inconsistency in an argument, recall an unfinished promise, feel compelled to repair a broken object, or become fascinated by an idea encountered while reading. Some of these motivators arise from biological needs. Some arise from social commitments. Some arise from learned values, professional responsibilities, personal projects, aesthetic preferences, moral standards, or attachment structures. Others arise because perception, memory, reflection, or internal monitoring has detected something relevant.

They are not all computed by a single deliberative process asking, “What action maximizes expected reward?” Nor are they all derived through means-end reasoning. Rather, they are generated continuously by multiple asynchronous motivator-generating processes distributed throughout the computational architecture. These motivator generators generate, modify, reactivate, and regulate motivators; they also assign insistence and intensity. Deliberative processes may subsequently assess, elaborate, reconcile, prioritize, transform, or inhibit these generated motivators, but deliberation is only one contributor to motivation—not its sole source.

This distinction is fundamental. It moves motivation from the periphery of cognitive science to its architectural core. It also helps explain why human-like autonomous agency cannot be modeled adequately as a sequence beginning with a goal and ending with an action. Goals themselves are products of architecture. They arise, compete, fade, recur, and sometimes become urgent through interacting processes distributed across the agent.

The distinction between top-level and derived goals now becomes indispensable. Derived goals are generated in the service of other goals. If I decide to submit a paper, I may derive goals to revise the abstract, check references, format figures, and send an email. But the top-level motivation to write the paper may arise from effectance (including curiosity), professional commitment, desire to contribute knowledge, social obligation, or some mixture of values. The architecture does not merely reason from goals. It generates and manages the goals that reasoning serves.

This is also where contemporary AI becomes interesting. Many current systems are increasingly capable of deriving subgoals from prompts. They can decompose tasks, call tools, monitor progress, revise plans, and recover from failures. These are impressive achievements. But this should not be confused with possessing a rich architecture of endogenous motivation. A system that derives subgoals from a user prompt is architecturally different from one that can generate top-level motivators of its own, assign insistence and intensity to them, reconcile them with existing goals, standards, and attitudes, and regulate them over time.

We can already imagine such systems. Consider a household robot that is vacuuming when it detects that its owner has collapsed and is in physiological distress. Rather than merely continuing its assigned task, it asynchronously generates a new top-level motivator to summon medical assistance. Or imagine that it detects an intruder entering the home. It generates a different top-level motivator—to protect its owner—which immediately interrupts its previous activity and recruits perception, deliberation, communication, and action toward the new concern. These are not merely new subgoals in service of vacuuming. They are new top-level motivators generated by the architecture itself in response to changing circumstances.

Take-away

Human-like autonomous agents do not merely pursue goals. They require architectures that generate, assess, prioritize, inhibit, and transform motivators. This is why motivation is not an optional add-on to intelligence.

Insistence, intensity, and executive controllability

It is tempting to speak of a motivator’s “strength.” But this is too crude. Several dynamic properties of motivators need to be distinguished.

Insistence is the degree to which a motivator persistently competes for limited cognitive resources, including attention, working memory, executive processes, and reflective processes. Intensity is the degree to which a motivator tends to recruit or energize behavioural systems, thereby increasing the disposition toward overt action. Importance is the value attributed to the motivator by executive functions. (These dimensions are expanded upon in chapter 3 of my thesis and briefly in Sloman, 1987) Executive controllability is the extent to which management and meta-management processes can regulate a motivator, its behavioural expression, or its influence on cognition.

In humans, these dimensions can dissociate. In obsessive-compulsive disorder, an intrusive thought about harming a loved one may have very high insistence: it repeatedly intrudes into attention and working memory. But it may have very low (behavioural) intensity: the person is horrified by the thought and has little or no disposition to act on it. Conversely, some action tendencies may have high intensity but low executive controllability, as in many motor tics associated with Tourette syndrome. Reflexively withdrawing one’s hand from a hot stove has high intensity and low deliberative control, but it may not be very insistent once the action is complete. One’s management processes may assign high urgency and/or high importance to a motivator that is not yet very insistent or intense (e.g., the important need to lose weight might not drive action). AI may or may not support all these distinctions.

These distinctions help explain why understanding motivation requires more than assigning a single strength to a motive. Some motivators dominate thought without producing action. Others produce action without prolonged thought. Still others quietly shape long-term life projects without constantly interrupting attention.

Take-away

Motivators differ not merely in “strength,” but in insistence, intensity, and executive controllability. This distinction is crucial for understanding perturbance, intrusive thought, impulsive action, self-control, and future AI motivation.

Motivators: goals, standards, and attitudes

Once we recognize that motivation is architecturally generated, another question immediately arises: what kinds of motivators must a human-like autonomous agent be able to generate and regulate?

Here it is helpful to draw on Ortony, Clore and Collins’ The Cognitive Structure of Emotions, and on the treatment of values in my Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge. I use the term motivator to refer to a value-bearing control state: something the agent treats as relevant or insistent, consciously or unconsciously, explicitly or implicitly. Motivators help drive assessment, attention, goal formation, planning, and action. In my terminology, motivators also draw attention to the fact that values are not merely labels attached to things. They can spawn assessments and other motivators, including goals.

To a first approximation, motivators come in three different flavors: goals and projects, standards, and attitudes. This taxonomy is partly based on Ortony, Clore and Collins, and I developed it in Principle 1 of Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge as a practical way of understanding values, motivation, and self-governance.

Goals and projects are states the agent is willing to work to achieve, preserve, accelerate, delay, or avoid. They vary in desirability. A scientist may have the goal of explaining a phenomenon. A student may have the goal of mastering a concept. A robot may have the goal of reaching a destination while conserving energy. Goals can be top-level or derived. They are often arranged not in simple hierarchies but in complex networks of means and ends.

Standards are norms, rules, ideals, or constraints that the agent treats as things that ought or ought not to hold. They vary in praiseworthiness and blameworthiness. “Be honest,” “do not mislead the reader,” “keep promises,” “respect evidence,” and “do not endanger others” are standards. Standards regulate conduct, constrain goal pursuit, and provide the basis for emotions such as guilt, shame, admiration, resentment, and indignation.

Attitudes are likes, dislikes, preferences, interests, aversions, tastes, fascinations, and affective biases. They vary in appeal. One may like a melody without having the goal of hearing it now; dislike a food while having the goal of eating it for health reasons; or be fascinated by a topic before having any explicit project involving it. Attitudes often guide attention and learning before explicit goals are formed.

These three kinds of motivators interact continuously. Standards can generate goals: “I ought to apologize, therefore I will apologize.” Goals can modify attitudes: “I began studying mathematics for instrumental reasons, but came to love it.” Attitudes can generate goals: “That topic fascinates me; I want to learn more.” Standards can constrain goals; goals can override attitudes; attitudes can bias the selection of goals; and all three can influence the assessment of events, actions, objects, people, and ideas. Human-like motivation is therefore not a simple hierarchy of goals. It is a dynamic network of interacting motivators. However, these forms of value can vary independently as well. One may view a state of affair as desirable but not praiseworthy, for instance.

This point is crucial for AI. Human-like intelligence is not merely the capacity to pursue goals. It also involves the capacity to generate, represent, revise, coordinate, and sometimes reject networks of goals, standards, and attitudes. A system that optimizes for a goal but lacks standards and attitudes may be powerful, but it is not human-like in the relevant sense. Conversely, a future AI system with goals, standards, and attitudes would raise a much richer set of questions about agency, rationality, alignment, responsibility, and the space of possible minds.

Take-away

Human-like autonomous agents are not merely goal-directed. They are also norm-governed and attitude-shaped. Human-like intelligence requires the capacity to generate and regulate networks of goals, standards, and attitudes.

Effectance, meta-effectiveness, and epistemic agency

This architectural perspective also helps explain why intelligence should not be understood merely as a collection of abilities. Highly intelligent people do not simply solve problems well. They often display a persistent tendency to become more capable over time.

Robert White called one important aspect of this tendency effectance): the motivation toward competence. In Cognitive Productivity: Using Knowledge to Become Profoundly Effective, I argued that effectance is better understood architecturally than behaviourally. Effectance is not merely a conscious or even unconscious desire to improve oneself. Rather, it is a propensity to generate top-level motivators whose pursuit tends to increase competence, effectiveness, and understanding.

Someone driven by effectance may decide to learn a new mathematical technique, master a musical instrument, study philosophy, understand a difficult scientific paper, improve a piece of software, refine a workflow, or simply ask a better question. None of these activities need be motivated explicitly by the thought “I want to become more intelligent.” Yet together they gradually reshape the architecture of the mind.

Human-like intelligence therefore includes a developmental dimension. Intelligent agents do not merely use knowledge; they acquire it, organize it, evaluate it, integrate it, apply it, and continually improve their capacity to use it effectively. This developmental perspective lays part of the foundation for what I believe should become an integrative design-oriented theory of epistemic agency: a theory explaining how human-like autonomous agents acquire, organize, evaluate, integrate, and apply knowledge in ways that continually increase their competence and effectiveness.

One might think of effectance as the motivation to become more competent, and meta-effectiveness as the skills and dispositions of becoming better at becoming effective. Meta-effectiveness encompasses not only the acquisition of knowledge and skills, but also the cultivation of the dispositions, habits, executive functions, and external symbolic supports required to apply them appropriately. Thus effectance is a subset of meta-effectiveness. Meta-effectiveness is the core concept of my first book, Cognitive Productivity: Using Knowledge to Become Profoundly Effective.

This perspective aligns naturally with Keith Stanovich’s distinction between intelligence and rationality. Rational thought depends not only on cognitive abilities, but also on knowledge and the dispositions to deploy those abilities when appropriate. Similarly, meta-effectiveness concerns developing the knowledge, skills, and dispositions that enable autonomous agents to use what they know effectively in pursuit of their goals, standards, and attitudes. Some educational and practical implications of this perspective are explored in Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge, where I propose seven principles of meta-effectiveness — augmenting human cognition through knowledge resources and information technology.

A competent agent can solve problems. An effectant agent seeks to become more competent. A meta-effective agent systematically develops the knowledge, skills, dispositions, executive functions, and external symbolic supports that make continual improvement possible.

Take-away

Human-like intelligence is not merely current problem-solving ability. It includes motivational architectures, including effectance, that continually develop competence. Meta-effectiveness extends this developmental orientation into the disciplined cultivation of knowledge, skills, dispositions, executive functions, and external supports.

Attachment structures

Human-like agency is not merely individual problem solving. Humans form enduring relationships with people, places, communities, disciplines, projects, and ideals. These relationships are not just memories. They reshape the architecture.

In a 1996 paper with Ian Wright and Aaron Sloman, we modernized the notion of attachment structures in the context of a design-oriented analysis of grief. (At the time we used the expression “design-based.”) The central point was that an attachment is not merely a feeling. It is a distributed computational structure embedded throughout an autonomous agent. Such a structure may include stored knowledge, expectations, preferences, habits, plans, predictive models, and specialized motivator generators that influence perception, deliberation, and action.

Attachment structures illustrate an important principle of human-like intelligence: long-term relationships do not merely produce memories. They reorganize the motivational architecture itself. An attachment to a person may alter what one notices, what one worries about, what one plans, what one treats as important, and what one feels compelled to protect. An attachment to a research programme may generate questions, projects, standards, and intellectual commitments over decades. An attachment to a moral ideal may repeatedly generate motivators that override convenience, fear, or short-term reward.

Attachment structures gradually modify and create motivator generators. They therefore influence which goals, standards, and attitudes arise in future situations, how insistent those motivators become, how intense their action tendencies are, and how readily they gain access to executive resources. This helps explain why attachment can be so powerful, and why grief, betrayal, loss, or separation can become perturbing.

This also sets a high bar for AI. A system that remembers a user is not thereby attached to that user. A human-like robot capable of forming attachments would need to develop distributed structures that alter its motivator generators, predictive models, standards, priorities, and executive control over time. Such a system would not merely store a profile. It would become differently organized because of a relationship.

Take-away

Attachment structures are distributed computational organizations that reshape motivator generators over time. Human-like AI would require more than memory of persons or projects; it would need architectures capable of forming, maintaining, revising, and sometimes dismantling attachments.

Designed and emergent affect

We can now directly address a topic that public discussions of intelligence often mishandle: emotion. It is common to treat emotion as the opposite of intelligence, or as something that disrupts rational thought. That picture is far too simple. In a human-like autonomous agent, affect is not an optional decorative layer added after cognition has done its work. It is part of the control architecture through which values, needs, concerns, and motivators influence attention, deliberation, action, and learning.

However, we should not treat “emotion” as a single thing either. Some affective mechanisms may be explicitly built into an architecture, whether by evolution or by human designers. Alarm mechanisms, pain systems, attachment mechanisms, curiosity mechanisms, reward systems, and other specialized motivator generators are examples of computational mechanisms that asynchronously generate or modify motivators. They help determine what the agent notices, avoids, approaches, protects, repairs, learns, or pursues.

Other affective phenomena are not best understood as built-in modules. They emerge from interactions among architectural components. This distinction was central to Aaron Sloman’s early work on computational theories of emotion. (See Sloman & Croucher(1981)- You don’t need a soft skin to have a warm heart and Sloman & Croucher (1981) - Why robots will have emotions.)

The point is not that programmers must insert a “fear module,” a “grief module,” or a “jealousy module” into an intelligent system. Rather, if an architecture contains specialized motivator generators, mechanisms for assigning insistence and intensity, limited cognitive resources, interruption mechanisms, learning, memory, executive functions, and reflective processes, then some emotional phenomena, including mental perturbance, may emerge as system-level patterns.

Mental perturbance is a state in which one or more highly insistent motivators repeatedly recruit attention and other cognitive resources, making it difficult for the agent to disengage and sustain alternative activities. It is not merely an emotion, nor merely a thought pattern. Mental perturbance, an architectural phenomenon, is produced when motivators, insistence, attention, working memory, executive functions, and limited computational resources interact over time. Perturbance may manifest as worry, rumination, grief, craving, obsessive planning, anger, shame, or other forms of persistent mental preoccupation, depending on the motivators involved. In particular, this architectural dynamic provides an architectural explanation for repetitive thought: highly insistent motivators repeatedly gain access to executive resources, making disengagement difficult despite competing goals, standards, and attitudes.

Thus, this perspective complements psychological theories of rumination and worry, and more generally Watkins’ (2008) framework of repetitive thought, by proposing a computational architectural account of why repetitive thought occurs: it reflects the recurrent recruitment of limited executive resources by highly insistent motivators.

Perturbance is not another component in a computational architecture. Rather, it is an emergent architectural phenomenon arising from interactions among motivators, executive functions, working memory, attention, and limited computational resources. This is one reason perturbance is theoretically useful. Rather than explaining persistent worry, rumination, grief, craving, or obsessive thought by positing a separate cognitive module, it explains them as recurrent patterns emerging from the dynamics of the architecture.

This point matters for AI as much as for cognitive science. We should not merely ask only whether an AI system “has emotions.” That question is too crude. We should ask which affective control mechanisms are explicitly designed into the system, which motivational processes it contains, whether any of its internal dynamics can generate persistent concern-like states, and which emotional phenomena, if any, could emerge from its architecture. The answer may differ radically across systems.

Take-away

Affect is not the enemy of intelligence. In human-like autonomous agents, some affective mechanisms are architecturally designed, while some affective phenomena, such as mental perturbance, emerge from interactions among motivators, insistence, attention, working memory, executive functions, and limited resources.

Intelligence is not rationality

We are now in a position to address another common confusion. Intelligence is often treated as though it implied rationality. If someone is highly intelligent, we expect them to think well, make good decisions, update their beliefs, avoid foolish errors, and resist obvious biases. Yet everyday experience and psychological research both show that this expectation is unreliable. Highly intelligent people can reason badly. They can be dogmatic, impulsive, biased, overconfident, inattentive to evidence, or unwilling to reconsider cherished beliefs.

Keith Stanovich has done more than almost anyone to clarify this distinction. In What Intelligence Tests Miss: The Psychology of Rational Thought, he argues that intelligence tests measure important cognitive abilities, but they do not adequately measure rational thought. People may have high cognitive ability while lacking the knowledge or thinking dispositions required to seek disconfirming evidence, consider alternatives, override impulsive responses, or use probabilistic reasoning when appropriate.

The architectural perspective developed here helps explain why Stanovich’s distinction is so important. Earlier, we distinguished between management processes and meta-management processes. Management processes construct, compare, and evaluate possible actions or beliefs. Meta-management processes monitor and regulate management processes themselves; they notice that one is reasoning poorly, wasting time, perseverating, being biased, or pursuing the wrong goal. But rationality depends not merely on having reflective mechanisms. It also depends on knowledge, values, dispositions, and motivations to recruit them when needed.

Stanovich’s theory of rationality fits naturally into a broader theory of human-like autonomous agency. In fact, he proposed an information processing architecture that is inspired by and similar to (but simpler than) Sloman’s H-CogAff architecture adapted here. (See figure 2.1, page 33 of Stanovich’s 2011 book, Rationality and the reflective mind). A person may possess the cognitive ability required to solve a reasoning problem, yet fail to engage the reflective processes that would lead to a better answer. They may be tired, anxious, angry, socially pressured, overconfident, or insufficiently motivated to think carefully. They may also lack effectance with respect to reasoning itself: the motivation to improve their own thinking habits, learn from errors, and cultivate better epistemic practices.

Thus, rationality is not a mysterious extra faculty added to intelligence. It depends on an architecture in which reflective processes, motivation, values, norms, knowledge, skills, and learned dispositions interact.

This also illustrates why perturbance matters. A highly perturbed mind (say: in love, angry or experiencing grief) may have ample intelligence and even strong reflective capacity, yet still struggle to reason well because highly insistent motivators keep capturing attention and working memory. Worry, anger, craving, shame, or grief can repeatedly redirect cognitive resources toward a dominant concern. In such cases, irrationality does not arise from lack of intelligence alone. It arises from the dynamics of the architecture.

The same lesson applies to AI. An AI system may perform well on reasoning benchmarks while lacking robust mechanisms for epistemic self-regulation. It may produce plausible answers without appropriately monitoring uncertainty. It may revise plans without understanding when to question the goals that generated them. It may simulate reflection in language without possessing stable dispositions to use reflective processes across contexts. Again, asking whether AI is intelligent tells us too little. We must also ask what kind of rationality, if any, its architecture supports.

Take-away

Intelligence and rationality are distinct. Rationality depends not merely on cognitive ability, but on reflective processes, thinking dispositions, motivational support, values, knowledge, and the regulation of perturbing concerns.

What this means for today’s AI

We can now return to the question with which we began. Is AI intelligent?

The answer is not a simple yes or no. Some AI systems exhibit some forms of intelligence, in some respects, according to some scientifically defensible theories of intelligence. Large language models can integrate information, generate explanations, write code, summarize documents, translate languages, and solve many problems that would once have seemed to require intelligence. Artificial autonomous agents can decompose tasks, call tools, maintain intermediate state, and pursue objectives over time. Robots can perceive, act, adapt, and learn in physical environments.

But these facts do not settle the question. They refine it.

The issue is not whether a system has crossed a single threshold called intelligence. The issue is which computational requirements for human-like autonomous agency it satisfies, which it lacks, and what kind of architecture explains its pattern of successes and failures. What are its capabilities and mechanisms that go beyond humans? A system may be linguistically fluent without being reflectively rational. It may derive subgoals without generating its own top-level motivators. It may optimize actions without possessing standards or attitudes. It may simulate emotional language without having affective control mechanisms or emergent perturbance. It may display competence without effectance or meta-effectiveness.

This is why both triumphalist and dismissive claims about AI tend to mislead. It is not illuminating to say simply that AI is intelligent. Nor is it illuminating to say that it is “not really intelligent.” Both statements compress too much. They ignore the space of possible minds.

A more useful approach asks:

• What kind of AI system are we discussing?

• What computational architecture does it instantiate?

• Which forms of human-like intelligence does it exhibit?

• What kinds of motivators, if any, does it generate?

• Can it regulate top-level and derived goals?

• Does it possess standards and attitudes, or merely optimize supplied objectives?

• What reflective mechanisms does it have?

• What thinking dispositions does it reliably display?

• What forms of rationality does its architecture support?

• Can it form attachment structures?

• Does it exhibit effectance or meta-effectiveness?

• Where does it lie in the space of possible minds?

These questions are more difficult than asking whether AI is intelligent. They are also more fruitful.

Take-away

The scientifically useful question is not whether AI is intelligent in general. It is which aspects of human-like intelligence and autonomous agency particular AI systems exhibit, and which computational architectures explain them.

An integrative design-oriented approach

The perspective developed here reflects what I have elsewhere called an integrative design-oriented approach to cognitive science and AI. The phrase matters. It is not merely the traditional design stance applied to minds. It is an attempt to integrate multiple disciplines, multiple levels of explanation, and multiple design requirements in order to understand human-like autonomous agents.

The integrative design-oriented approach begins not with dictionary definitions, but with requirements. What must an architecture be able to do in order to support human-like autonomous agency? It must perceive, act, learn, remember, generate motivators, evaluate alternatives, manage goals, use working memory, reflect on its own processes, regulate affect, develop competence, form attachments, interact with external symbolic systems, and coordinate with other agents. No single discipline can explain all of this. Psychology, AI, neuroscience, philosophy, anthropology, education, evolutionary theory, and software design all have something to contribute.

The integrative design-oriented approach replaces arguments about labels with questions about requirements, mechanisms, architectures, development, and emergence. It seeks not merely to classify minds, but to explain how different forms of autonomous agency become possible.

That, to my mind, is the deeper lesson AI is forcing upon us. Artificial intelligence has not simply challenged our understanding of machines. It has challenged the adequacy of our theories of mind. If we want to understand AI, we need better cognitive science. And if cognitive science wants to understand minds, it needs richer theories of computational architecture, autonomous agency, motivation, affect, executive function, working memory, attachment, rationality, and development.

Take-away

An integrative design-oriented approach asks what computational requirements and architectures make human-like autonomous agency possible. It treats intelligence as one aspect of a broader scientific problem.

Large language models have changed the debate

It would be a mistake to read this essay as minimizing the achievements of modern AI, particularly large language models such as ChatGPT, Claude, Gemini, and others. They represent one of the most significant developments in the history of artificial intelligence.

These systems can explain complex ideas, summarize books and scientific papers, generate software, translate between languages, critique arguments, adapt their writing style to different audiences, brainstorm new ideas, tutor students, help researchers explore unfamiliar literatures, and collaborate with people on extended intellectual projects. In my own work, including writing this essay, I have found ChatGPT to be an extraordinarily valuable thinking partner for brainstorming, criticism, literature exploration, and improving scientific writing. These are not trivial accomplishments. They deserve to be recognized as genuine forms of intelligent information processing.

Indeed, one of the most remarkable features of large language models is not merely what they can do in isolation, but what they can accomplish in sustained interaction with human users. A productive dialogue often becomes a joint cognitive process in which the human contributes goals, background knowledge, judgment, and evaluation, while the AI contributes retrieval, synthesis, reformulation, analogy generation, and the rapid exploration of alternative ideas. The result is frequently better than either participant could have achieved alone.

Recognizing these remarkable capabilities, however, does not bring us any closer to answering the question, “Is AI intelligent?” On the contrary, it exposes the inadequacy of the question. Large language models exhibit extraordinary strengths in some forms of cognition while remaining limited in others. They have therefore made it even more important—not less—to distinguish among different forms of intelligence, different theories of intelligence, and different computational architectures.

Ironically, the success of large language models strengthens rather than weakens the central argument of this essay. They have shown that intelligent behaviour can emerge in ways that many researchers did not anticipate. Rather than forcing us to abandon cognitive science, they challenge us to develop richer theories capable of explaining both biological and artificial forms of intelligence. The scientific task remains the same as the core of this essay argues: It is no longer to decide whether AI is or is not intelligent. It is to understand what kinds of intelligence different systems possess, how those capacities arise, and how they can best complement and extend human intelligence.

So, is AI intelligent?

So where does all this leave the original question?

One should no longer feel compelled to answer the question “Is AI intelligent?” with either a simple yes or no, or by asking to what quantitative degree it is intelligent. More scientifically productive questions concern the computational requirements for different forms of autonomous agency, the architectures that satisfy those requirements, and the capabilities, limitations, developmental trajectories, and emergent phenomena that follow from those architectures.

Some AI systems exhibit some forms of intelligence, in some respects, according to some scientifically defensible theories of intelligence. Others do not. Future systems will almost certainly occupy regions of the space of possible minds that have never previously existed on Earth.

The question itself is therefore not wrong. It is simply incomplete. Before we can answer it responsibly, we must ask what kind of AI system we are talking about, what theory of human-like intelligence we are using, what computational architecture the system embodies, what forms of value and motivation it possesses, what reflective capacities it exhibits, and where it lies in the space of possible minds.

Those questions do not make the debate disappear. They transform it from an argument about words into a scientific inquiry about minds.

The goal is not to settle the meaning of the word “intelligence.” It is to understand the architectures that make different forms of intelligence and autonomous agency possible.

The next time someone tells you that AI is—or is not—really intelligent, you could ask: What kind of AI? According to what theory of intelligence? And what computational architecture are we talking about?

That is where a serious conversation can begin.

What about consciousness and emotions?

This essay has focused on intelligence and autonomous agency. The same points I made here apply to concepts of consciousness and emotion. In A Mind So Rare: The Evolution of Human Consciousness, mentioned above, Merlin Donald made the same case for consciousness: there is a space of possible minds supporting different forms of consciousness. It’s not a matter of “this animal or machine has consciousness” or “it doesn’t have consciousness,” but what forms of consciousness does the agent have?

Similarly, it is silly to debate whether an AI agent can experience emotions or not without qualifying the question with respect to a particular integrative design-oriented theory of emotion.

Why You Can’t Make a Machine That Feels Pain

This paper was partly inspired by Daniel Dennett’s paper, “Why You Can’t Make a Machine That Feels Pain” (answer because the concept of pain is polymorphous). Here the same idea/pattern is used, generalized and extended and applied to intelligence and consciousness.

Glossary of Key Concepts

This essay develops and integrates terminology from AI, cognitive science, psychology, philosophy, and education. The following glossary summarizes key concepts as they are used here. Many are adapted from prior work; some are refinements introduced in this essay. This could be extended, e.g., to define the forms of awareness specified by Donald in A Mind So Rare (selective binding, short-term control, and intermediate- and long-term governance), rationality and other key terms.

• Attachment structure. A distributed computational organization that develops through repeated interaction with particular people, projects, organizations, places, or ideals. Attachment structures influence perception, memory, motivator generation, insistence assignment, planning, action, and executive control, thereby reshaping the motivational architecture over time.

• Autonomous agent. A system capable of perceiving, acting, learning, generating and regulating motivators (both top-level and derivative), managing competing demands, and sustaining coherent behaviour over time under constraints of limited information, time, working memory, and computational resources.

• Cognitive productivity. Efficience and effectiveness in using knowledge to solve problems, acquire new knowledge, develop expertise and deliver services.

• Computational architecture. The organization of interacting computational mechanisms that together enable an autonomous agent to function. An architecture specifies interacting components and processes, not a fixed sequence of operations.

• Effectance. The architecture-based motivation to become more competent. Effectance contributes to learning, self-improvement, and the development of expertise.

• Epistemic agency. The capacity of an autonomous agent to acquire, organize, evaluate, integrate, apply, and improve knowledge in pursuit of its motivators. More generally it involves cognitive productivity.

• Executive controllability. The extent to which management and meta-management processes can regulate a motivator, its behavioural expression, or its influence on cognition.

• Executive functions. The family of management and meta-management processes responsible for regulating affect, cognition, motivation and behaviour. In this paper, executive functions are understood architecturally rather than as a single faculty.

• Human-like autonomous agency. The capacity to function as a human-like autonomous agent (see “autonomous agent” above), integrating perception, action, memory, working memory, motivator generation, executive functions, affect, learning, reflection, attachment, and development within a coherent computational architecture.

• Insistence. The degree to which a motivator persistently competes for limited cognitive resources, including attention, working memory, management processes, and meta-management processes.

• Insistence-based motivator filters. Architectural mechanisms that regulate which currently active motivators gain access to limited executive resources on the basis of their insistence and other contextual factors.

• Intensity. The degree to which a motivator tends to recruit or energize behavioural systems, thereby increasing the disposition toward overt action.

• Integrative design-oriented approach. A scientific approach to understanding autonomous agency by integrating evidence and theory across disciplines while analysing computational requirements, architectures, mechanisms, development, and design, using the design stance. Regarding the design stance, part of the IDO approach, see Sloman (1993) - Prospects for AI as the general science of intelligence but replace “design-based” with “design stance”. See also Dennett 1987 The Intentional Stance.

• Management processes (deliberative processes). Executive processes responsible for planning, reasoning, scheduling, conflict resolution, resource allocation, and other forms of executive control.

• Mental perturbance. An emergent architectural phenomenon in which one or more highly insistent motivators repeatedly recruit executive processes, making disengagement difficult; i.e, interrupting and influencing attention, working memory, deliberation, memory, and action.

• Meta-effectiveness. Skills, knowledge and dispositions (effectance) to use knowledge to become a more effective person.

• Meta-management processes (reflective processes). Executive processes that monitor, evaluate, and regulate management processes themselves. They support self-monitoring, error detection, strategic revision, mental flexibility, and other forms of reflective control.

• Motivator. A value-bearing control state that influences assessment, attention, planning, executive control, learning, and action. Motivators include motives, goals and projects, standards, and attitudes.

• Motivator generator. A computational mechanism that asynchronously generates, modifies, reactivates, and regulates motivators, and assigns insistence and intensity to them.

• Reflective processes. See meta-management processes.

• Repetitive thought. Persistent or recurrent cognition, such as worry, rumination, obsessive thinking, or craving-related thought. In this framework, repetitive thought is explained architecturally as recurrent recruitment of executive resources by highly insistent motivators. This is a subset of mental perturbance.

• Space of possible minds. The space of possible computational architectures and autonomous agents, encompassing biological, artificial, individual, collective, and hybrid forms of mind.

• Working memory. The limited-capacity executive workspace in which information is temporarily maintained, manipulated, integrated, and evaluated during ongoing cognition. In this framework, working memory is closely tied to executive functions rather than treated as an isolated memory store.

Colophon

The images in this document were generated by ChatGPT. Some of the text was generated through interaction with ChatGPT.

Hookmark now wants to read your mind. Not literally, of course. But it does want to infer what information you are likely to want next so that it can present you a link to it.

Some background

Every day, as we engage in knowledge work, we move among many different kinds of information resources: emails, documents, PDFs, web pages, notes, task lists, calendar events, issue tracker entries, and more. While working on any one of these resources, we often realize that another related resource would be helpful. The challenge is not that we cannot find it eventually. Modern search engines are remarkably good. Rather, the challenge is remembering what to look for, where it is, and interrupting our train of thought long enough to retrieve it. Search itself becomes part of the cognitive burden.

In my book Cognitive Productivity: Using Knowledge to Become Profoundly Effective, I called this challenge the meta-access problem: the problem of accessing information that is relevant to one’s current task. It is a higher-order access problem. The question is not simply “Where is this document?” but “Given what I am trying to accomplish right now, what other information is likely to help me?”

This is the problem that Hookmark was created to solve.

From the beginning, Hookmark has been based on the idea of contextual information retrieval (CIR). Rather than expecting users to remember where everything is or to formulate search queries continually, Hookmark helps users navigate directly among related resources. Unlike bucket applications such as Notion, DEVONthink or Obsidian, Hookmark does not ask users to move their information into a single repository. Instead, it acts as connective tissue among the many applications people already use. A manuscript can be linked to the emails discussing it, to the papers it cites, to figures, outlines, notes (in the user’s preferred note-taking app), calendar events, task lists, and many other kinds of information. Hookmark’s links are robust: they continue to work even when files are moved or renamed, and they can be shared with collaborators who have access to the underlying resources (e.g., file or email).

Present and future

Until recently, however, these relationships depended largely on users creating them, either explicitly or implicitly during their work. Although this remains a powerful approach, we have increasingly asked ourselves a more interesting question: can software infer useful relationships even when users have never linked the resources together?

That question has become one of the principal directions of Hookmark’s development.

Our goal is increasingly to anticipate what resource you are likely to want next. If you are reading a PDF, perhaps you might want the publisher’s web page. You might well want to access the key papers that cite the current PDF, or those that it cites. If you are editing a manuscript, perhaps you will want the email from your co-author, the issue tracker entry describing a revision, or the task reminding you to update Figure 3. These are not arbitrary recommendations. They arise from the context of your current work.

Some of these recommendations require no AI whatsoever. They can be derived from explicit links that you have created, from document metadata, from persistent identifiers such as DOIs, from citation networks, URLs, application context, and other structured information. Some of them are based on log data to which Hookmark has access. Hookmark already exploits many of these sources. For example, when you are reading an academic paper, Hookmark can identify its DOI and immediately offer links to the paper on the publisher’s web site. Similarly, Hookmark 7.3 can read the PDF’s meta-data such as its Where from: field. Hookmark 7.3 will also recommend papers that cite the current paper and papers that are cited by it. No manual linking is required.

These recommendations appear in the RELATED section of Hookmark’s Context window, which can be opened with a single keystroke (⌃H) from virtually any link-friendly application. (You can show and hide this section using the ⌥⌘R keyboard shortcut.) Populating this section has become one of our major areas of ongoing development, and each release expands the kinds of relationships that Hookmark can recognize automatically.

In this sense, Hookmark is trying to “read your mind.” More precisely, it is trying to infer your current information goals. Rather than asking simply, “What documents resemble this one?”, Hookmark increasingly asks, “Given what the user is doing right now, what information is most likely to help them accomplish their current goal?” I believe this is a fundamentally different problem from traditional search and from most web-based recommendation systems.

Traditional web recommendation systems are largely designed to maximize engagement. Their objective is often to keep users consuming content for as long as possible so that they can serve you more ads. Hookmark’s recommendations have a very different purpose. They are intended to reduce cognitive effort, preserve flow, and help users complete meaningful work. Their success should be measured not by how long users remain inside Hookmark, but by how quickly they reach the information that advances their task. And unlike web recommendation systems, Hookmark can serve not only web links but deep app links as well.

Looking ahead, we also plan to incorporate optional LLM-based capabilities into Hookmark’s recommendation engine using local AI. (Analogous to Cotypist’s app’s usage of local AI.) We expect these techniques to complement—not replace—the deterministic methods on which Hookmark already relies. Explicit links, metadata, persistent identifiers, citation networks and application context will remain important sources of evidence. AI simply gives us additional ways of inferring what information is likely to be relevant in a given context.

We also intend soon to open up this information to the AppleScript API, macOS Shortcuts, and the app’s intents API.

Significance

I believe this represents an important shift in personal knowledge software. For decades we have focused on storing information and searching for it. Increasingly, our software should help us anticipate what information we will need before we think to ask for it. That is, in my view, one of the next frontiers of contextual information retrieval.

If you would like to learn more, see our continually updated documentation on the RELATED section of Hookmark’s Context window.

The recommendation features are available today in Hookmark Lite and in the Hookmark Trial. I invite you to download Hookmark and let us know what recommendations you would like to see it make in future releases.

I look forward to reading your comments below ↓.

I designed Hookmark primarily for academics. It happens that solving the meta-access problem for academics also solves it for everyone else.

CogSci Apps Corp. is currently making a push to ensure that as many academics as possible use Hookmark. So I wrote the following web page: Hookmark for Academics. The following diagram sums it up:

If you are an academic or anyone who works with many knowledge resources on a Mac, I invite you to consider using Hookmark.

Search has largely solved the problem of finding information. AI has exposed a different problem: how to retrieve the information that matters because of what you are currently working on.

Every research project now accumulates an expanding constellation of PDFs, AI conversations, notes, email threads, meeting notes, transcripts, datasets, web pages, code, diagrams, drafts, tasks, prompts, and AI-generated reports. The challenge is no longer merely finding one of these resources. It is retrieving the information that is relevant because of the current context, which is typically defined by the foreground resource: the paper, draft, email, task, AI chat, or other item that currently has your attention.

I call this contextual information retrieval. More than a decade ago, in my book Cognitive Productivity: Using Knowledge to Become Profoundly Effective, I introduced the closely related concept of the meta-access problem: the problem of efficiently accessing information because of its relationship to the information you are currently viewing. Generative AI has now made that problem much more important.

From information access to contextual information retrieval

Suppose you were reading an important research paper yesterday and now need to get back to it. Traditional information retrieval asks, “How do I find this paper?” That is what Google Scholar, Spotlight, email search, file search, and reference-manager search are good at.

Contextual information retrieval begins after you have found the paper. It asks, “Now that I am looking at this paper again, what else should be immediately available because it belongs with this paper?” That might include notes (in the app of your choice), ChatGPT conversations, Claude Projects, Gemini Deep Research reports, NotebookLM notebooks, Perplexity searches, DEVONthink records, Bookends or Zotero references, OmniOutliner outlines, meeting notes, transcripts, datasets, code, email discussions, grant proposals, entries in your issue tracking system, tasks, calendar events, manuscripts, diagrams, glossaries, and related papers.

These resources are not necessarily connected by keywords alone. They are connected because they participated in the same line of thought, research process, design problem, collaboration, or project. That is why contextual information retrieval is different from ordinary search.

Three complementary kinds of retrieval

Modern knowledge work depends on at least three complementary kinds of retrieval.

Information retrieval answers the question, “Where is this document?” Typical tools include Google, Spotlight, email search, Finder search, and reference-manager search.

Semantic retrieval answers the question, “What documents discuss this topic?” Typical tools include AI search, vector search, semantic search, and embedding-based search.

Contextual information retrieval answers the question, “Given what I am working on right now, what other resources belong with it?” One important mechanism for this is deep relationship retrieval: retrieving resources because of their deep relationships to the current foreground resource.

These three forms of retrieval complement one another. None replaces the others. Search finds documents. Semantic retrieval finds conceptually related materials. Deep relationship retrieval finds the web of resources surrounding the thing you are currently working on.

Where RAG fits

One of the most important ideas in modern AI is retrieval-augmented generation, or RAG. A RAG system retrieves relevant information before generating an answer. That is enormously valuable, but it solves a different problem.

RAG asks, “What information should an AI retrieve before answering this question?” Contextual information retrieval asks, “What information should I be able to retrieve because of what I am currently working on?” Put differently, RAG retrieves information for an AI, whereas contextual information retrieval retrieves information for a human.

These are complementary capabilities. As AI becomes more powerful, both become more important. AI systems need better retrieval to generate better answers. Humans need better contextual retrieval to manage the growing information ecosystems that AI helps create.

Every foreground resource has an information ecosystem

Every foreground resource — a paper, proposal, email, software issue, AI conversation, legal document, presentation, or draft — has an information ecosystem. That ecosystem includes everything that explains it, challenges it, extends it, implements it, summarizes it, cites it, depends on it, or resulted from it.

For example, a manuscript may be connected to AI chats, meeting notes, transcripts, reviewer emails, Bookends references, Zotero collections, DEVONthink records, OmniOutliner outlines, diagrams, glossaries, datasets, code, tasks, calendar meetings, source PDFs, web pages, and related drafts. The challenge is navigating this information ecosystem without repeatedly resorting to search.

That is the meta-access problem in its contemporary form.

Deep relationships, not merely similar text

It is tempting to think that contextual information retrieval is just about finding resources that are textually or semantically similar to the current foreground resource. But that misses the deeper point. What really matters is that these resources are connected by a common episode of knowledge work. A tool may later make those relationships explicit and navigable, but the relationships themselves often already exist in the work.

Two documents that happen to contain the same words have a shallow lexical relationship. Two resources that participated in the same cognitive, scholarly, design, or work process have a deep relationship. A PDF, a ChatGPT conversation, a grant proposal, a dataset, an issue tracker entry, a meeting transcript, and a draft manuscript may share few keywords. Yet they may belong together because they all contributed to the same line of reasoning.

Contextual information retrieval is therefore not primarily about text similarity. It is about preserving and navigating the deep relationships that constitute the structure of your thinking.

Hookmark and deep relationship retrieval

Hookmark was designed for this problem. Rather than asking you to move everything into yet another repository, Hookmark lets you create durable links among the resources that already exist in the applications you already use.

Those relationships can span PDFs, Finder files, folders, notes, AI chats, emails, tasks, calendar events, DEVONthink records, Bookends references, Zotero items, OmniOutliner outlines, web pages, source code, and many other resources. When you invoke Hookmark for the current foreground resource, it shows the resources connected to it. In doing so, Hookmark makes deep relationships explicit and navigable.

This is especially valuable in AI-assisted work. If a ChatGPT conversation helped you understand a paper, hook it to the paper. If a Claude Project helped shape a proposal, hook it to the proposal. If NotebookLM generated useful questions about a source, hook them to the source material. If Perplexity uncovered an important paper, connect it to the manuscript that cites it. The objective is not merely to preserve links. It is to preserve the structure of your thinking.

You are already familiar with links on web pages. Hookmark extends that idea beyond the browser. It can connect resources across many link-friendly applications, meaning applications that expose stable links to their resources. Hookmark works not only with standard links (like https:// links) but with omni-links, deep links that make resources addressable across applications.

Beyond search

Search transformed knowledge work by making individual resources easy to find. Semantic retrieval made it possible to search by meaning. RAG is transforming AI by giving language models access to external knowledge. The next frontier is helping people navigate the growing web of relationships among the resources they create.

Every interaction with ChatGPT, Claude, Gemini, NotebookLM, Copilot, or Perplexity can produce another potentially valuable knowledge artifact. The bottleneck is no longer generating information. It is maintaining contextual access to the information ecosystem surrounding whatever you are currently working on.

That is why contextual information retrieval matters. It is also why the meta-access problem has become more important than when I first described it in Cognitive Productivity. Hookmark was built to solve it.

Executive summary

This article introduced the following concepts.

• Contextual information retrieval — retrieving information based on your current working context, typically the current foreground resource.

• Meta-access problem — the problem of efficiently accessing information because of its relationship to the information currently in the foreground.

• Deep relationships — relationships among information resources that arise from a common cognitive, scholarly, design, or work process, rather than merely from lexical overlap, semantic similarity, or temporal proximity.

• Deep relationship retrieval — a principal mechanism for contextual information retrieval, in which resources are retrieved because they have deep relationships to the current foreground resource.

• Foreground resource — the document, note, AI conversation, email, task, web page, manuscript, or other resource that currently has your attention.

• Information ecosystem — the network of resources surrounding a foreground resource, including AI chats, notes, emails, tasks, datasets, drafts, diagrams, glossaries, code, and related documents.

• Information retrieval — finding a known resource, typically through keyword or file search.

• Semantic retrieval — finding resources because they are conceptually related to a query.

• Retrieval-augmented generation (RAG) — an AI technique in which retrieved information is supplied to a language model before it generates a response.

• Knowledge artifacts — the intellectual products of knowledge work, including AI conversations, annotations, drafts, reports, diagrams, code, notes, datasets, and summaries.

• Link-friendly applications — applications that expose stable deep links so their resources can participate in a larger information ecosystem.

• Omni-links — app-specific or standard deep links that uniquely identify resources across applications, enabling tools such as Hookmark to connect them into a navigable knowledge graph.

• Hookmark — a contextual information retrieval system that uses deep relationship retrieval to make the information ecosystem surrounding a foreground resource immediately accessible.

It’s been covered in over 100 major media articles and episodes since 2014 (you can see a partial list here), and in the past few years it has spread rapidly across social media. If you’re curious, just search “the cognitive shuffle.”

Today, NPR aired a short interview with me about the technique—a nice milestone after more than a decade of ongoing interest. You can listen to it here: Struggling to sleep? Try this cognitive shuffling technique.

I’m often asked why the cognitive shuffle has become so popular. I’ll unpack this in more detail in a future post but in a nutshell, I think several factors are at play:

• Many people experience difficulty falling asleep or getting back to sleep at some point during the year—even if they don’t have clinical insomnia.

• Most cognitive techniques for sleep that have been studied empirically have only modest effects. I discuss this in more detail in a blog post about our chapter in the Cambridge Handbook of Sleep Theories and Models.

• The cognitive shuffle has a simple, intuitive rationale: (a) it helps interrupt the kinds of insistent, repetitive thought patterns that often interfere with sleep onset—that is, it is counter-insomnolent; and (b) it mimics the imagery-rich mind-wandering that naturally occurs at sleep onset, which serves as a signal to the brain to progress toward deeper sleep—that is, it is pro-somnolent.

• It’s imaginative and surprisingly enjoyable to do (technically, it involves serial diverse imagining).

• It also functions as a kind of informal, cognitively engaging meditation—though in a very different way from traditional practices.

We also developed an app, mySleepButton, to make it easier to practice the cognitive shuffle.

If you’ve tried it, I’d be very interested to hear how it worked for you—feel free to share in the comments.

We will draw heavily on the work of Aaron Sloman of the University of Birmingham, England who has written, from an AI perspective, more than anyone else on the subject. (See my encomium here.)

A prior post

Check out my 2019 post on the subject: Drawing Diagrams in the Head and with Technology: Benefits, Cognitive Mechanisms, Artificial Intelligence, Apps, and Sleep Onset Dreaming – CogZest

Terms and distinctions

We will start by distinguishing

• sentences,

• diagrams,

• Fregean representations (roughly: representations that use application of functions to arguments to combine information items to form larger information items — recursively and with logical connectives), and

• analogical representations (roughly: representations in which part of the structure of the representation maps onto the structure of that which is represented.)

• world 2 (subjective) vs. world 2’ (virtual machine) vs. world 3 (objective) representations, using Karl Popper’s (essential) 3-world ontology (which I augmented Cognitive Productivity: Using Knowledge to Become Profoundly Effective)

• the logicist claim: (that systems built solely on logical representations and general logical forms of inference might exhibit human-like intelligence. Counterargument here.)

Hint: the most helpful concepts in this space are Fregean and analogical reasoning. These concepts are not mutually exclusive. E.g., Fregean representations can be organized in analogical ways, e.g., a sequence of propositions matching the order in which the actions denoted by the propositions took place. A diagram can contain Fregean representations, per below. See this paper for technical definitions of these two concepts. Here we focus on analogical representations and reasoning.

Analogical representations

We’ll jump straight into some examples of analogical representations and reasoning.

The figure above shows that very abstract concepts, even the infinite, can be represented by diagrams.

The above and below show that we can reason causally with diagrams.

The next figure (taken from a prior article of mine on BSBM+ meditation which I invented) clearly illustrates many important facts about analogical representations. Parts of an analogical representation map to parts of the represented object. This also shows how a diagram can indicate sequence. It also illustrates the annotation of a diagram. (Hohol & Milkowski (2019) demonstrate the great historical importance of annotating diagrams.) It also shows that a diagram can contain sentences which are also analogical (here, the order of the sentences reflects the sequence of the procedure). What else does it show?

Can you count how many ways in which Western musical notation is analogical? The next figure might help:

Geometry and topology

The most rigorous analogical representations and forms of reasoning are in geometry and topology. If there’s time I will illustrate geometrical proofs. See also my recent article What We Cut from Education—and Why It Matters for Cognitive Science and AI (i.e., in many jurisdictions geometry has been sacrificed, and that is a shame).

Sign language and videos

Many signs are analogical. Gesture taps into an ancient part of the brain. Drawing is an extension of gesture. (Search for “gesture” in this PDF for more information about the evolution of language from a design stance.)

Conducting is a great example of analogical representations and their use (not so much about analogical reasoning, however). See this series of videos on the subject.

Videos themselves are also analogical, which is a major reason they are so helpful for learning.

Mr. Bean

Let’s jump into an example from Sloman, 2002: “Diagrams in the Mind?” Mr. Bean was on the beach, and wished to remove his underpants then to put on his swimming trunks, both without removing his trousers. How can he do this?

Here he is thinking about the problem:

Now he has gotten pretty far:

Almost done!:

Voilà!:

Watch him in action on YouTube.

Can you count how many different ways Mr. Bean could have accomplished this feat? You may be surprised by the answer. Answering this question requires extensive topological, i.e., analogical reasoning. (Fregean reasoning won’t do.) The solution can be reduced using the following diagrams which result from and support topological reasoning on the problem faced by Mr. Bean:

Questions

We will address some of the questions below. But please also bring your own questions … and diagrams (or videos).

• How and why do you tend to draw diagrams for understanding something or solving problems?

• What visuals do you use?

• How are analogical representations used in Unitarian services (not just diagrams)?

• Can you visualize the properties of infinity, such as an infinite sequence of dominoes falling over each other? How?

Why are the following claims about analogical vs. Fregean representations false?

1. The mind contains diagrams.

2. Analogical representations are continuous, Fregean representations discrete

3. Analogical representations are 2-dimensional, Fregean representations 1-dimensional.

4. Analogical representations are isomorphic with what they represent.

5. Fregean representations are symbolic, analogical representations non-symbolic.

6. Sentences in a natural language are all Fregean.

7. Analogical representations are complete: whatever is not represented in a picture or map is thereby represented as not existing. By contrast Fregean representations may abstract from as many or as few features of a situation as desired: if I say ‘Tom stood between Dick and Harry’, then nothing is implied about whether anyone else was there or not.

8. Fregean representations have a grammar, analogical representations do not.

9. Although digital computers can use Fregean representations, only analog computers can handle analogical representations.

10. Every symbolism, or representational system, must be analysed as being either analogical or Fregean.

The answers to the above are in Sloman (1975): Afterthoughts on analogical representations.

And:

11. Visualizing is like seeing

is countered in Sloman (2002) Diagrams in the Mind?

AI

It has been 55 years since Aaron Sloman first called for AI to pay more attention to analogical representations. There has been some progress but AI, and other cognitive sciences’, models of diagrammatic and topological reasoning are still in their infancy. Understanding these capabilities is a hard problem in cognitive science and AI.

Recommended readings

• Beaudoin (2019) Drawing Diagrams in the Head and with Technology – CogZest.

• Fernandes, M. A., Wammes, J. D., & Meade, M. E. (2018). The Surprisingly Powerful Influence of Drawing on Memory. Current Directions in Psychological Science, 27(5), 302-308.

• Hohol, M., & Milkowski, M. (2019). Cognitive Artifacts for Geometric Reasoning. Foundations of Science, 24(4), 657-680.

• Karmiloff-Smith, A. (1995). Beyond modularity: A developmental perspective on cognitive science. Cambridge, MA: MIT Press.

• Larkin, J. H. & Simon H. A. (1987) Why a Diagram is (Sometimes) Worth Ten Thousand Words.

• Chapter 7 of Sloman A. (1978): The computer revolution in philosophy.

• Sloman, A. (1975). Afterthoughts on analogical representations.

• Sloman (1995) Musings on the roles of logical and non-logical representations in intelligence

• Sloman, A. (2002). Diagrams in the Mind?

• Whittle, M. Diagrammatology: a reader.

Figure courtesy of ChatGPT

About the article. Some of the hyperlinks here do not work because they cross reference sections of the book itself. The book contains a template for processing stories, including important questions to ask with respect to any story one reads, sees or hears. This article is long because I meant to cover several genres: films, plays, dance shows, musicals, songs and real stories. And I wanted to provide enough information to show how to use the Discontinuities framework to understand these works. Even so, the individual commentaries are relatively brief.

Anyone who lives long enough will experience grief. Apt stories cannot innoculate us against grief, but I believe if grief is processed in terms of the schema from Discontinuities apt stories can help prevent grief from becoming unmanageable or pathalogical.

I welcome your feedback in comments below on this article and the previous two on grief.

Learning from stories and music about grief

Works of art can help us understand grief, but not merely by depicting people who have suffered losses. Their deeper value is that they can make salient patterns of mind that are otherwise difficult to observe clearly: altered salience, insistent motivators, attentional capture, disruptions of executive control, failures of reorganization, and the partial reconstruction of a life after loss. Stories and music can thus help us think more concretely about the very processes discussed above in more abstract, scientific terms.

The point, then, is not simply to assemble a list of moving works about bereavement. Nor is my aim to offer correct interpretations of these works, or to critique them as art criticism would. My concern is rather to use art to understand grief, and to use grief to understand minds. Art matters here because it often reveals the temporal structure, phenomenology and interpersonal consequences of perturbance more vividly than theory alone can.

At the same time, I do not want the brevity of some of the remarks below to suggest that one can learn much from cursory acquaintance with these works. On the contrary, I am advocating deep affective and reflective engagement with particular works. Only when one becomes intimate with a story, performance or piece of music does it begin to function as a serious aid to integrative design-oriented understanding.

Musicals

The musical is my favorite genre. It makes multiple media available at once—language, music, gesture, staging, timing, and silence—and is therefore particularly well suited to depicting and eliciting emotions. For a design-oriented theorist of mind, this is not incidental. Musicals often show how affect is distributed across multiple systems at once rather than being confined to a merely verbal or introspective level.

Onegin

The Veda Hill & E. Gladstone 2016 live adaptation of Pushkin’s Eugene Onegin novel is, among other things, a story about belated recognition, romantic perturbance, and grief for what might have been. There is no video publicly available online; however, it is now on Spotify. I highly recommend the soundtrack, which conveys much of the emotional force of the work.

The story goes roughly like this. The eponymous character, Eugene Onegin, and Tatyana Larina meet in a country residence and like each other. Tatyana is his best friend’s (Vladimir Lensky’s) fiancée’s sister. She makes overtures to Onegin in a love letter. One of the themes of this book, sapiosexuality, is relevant, because Tatyana is portrayed as bright and thoughtful. Onegin nevertheless rejects her overture, saying that marriage would kill passion. In a duel, Onegin kills his best friend, Vladimir, and leaves. While apart, his love for Tatyana grows; meanwhile she marries an older man. Onegin later writes to Tatyana, saying “I am interested in nothing else [but seeing you],” trying to regain her love. They meet, and he tries to woo her. She still loves him, but refuses him.

What makes this story relevant to grief is not only the death of Lensky, though that matters. It is also the grief of belatedness: the perturbance generated when commitments and motivators crystallize too late to be enacted. Onegin comes to be organized around Tatyana only after the practical conditions for union have changed. The result is not merely disappointment but a form of romantic grief structured by counterfactuals, regret, and insistent attention to an unavailable future. The mind continues to generate relationship-relevant motivators even though the relevant action path has effectively closed.

The work also shows how grief often does not occur in isolation. Onegin’s romantic grief is entangled with guilt over Lensky’s death, with shame, with self-reproach, and with the dawning recognition of his own immaturity. This matters theoretically because it reminds us that grief is often not a single emotional process but part of a larger perturbance involving multiple interacting motivators and evaluative systems. Onegin is therefore useful not only as a story of lost love but as a study in how delayed understanding can intensify and stabilize perturbance. Ironically, Pushkin himself died in a duel — showing the limits of learning from even one’s own stories.

Plays

Here are a few plays that I have found relevant to grief. Theatre can be especially revealing because it externalizes, through dialogue and staging, conflicts that in real life are often partly hidden within one mind or dispersed across families.

Rabbit Hole

Rabbit Hole depicts a family dealing with grief after young Danny is killed by a car while crossing the street—chasing after a dog. His parents, Becca and Howie, deal with the loss in different ways. Becca’s well-meaning sister, Izzy, is meanwhile pregnant. Izzy’s outspoken mother, Nat, tries to be helpful; Nat herself lost her son to suicide. The driver of the car, Jason Willette, a 17-year-old boy who probably could have done nothing to prevent the accident, is himself obviously distraught and tries, in his own way, to respond through creative school work and by relating to the parents.

Thus we have parents dealing with one of the most evolutionarily consequential losses, the loss of a child. We also have another young person, Jason, in an unspeakable position trying to cope with the aftermath. The play is especially valuable because it does not present grief as a uniform condition. Different people, bound to the same loss, manifest perturbance differently.

One parent wants to move house and move on. The other wants to stay put. What should be done with Danny’s things? Should they have another child? These are not merely practical disagreements. They reveal competing strategies of reorganization. One strategy seeks to reduce cue-triggered insistence by altering the environment and loosening ties to the old attachment structure. Another seeks to preserve traces of the lost child and to maintain continuity with the prior organization of life. The conflict between these strategies can itself become a further source of perturbance.

It is noteworthy that the entire play centers around grief. The parents, Nat and Jason are captivated by the loss of Danny. Nat is still grieving her own son. We can learn also from the stories of other parents who’ve lost a child: Shakespeare, Darwin, Antonín Dvořák and Eric Clapton the fictional Fisk Senior (Horatio) in Dean Spanley. Because parental grief is expected to be intense, there is a danger that we stop asking explanatory questions about it. We treat it as obvious. But this is exactly where theory must become sharper.

Lest we be like thinkers before Newton who did not pay enough attention to the obvious fact that things fall, we need to ask ourselves: why should the loss of a child be so upsetting, and for so long? We must not simply appeal, circularly, to the fact that it tends to happen. The questions are: why does this grief arise, what is happening in these grieving, partially hijacked minds, how does this hijacking occur, and why does it endure? Rabbit Hole helps with this “problem shift” because it makes visible not only sadness but prolonged executive capture, recurrent relational negotiation, and the difficulty of reorganizing a mind and household after catastrophic loss.

The Winter’s Tale by Shakespeare

The Winter’s Tale I take to be primarily a story about irrational jealousy, experienced by the main character, Leontes, the King of Sicily — itself an interesting emotion (perturbance). (An even stronger Shakespearian play about jealous is Othello, which is also about grief.) But secondarily and essentially The Winter’s Tale is a story of grief. That conjunction is important. Emotional episodes are often intelligible only in relation to one another. Jealousy, accusation, loss, guilt, hope and mourning form here a connected architecture rather than a sequence of isolated feelings.

The play illustrates the counter-productiveness and irrationality of some emotions, but it also illustrates that grief often comes entangled with guilt. Leontes’ delusional jealousy helps bring about what he then must grieve. In design-oriented terms, one might say that a maladaptive evaluative and motivational configuration generates losses that later reorganize the entire person. Grief here is not simply a reaction to fate; it is partly the downstream consequence of an agent’s own perturbance.

Like many famous stories about grief, including that of Jesus and Cinderella, The Winter’s Tale is also a tale of hope. This is a clue. Grief is not only about absence; it is also about the persistence of motivators whose object is no longer straightforwardly attainable. Hope, fantasy, denial, and the continuing felt presence of the lost are therefore not peripheral to grief but often part of the mind’s attempt to manage unsatisfied commitments.

There is also an interesting mixture in this play of romantic grief. Shakespeare repeatedly returns to both objects of grief. This invites comparison. How should grief for a child be compared and contrasted with grief for the person one was romantically attached to? The play does not answer that question theoretically, but it helps keep it vivid.

Pourquoi Tu Pleures by Christian Bégin

Pourquoi tu pleures…? is a thought-provoking French Canadian play about the execution of the will of a wealthy, authoritarian, and in other ways questionable French Canadian husband and father who left the following ambiguous instructions: “Let my assets be divided amongst my children and spouse in accordance with their needs.” Was this a final way of putting it to his family? Or did he think that this process would help unite and heal them?

Here is the French description of this play by Le Théatre la Seizième :

> The death of an authoritarian father, an estate of more than five million dollars to be divided “according to each person’s needs,” and suddenly the value system of a mother and her four children is put to a severe test. What is a need? An ambition, a compensation, a dream… In a back-and-forth between present and past, family secrets resurface.
> A biting and uproarious comedy, Pourquoi tu pleures…? marks the return of Christian Bégin and the Éternels Pigistes to Vancouver. Backed by a cast at the height of their craft, this production—first created at the Théâtre du Nouveau Monde in 2016—confronts us with the individualism of our societies.
(translation from French by ChatGPT).

This play is useful because it shows that grief can be prolonged not only by attachment in the narrow sense but by unresolved social coordination problems. Even in complex grief — of a person towards whom one had a very ambivalent relationship — the dead do not simply disappear from the control architecture of the living. Wills, inheritances, secrets, resentments and ambiguous final acts can continue to generate new motivators, conflicts and interpretations. In such cases, grief endures partly because the lost person remains causally active in family cognition and interaction through legacy structures.

This matters for theory. If grief is a process of mental reorganization, then that process can be delayed or destabilized when practical, moral and interpersonal questions remain unresolved. Legacy questions must therefore be considered when we attempt to answer “What causes grief to endure?” Some grief persists not because the mourner fails to accept reality, but because the loss continues to ramify through commitments, identities and negotiations among the living.

Novels

Novels can be especially useful because they provide prolonged access to interiority, recollection, counterfactual reflection and the slow transformation of a life narrative. Grief is often extended and recursive; the novel is therefore an especially apt form for exploring it.

L’ignorance by Milan Kundera

On the surface, Milan Kundera’s L’ignorance only tangentially concerns grief: both main characters, Josef and Irena, have lost their respective spouses before the story begins. Yet this apparent marginality is misleading. Grief is not thematized directly so much as embedded in the conditions of the characters’ lives, shaping their perceptions, relationships and sense of self.

The most explicit theme of the novel is immigration. Josef and Irena have both emigrated from Czechoslovakia to the West, and Kundera explores in depth what it means to live as a stranger in a new land. This condition requires continuous adaptation. In this respect, immigration provides a powerful analogue for grief: both involve dislocation, reorientation, and the reconstruction of personal narrative in altered circumstances. In both cases, familiar expectations no longer fit the world one now inhabits.

The novel, a masterpiece of what Kundera calls the fugue romanesque, is part of his so-called “French trilogy.” Like other works from this period, it interweaves narrative with essayistic reflection—philosophy, history and especially psychology—commenting directly on the unfolding story. This hybrid form, blending fiction with conceptual analysis, aligns closely with the aims of the Learning from Stories project: it invites the reader not just to follow events, but to think with them.

As L’ignorance develops, Kundera reflects extensively on themes central to grief even if not labeled as such: memory and its distortions, nostalgia and its illusions, the fragility of personal identity, and the experience of solitude and loneliness[^^loneliness]. These are not merely background motifs but structural elements of grieving, understood as an ongoing process of negotiating one’s relation to the past, to others, and to oneself. The novel is thus valuable because it helps us see grief not only as painful attachment to the lost person but as a broader reorganization problem affecting identity, belonging and temporal orientation.

Les Liaisons Dangereuses by de Laclos

Les liaisons dangereuses (adapted to film as Dangerous Liaisons) is a late 18th century French epistolary novel by Pierre Choderlos de Laclos that, in my opinion, rivals the best of the English Bard’s work. (I suspect John Malkovich, who starred in that film and the novel’s English play adaptations, would concur.)

I cannot sufficiently explain the relevance of this story without playing some theoretical cards:

• from Michel Aubé we learn that emotions are social motivational systems that regulate interpersonal relations through the proxies of commitment and trust;

• from Sloman we learn that perturbance is a distinctive emergent property of human, and future sophisticated robotic, computational processes;

• from Geoffrey Miller we learn the importance of sapiosexuality in the distinctive evolution of human mating and cognition.

My own theory of perturbance, expanded upon throughout the current book, blends, extends and illustrates these key ideas. Some of the most fascinating human emotions follow from epistolary romance. The advent of email has exponentially multiplied examples thereof, as fictionally depicted in the first chapter of this book.

When the Canadian member of the Académie française, Dany Laferrière, whose impromptue speech matches the eloquence of Shakespeare’s most eloquent characters, was asked what works educated him sentimentally or with respect to love, Laferrière answered:

> L'amant de lady Chatterley m’a fait découvrir l’érotisme physique, et _Les Liaisons dangereuses_, l’érotisme intellectuel.

> “*Lady Chatterley’s Lover* introduced me to physical eroticism, and *Les Liaisons dangereuses*, to intellectual eroticism.”

This of course makes Les Liaisons dangereuses supremely relevant to Discontinuities.

For present purposes, however, the important point is that epistolary form, underlain by sapiosexual attraction, makes visible the recursive structure of socially mediated perturbance. Letters do not merely report emotions; they help generate, amplify and redirect them. The novel is therefore illuminating for grief in at least three ways: grief in betrayal, grief for lost innocence, and grief for damaged commitment structures. It shows how social intelligence, seduction, and strategic communication can produce not only desire but forms of loss that reorganize the self.

Les liaisons dangereuses is thus useful not because it is centrally a novel of bereavement, but because it reveals how some kinds of grief are rooted in the collapse of trust, the corruption of intimacy, and the painful revaluation of oneself and others. Grief here is inseparable from conscience, shame, betrayal and the social architecture of attachment.

Grief in dance shows

See Betroffenheit below. Dance is especially important for grief because some forms of perturbance are bodily and temporal before they become verbally articulated. Movement, repetition, interruption and rhythm can express disorganization and attempted reorganization in ways language alone often cannot.

Grief in classical music

Music is especially valuable for understanding grief because it can model temporal and affective dynamics without having to specify a narrative. A piece of music can enact suspension, recurrence, fragmentation, insistence, attenuation, release or the refusal of release. In this way it can illuminate grief not merely as content but as process.

The Messenger by Valentyn Sylvestrov

Valentyn Sylvestrov composed The Messenger after his musicologist wife died suddenly. Compare: Requiem for Larissa | gramophone.co.uk. It sounds like a musical attempt to portray grief.

Hélène Grimaud – Silvestrov: The Messenger (Piano Solo)

Silvestrov’s work is well suited to illuminating grief because it does not dramatize loss so much as enact some of its inner structure. The music is quiet, sparse and fragmentary, with gestures that seem to emerge only to fade or remain incomplete. This mirrors a familiar phenomenology of grief: something of high significance remains active while ordinary energetic and executive engagement is attenuated. Thoughts and feelings arise in partial, recursive forms rather than progressing toward resolution. The music feels less like an unfolding narrative than like a series of returning traces—echoes of something no longer present.

At the same time, the work evokes a striking sense of presence-in-absence: it sounds like remembrance itself, as if the music were recalling something lost rather than presenting something new. Its suspended temporality and avoidance of catharsis further align with grief’s lived dynamics, where time can feel stretched and resolution elusive. In this way, The Messenger does not offer release so much as a gentle stabilization of perturbance, allowing the listener to inhabit grief without being overwhelmed and to experience how loss can persist as a quiet, enduring form of cognition and feeling.

Films

A Single Man

Tom Ford’s A Single Man offers a striking portrayal of grief as a condition of altered salience and precarious control. Following the sudden loss of his partner, George (Colin Firth) inhabits a world in which ordinary affordances have drained of meaning, while selected stimuli—memories, bodily cues, fleeting human connections—become intensely charged. The film renders this through shifts in visual saturation, framing and temporal pacing: color blooms briefly when something pierces George’s emotional flatness, then recedes. This stylistic device captures a core feature of grief: motivators tied to the lost relationship remain highly insistent, but are no longer integrated into a viable action system. The result is a state poised between numbness and intrusion, where executive processes are intermittently captured by reminders that cannot be resolved.

At the same time, the film traces a fragile reconfiguration rather than a simple trajectory toward recovery. George’s day unfolds as a series of encounters—some accidental, some sought—that momentarily restore connection, suggesting that grief does not eliminate the capacity for meaning but destabilizes its organization. These moments do not culminate in catharsis; instead, they reveal how new or residual motivators can briefly counterbalance the insistent pull of loss. In this sense, A Single Man presents grief as a form of sustained mental perturbance that can be modulated but not simply extinguished—an ongoing negotiation between absence and the possibility of renewed engagement with the world.

The Demons (Les démons) by Philippe Lesage

Philippe Lesage’s The Demons (Les démons) is not, strictly speaking, a film about grief. It is, rather, a film about the emotional conditions out of which grief later becomes intelligible. Set in suburban Montreal, it follows Félix, a sensitive ten-year-old boy, as he moves through a world saturated with worry, confusion, sexual curiosity and menace; the film’s background of child abductions and Félix’s own excessive fear give it an atmosphere in which childhood vulnerability is constantly palpable. Lesage has described the film as being not only about children’s fears but also about a child discovering the sexual world of adults and, in that discovery, experiencing fear because he does not yet understand what he is encountering.^^Wikipedia1

That is precisely why the film belongs in a chapter on grief. We cannot adequately understand grief in adulthood if we detach it from the broader development of affect in childhood. Grief does not arise in an emotional vacuum. It emerges in minds already shaped by fear, attachment, shame, desire, secrecy, helplessness and the dawning recognition that the world contains threats one cannot fully comprehend or control. The Demons is valuable because it lingers within that formative emotional terrain. Critics repeatedly describe the film as an examination of childhood fears, of turbulence beneath the surface of ordinary suburban life, and of a child learning that the world is more dangerous and morally complex than it first appeared. ^^RottenTomatoes1

The relevance to grief, then, is indirect but important. The film illuminates the developmental background against which later grief must be understood: the child’s growing awareness of vulnerability, loss of innocence and the disturbing opacity of adult motives. Grief is only one powerful human emotion among others, and adult grief cannot be fully understood unless it is related to this wider emotional ecology. The Demons reminds us that if we focus only on adulthood, we risk forgetting how much of our emotional life—including our ways of grieving—depends on structures of feeling and forms of perturbance that begin much earlier.

Death at a Funeral (British version), and Fawlty Towers

To understand grief and its time course we also need to understand humor itself, and then understand how and why grief places bounds on humor. Death does not merely silence laughter; it also creates conditions in which laughter becomes unstable, risky, therapeutic or transgressive.

I doubt that we can find a funnier treatment of the initial stage of grieving than Death at a Funeral, which is why I do not understand why the Americans attempted to redo the film.

Aside. With apologies to my American friends, one only needs to watch international sports competitions to understand that many Americans think their country is, can and/or should be the best at everything. Meanwhile, the day I wrote this paragraph, [Canadian Bianca Andreescu apologized to Americans for beating American Serena Williams at the U.S. Open]( https://www.cbc.ca/sports/tennis/bianca-andreescu-us-open-canadian-apology-1.5275031). We can add grieving loss at sporting events to interesting forms of grief.

There is a version of the Fawlty Towers series that includes pre-episode commentary by John Cleese. There he explains how taboo is ripe for humor. The taboo of death, and how this plays out in humor, are important. He also suggests that serious matters call for humor rather than solemnity.[humor]

What is theoretically interesting here is that humor can temporarily reframe what would otherwise remain perturbing. It can relax certain control settings, permit the exploration of forbidden or threatening material, and create brief distance from insistent motivators. But grief also places limits on such reframing. Where commitment structures remain too raw and insistence too high, humor may fail, offend or intensify distress. These works therefore help us think about the boundary conditions under which perturbance can be modulated rather than merely endured.

Humorists often have an intuitive understanding of humor. But humor was poorly understood by humorists and scientists alike until the publication of Inside Jokes. Though not a work of fiction, it is loaded with stories that illustrate the theory. It does not deal centrally with grief, but it does have a few words to say about humor with respect to death. Their theory of emotion needs some work.

Mystical stories

Mystical spirituality may arise in part from the desire to deal with that which cannot be controlled, cannot be repaired, cannot be got again. Even the great Indian mathematician Srinivasa Ramanujan believed in his culture’s myths.

Many religions, Unitarianism aside, offer stories of life after death. These stories are deeply relevant to grief, not only because they console, but because they can help manage otherwise unsatisfiable motivators. If one cannot restore the lost person in ordinary reality, one may preserve attachment through narratives of continuation, reunion or transcendence. In design-oriented terms, such stories may function as culturally scaffolded ways of regulating perturbance generated by irrevocable loss.

Jesus Christ (by various authors)

One of the best known religious stories is, of course, that of the resurrection of Jesus Christ. Many parents, while not truly believing in the story, teach it to their children, playing the long game, i.e., hoping it will assuage their grown children’s existential anxiety. That is what makes this type of story essential to understand for those wishing to understand grief.

Although there is much scholarship on myths of resurrection, Heaven and the like, it has not, to my knowledge, taken an integrative design-oriented perspective. That is yet another set of theoretical problems on which IDO may make a significant contribution. Such stories may be especially important because they do not merely describe comfort; they help generate socially shared ways of continuing bonds with the dead and of placing grief within a larger structure of meaning.

Real stories

Not all stories are entirely fictitious. One can learn as much about grief from real stories as from fictional ones. Conversely, most of our personal narratives are somewhat fictitious—a theme brilliantly explored by Kundera in L’ignorance, itself a mind-bending mixture of fiction and non-fiction.

Learning from a clinical case study: grief, guilt and “The Wrong One Died”

A potent way of learning from true stories is to learn from a clinical case study. A clinical case study is an account, written by a therapist or other clinician, of work with a particular client or patient, usually selected because it vividly illustrates some psychologically important pattern, difficulty, insight or therapeutic process. Such stories are typically altered in some respects to protect privacy: names, identifying details, and sometimes circumstances are changed. Yet they remain potent sources of learning precisely because they are chosen for didactic purposes. The clinician is not merely recounting what happened, but presenting a case that can help readers notice something important about the mind, suffering, relationships, or change.

For bibliotherapy regarding grief, one especially useful example is Penny’s story in Irvin Yalom’s Love’s Executioner, titled “The Wrong One Died.” Penny is a mother whose daughter, Chrissie, has died after a long and difficult illness. Her grief is intense and immobilizing, but it is not simple mourning. Her life has become organized around the loss: she idealizes her daughter, remains psychologically bound to her, and withdraws from engagement with her two surviving sons, who are themselves troubled and in need of attention. As therapy unfolds, it becomes clear that Penny’s grief is intertwined with exhaustion, resentment, guilt, and a deeply unsettling recognition. The daughter she mourns so intensely had been, in many ways, the easier child—the one through whom Penny could sustain a sense of herself as a loving and competent mother. Her sons, by contrast, present ongoing difficulty and strain. In a moment of painful honesty, Penny voices the thought she had been unable to admit even to herself: that, in some sense, “the wrong one died.”

The power of the case lies in its refusal to sentimentalize bereavement. It shows that grief is not always a pure expression of love. It may also involve ambivalence, moral shock, family role tensions, and the collapse of an idealized self-image. Penny is not only mourning her daughter; she is also struggling with what her reactions to the loss reveal about her attachments, her limits, and the structure of her family life. Her suffering is intensified by the belief that having such thoughts makes her a bad mother—someone who does not deserve to grieve.

This makes the story especially valuable for readers who are suffering not only from loss, but from the fact that their own responses to loss do not fit the culturally preferred script. Some grieving people feel not only sadness, but relief, anger, numbness, guilt about divided attention, or shame about the thoughts that arise under strain. A story like Penny’s can help such readers recognize that disturbing reactions do not necessarily cancel love. Human attachment is often affectively mixed, especially under prolonged burden or when relationships have been asymmetrical, idealized, or fraught. One may love deeply and still feel exhausted. One may mourn sincerely and still harbor forbidden comparisons.

That is one reason why this case belongs in a discussion of learning from real stories. It can help the reader move from self-condemnation toward more accurate self-understanding. Instead of asking only, “Did I grieve properly?”, the reader may begin to ask better questions: “What exactly am I grieving? The person who died? The future I imagined? The role I had in relation to that person? The version of myself I believed myself to be? The family story I can no longer sustain?” Such questions do not reduce grief to analysis, but they can help loosen the grip of undifferentiated suffering.

Penny’s story also illustrates something important about productive reflection on cases. The point is not to identify simplistically with the protagonist, nor to extract a neat moral. It is to use the case as a prompt for disciplined self-inquiry. A reader can ask: “What feelings have I declared unacceptable in myself? What have I not permitted myself to say? What mixture of love, guilt, resentment, relief, protectiveness, anger, or helplessness might be present in my grief? What aspects of my mourning have remained frozen because they threaten my moral self-image?” In this way, the case becomes not only moving, but usable.

For the purposes of bibliotherapy, I chose this story because it can help some readers bear the complexity of grief without collapsing into self-accusation. It offers a corrective to over-tidied accounts of mourning. It reminds us that grief is not always singular in feeling or simple in structure. It may be threaded through with conflicting motives and difficult truths about attachment. A clinically chosen case study can therefore do something that abstract advice often cannot: it can give the reader a psychologically concrete scene in which the mind becomes more intelligible.

Shakespeare and Darwin lost children

Consider how Charles Darwin was affected by the loss of his child. Grief contributed to his spiritual development: atheism and unitarianism. Shakespeare’s loss of his only son, Hamnet, arguably, helped shape later work, including Hamlet and Twelfth Night.

These examples matter because grief does not remain confined to feeling. It can reshape worldview, creativity, vocation and intellectual life. The attachment structure that has been ruptured does not simply disappear; its reorganization can ramify through thought, value, ambition and art. Real stories like these therefore remind us that grief is often architecturally pervasive. It can alter not only what one feels, but what one works on, what one believes, and how one interprets existence itself.

Winston Churchill and his father

Enough is known about how Winston Churchill processed his father’s death, Randolph Churchill, to find there matter for reflection about grief and its time course. Here we have the not uncommon situation of a child desperately wanting and failing to impress his father, and being enduringly affected by that. Winston named his first son Randolph—fittingly, perhaps, as neither Randolph was particularly kind to Winston. We know that Winston, with considerable effort, wrote a hagiographic biography of his father which was not well received. Winston’s writing about a more distant relative, Marlborough, in contrast, is acclaimed. Winston also recounted an encounter with the ghost of his father, The Dream. Whether it was pure fiction, a hallucination or a dream is not entirely clear or relevant.

The relevant point is that it is not uncommon for the grieving mind-brain to continue to produce dialogues with the deceased. This is something a theory of grief and its time course must account for. Such phenomena may reflect not pathology but the persistence of commitment structures and the mind’s ongoing attempt to renegotiate relations to a person who is no longer available for actual interaction. Churchill is useful here because his grief appears not as a brief episode but as an enduring organizational factor in identity, writing and self-evaluation.

My chapter on Jocelyn Morlock

Winter Journal by Paul Auster

Winter Journal is an engagingly personal and stylistically distinctive autobiography. Liszt’s Piano Concerto has no movements. Winter Journal has no chapters, but many unnumbered, unlabeled sections. These are things one notices when one is interested in discontinuities. The transition from life to death is, of course, a somewhat significant type of discontinuity.

Winter Journal is worth reading by men who wish to better understand themselves and other men, and by women who want to understand men. The book does not pretend to be of universal use—no one is everyone. But who writes and publishes a book about themselves just for themselves? Not Auster, evidently. (Once again I care not one iota for the Guardian’s critique.)

By the winter of one’s life one has become well acquainted with death. The précis of this particular life is, among other things, a kaleidoscope of mortality that is germane to our subject. Here I will only single out Auster’s depiction of a near death experience. To understand grief we also need to understand how we can be transformed by near tragedies, and by finding things we thought were lost. That too is a clue. Such experiences can recalibrate salience and vulnerability, thereby changing the background against which later losses are experienced and interpreted.

Song

I don’t know what percentage of pop songs deal with grief — romantic or mortal grief. However, it’s a high number. Some deal with the past, present and future of grief. I will just pull out a few songs.

Tears in Heaven by Eric Clapton

Eric Clapton’s Tears in Heaven provides a stark and intimate illustration of grief following the loss of a child, making visible the persistence of attachment structures even when their object is irretrievably gone.

video: “Tears in Heaven,”

The song centers on questions of recognition and reunion—“Would you know my name…?”—which can be understood as the continued activation of deeply embedded motivators oriented toward connection, despite the impossibility of fulfillment. This creates a poignant form of perturbance: the system continues to generate relationship-directed processes that cannot be resolved through action. At the same time, lines such as “I must be strong and carry on” suggest an effort at reorganization—a tentative attempt to regulate insistence and re-engage with other aspects of life. The song thus captures both the persistence of insistent motivators and the fragile beginnings of their gradual modulation.

The River by Bruce Springsteen

Let’s now consider The River by Bruce Springsteen:

video: The River

The River is not about bereavement in the narrow sense, but about a quieter, more pervasive form of grief: the loss of a hoped-for life. The song traces how early commitments—to love, work, and a shared future—become progressively undermined by circumstance, leaving behind a persistent sense of what might have been. In architectural terms, it illustrates how grief can arise when long-standing commitment structures tied to identity and future plans can no longer be enacted, yet continue to generate low-level, insistent motivators and counterfactual reflections. The result is not acute perturbance but a chronic, attenuated form of it—a background condition in which the past retains salience and the present feels comparatively diminished.

Jaques Brel’s Voir un ami pleurer

Jacques Brel’s Voir un ami pleurer (“To See a Friend Cry”) offers a particularly stark portrayal of grief as a uniquely powerful and disorganizing emotional condition. Here is a particularly high caliber translation:

The song proceeds by systematically dismissing other sources of human concern—politics, war, ambition, even death itself—as comparatively insignificant, only to culminate in the claim that seeing a friend cry is what truly matters. This rhetorical structure is revealing: it models a collapse of ordinary evaluative hierarchies, in which most motivators lose their salience when confronted with the immediate, relational reality of another’s suffering. In architectural terms, grief here is shown not merely as an emotion among others, but as a state in which attachment-based motivators become overwhelmingly insistent, reorganizing attention, valuation, and meaning across the system. Brel’s framing also invites comparison with limerence: both involve extreme prioritization of a particular person, such that other concerns recede dramatically. Yet grief differs in that its object is wounded or absent, generating not anticipation or longing for union, but a confrontation with vulnerability, loss, and the limits of control. The song thus points toward existential dimensions of grief: the recognition that what matters most is fragile, that suffering cannot be prevented, and that meaning itself is grounded less in abstract structures than in deeply embedded, interpersonal commitments.

> Some men are still at war in this land
> For certain songs and certain dates
> The tender gave way to the firebrand
> And Europe gave way to the States
> So now that money's all but scentless
> Noses and consciences are clear
> The pointless flowers can be dispensed with
> To see a friend in tears
> So our defeats are just reminders
> Of death that waits behind it all
> The body wilts before the mind does
> Surprised to see how soon it falls
> It's true our women have deceived us
> All hunted species disappear
> It's true we've shot the golden eagles
> To see a friend in tears
> It's true our cities are exhausted
> Made by and for the middle aged
> Our weakness gave them more than force did
> We thought that love could cure a toothache
> And in the underground we're drowning
> Accelerating through the years
> You think you'll find the truth by frowning
> To see a friend in tears
> It's true our mirrors don't show heroes
> We lack the courage to be Jews
> Without the elegance of Africa
> With our youthful fireworks all defused
> And all these men who are our brothers
> Wonder why we don't want to hear
> How their worst enemies are their lovers
> To see a friend in tears

Other relevant artifacts

There are several other artifacts pertaining to grief described elsewhere in this book:

• Betroffenheit: a dance show by Crystal Pite,

• Monsieur Lazhar: a film by Philippe Falardeau.

• Act in Three Acts: a unitarian service created by myself featuring romantic grief.

These deserve separate treatment, but they support the same general point as the works discussed above. Art helps not merely by depicting grief as sadness, but by revealing the organization and disorganization of a mind under loss: how attention is captured, how commitments persist, how the dead remain psychologically active, and how reorganization can be blocked, partial, socially scaffolded, or unexpectedly transformed.

Our papers on grief and attachment:

• Beaudoin, Hyniewska and Pudlo (2020). Mental perturbance: An integrative design-oriented concept for understanding repetitive thought, emotions and related phenomena involving a loss of control of executive functions

• Wright, Sloman & Beaudoin (1996) Towards a Design-Based Analysis of Emotional Episodes

• Petters & Beaudoin (2017). Attachment Modelling: From Observations to Scenarios to Designs | Springer Nature Link) (pp 227–271) part of the book, Computational Neurology and Psychiatry | Springer Nature Link.

Concluding note on art and grief

These works do not merely depict grief; they instantiate its dynamics in different media. Music can model its temporal recurrence and attenuation, narrative can track its interaction with identity and social structure, and performance can externalize its conflicts across agents and bodies. Taken together, they function as a distributed laboratory for observing perturbance, insistence, and reorganization—making visible aspects of grief that are otherwise difficult to isolate or describe within purely theoretical analysis.

[^^loneliness]: For a real life example of solitude and loneliness in grief, consider: The crack of a falling tree, the terrible loss – then the silence and How did we miss our colleague’s grief? | Ranjana Srivastava | The Guardian

The chapter takes an integrative design-oriented perspective.

Credit: image generated by AI using ChatGPT

What is Grief and What Causes It to Endure?

Grief has often been defined in broad, descriptive terms—as the emotional, cognitive, and behavioral response to the loss of a significant person. Such definitions capture something important about the phenomenology of grief: its sadness, its yearning, its disruption of everyday life. Yet they remain largely pre-theoretical. They tell us what grief feels like and how it appears, but not how it is generated, sustained, or resolved within the architecture of the mind.

More cognitively oriented accounts move a step further. They emphasize the intrusive and repetitive nature of thought during grieving: memories that return unbidden, counterfactual reflections (“if only…”), persistent attention to the lost person and the circumstances of the loss. These approaches identify a key feature of grief—its tendency to capture and redirect attention—but they still lack a principled explanation of why such thoughts are so difficult to regulate.

A more explanatory approach emerges when we adopt a design-oriented perspective on the mind. In this framework, grief is not treated as a single mechanism or module, but as a systemic phenomenon arising from the interaction of multiple information-processing processes within an autonomous agent. As analyzed in our earlier work[^griefPapers], grieving involves a partial loss of effective control over thought processes: memories, desires, and evaluations related to the deceased repeatedly intrude, often displacing other goals and concerns. This is not simply dysfunction. It reflects the operation of mechanisms that are essential for intelligent agency—mechanisms for generating motives, prioritizing them, and allocating limited executive resources—operating under conditions where their normal targets are no longer attainable.

Central to this account is the notion of an attachment structure. As described above, through repeated interaction with another person, the mind develops a highly distributed and deeply embedded set of control states: preferences, expectations, plans, evaluative dispositions, and motive generators that concern that individual. These structures are woven throughout the architecture, influencing both reactive and deliberative processes. When such an attachment is disrupted by death or loss, the resulting disturbance propagates widely through the system.

The concept of mental perturbance provides a general framework for understanding this disturbance. Perturbance refers to a condition in which insistent motivators—structures that dispose the agent toward certain states of affairs—repeatedly influence or disrupt executive processes, even when the agent attempts to suppress them. Grief is a paradigmatic instance. The mourner is subject to the continual reactivation of commitment-grounded motivators: desires to reconnect, counterfactual simulations of how things might have unfolded differently, evaluations of the loss, and attempts to make sense of its implications. These motivators tend to retain high insistence, penetrating attentional filters and consuming limited executive resources.

Grief endures because the mind continues to generate highly insistent, commitment-grounded motivators toward a person who is no longer available, and because reorganizing the distributed structures that support those motivators is a slow, resource-limited process.

From this perspective, grief is not simply an emotional state but an extended process of mental reorganization. It involves the gradual restructuring of a complex attachment system in light of the fact that its central object is no longer available. This reorganization is neither immediate nor straightforward. The attachment structure is deeply entrenched, distributed across multiple layers of control, and integrated with many other aspects of cognition and behavior.

Several factors contribute to the endurance of grief.

1. One is the sheer complexity of the attachment structure itself. Because it is distributed and multi-layered, it cannot be simply “turned off.” The process is more akin to relearning a deeply ingrained skill than updating a belief. One might compare it to adapting to driving on the opposite side of the road after years of habituation when moving to a new country: declarative knowledge of the new rule is insufficient. What must change are numerous interconnected control processes—perceptual habits, expectations, attentional priorities, and action tendencies. Similarly, in grief, the system must reorganize a vast network of dispositions that were built around the presence of the other person.

2. A second factor is the limited control that executive processes have over the mechanisms that generate and prioritize motivators. The assignment of insistence to motives is automatic and only partially accessible to reflective control. Even assignment of importance, intensity and urgency are not fully controlled by executive functions. As a result, even when one resolves to redirect attention or “move on,” commitment-linked motivators continue to arise and capture processing resources. This reflects a fundamental discontinuity between knowledge and control: one may fully know that the person is gone, yet the system continues to operate, in important respects, as if reconnection were still possible.

3. A third factor concerns the role of counterfactual and simulation processes. The mind generates alternative scenarios—what might have been done differently, how events could have unfolded otherwise—driven by commitment structures that encode concern for the other. These simulations function as error-signaling and evaluation mechanisms, but in grief they can become persistently activated, contributing to repetitive thought and sustained insistence.

4. Evolutionary considerations may also play a role. The mechanisms that generate persistent, insistent motives toward a lost individual may have evolved under conditions in which separation was often temporary and recovery possible. From this perspective, the mind continues to act as though reconnection might still be achieved. At the same time, the enduring pain of grief may function as a powerful learning signal, encoding the significance of attachment and the cost of its disruption. This helps explain why grief is often accompanied by guilt: counterfactual evaluation of one’s actions in relation to commitment structures can generate motives oriented toward repair, even when repair is no longer possible.

5. Attachment structures should also be understood as commitment structures: long-term configurations of motives, plans, and expectations that bind the agent to others. Grief reflects the breakdown of such commitments and the difficulty of withdrawing, revising, or redistributing them. This difficulty is compounded by the fact that reorganizing entrenched control structures is inherently slow, especially when they are reinforced across multiple layers of the architecture.

6. Finally, grief has a social dimension. Persistent grieving can function as a signal to others of the depth and endurance of one’s commitments. In some cases, the most effective way to signal such commitment is to experience it genuinely—to be, in a sense, convinced by one’s own grief. This signaling function does not reduce grief to communication, but it highlights an additional layer at which commitment structures may operate and be displayed. (Compare the earlier discussion of social signaling.)

Taken together, these considerations suggest that grief is best understood not as a unitary emotion but as a prolonged, system-level phenomenon. It involves the interaction of insistent motivators, limited executive resources, and deeply embedded attachment structures undergoing reorganization. What appears, from the inside, as an overwhelming and persistent emotional experience is, from an architectural perspective, the unfolding of a complex and necessary process: the gradual transformation of a mind that had been organized around the presence of another.

Let us know

Please let us know in the comments ↓ whether this text helps you make sense of grief you have experienced.

Our papers on grief as mental perturbance

• Mental perturbance: An integrative design-oriented concept for understanding repetitive thought, emotions and related phenomena involving a loss of control of executive functions

• Wright, Sloman & Beaudoin (1996) Towards a Design-Based Analysis of Emotional Episodes

Next up

In part 3, we will examine works of art, in multiple genres, that can help one understand and alleviate grief.

In a series of articles here on my Substack I am gradually publishing sections of this chapter. So here is a draft of the first two sections of the chapter. Comments are welcome!

Introduction to the chapter

Grief occurs when someone we love has died or is lost. It can also be something we love that is lost. In order to understand grief, we first need to understand the nature of love. Next we do this in integrative design-oriented terms. Following that, we dig more directly into the nature of grief. We then reflect on grief with the aid of several stories, songs and a classical music piece.

Love: attachment structures, commitments, motivators

Love, in its attachment-related forms, can be understood as an emergent mode of organization within a security-regulation subsystem of the mind, embedded in a broader architecture of interacting control processes. This subsystem monitors conditions of vulnerability—threat, uncertainty, separation, dependency—and regulates interpersonal security through the generation and modulation of motivators oriented toward proximity, reassurance, responsiveness, and trust. Love is not a static state or a mere feeling; it is a dynamically sustained pattern of control in which these motivators are selectively activated, prioritized, and integrated with other ongoing processes.

In more developed forms, love is structured not only by transient motivator activation but by enduring commitments in the sense articulated by Michel Aubé and discussed above. A commitment is a relatively stable configuration of motivators and control dispositions that persists across contexts and time. Love typically involves not a single overarching commitment, but a network of interrelated commitments: to support, attend to, protect, understand, cooperate with, and foster the growth of the other. These commitments are often fine-grained and situationally specific, yet coordinated into more stable patterns of interaction. In adult relationships, love characteristically involves an exchange of commitments, in which each person forms, maintains, and revises commitments in relation to the other. Thus love entails a disposition to create derived commitments. That means that commitments operate at different levels of abstraction.

This framework applies across relational forms—romantic partnerships, friendships, parent–child relations, and fraternal or brotherly love—each involving distinct but overlapping configurations of such commitment networks.

Love, thus understood, entails caring for the other in a commitment-grounded way. When the other is perceived to be threatened or in difficulty, the system tends to generate insistent motivators directed toward helping, protecting, or supporting them. These motivators recruit attention, guide action selection, and can temporarily dominate processing, reflecting the priority conferred by underlying commitments. The stability of love depends less on the persistence of affective states than on the robustness, coherence, and mutual alignment of these commitments across changing conditions.

Fromm’s classical characterization of mature love, articulated in The Art of Loving — involving care, responsibility, respect and knowledge — can be reinterpreted within this architectural framework. Care corresponds to the sustained generation of motivators oriented toward the life and growth of the other. Responsibility reflects a standing, commitment-based readiness to respond to the other’s needs. Respect involves being guided by an understanding of the other as an autonomous agent with their own goals and developmental trajectory. It is not merely accurate representation (knowledge), but a constraint on commitment: it limits treating the other as an object of control, projection, or use, and includes a disposition to refrain from overriding their agency even under strong, insistent motivators. Knowledge reflects the ongoing refinement of these models, enabling commitments to be enacted in a context-sensitive and effective manner. These are not merely ethical ideals but functional properties of well-configured commitment structures within an interpersonal regulatory system.

Within an architectural perspective, these processes span multiple layers. At the reactive layer, rapid, affectively charged responses—such as alarm, distress, or comfort—are triggered by cues of threat or availability, supporting immediate proximity-seeking or caregiving actions. At the deliberative layer, internal working models and situational interpretations guide planning, decision-making, and the formation and revision of commitments. At the reflective layer, agents can evaluate their own commitments, regulate their responses, and engage in higher-order processes such as perspective-taking, norm-guided adjustment, and long-term coordination of shared goals. Love, in its mature forms, depends critically on the integration of these layers: reactive processes provide urgency and salience; deliberative processes support flexible coordination; reflective processes enable stability, coherence, and ethical regulation.

Internal working models function as learned predictive structures that shape perception, interpretation, and action selection across these layers. They influence how readily commitments are formed, how they are calibrated, and how they are revised in light of experience. They also bias the generation and interpretation of signals relevant to relational security, thereby affecting both motivator activation and commitment stability.

A key dynamic within this system is the modulation of insistence. Under conditions of uncertainty or threat, commitment-relevant motivators—such as those driving proximity-seeking or helping—can become highly insistent, capturing attention and biasing cognition toward the other. When regulation succeeds, insistence subsides, allowing other processes, such as exploration and task engagement, to proceed. However, when regulation fails, or when commitments are destabilized or in conflict, the system can enter states of sustained high insistence. These states are closely related to mental perturbance: intrusive, repetitive, and difficult-to-regulate patterns of thought and affect that reflect the persistent activation of insistent concerns.

Individual differences in attachment can be understood as differences in how commitment structures and insistence dynamics are calibrated. Anxious patterns involve rapid activation and escalation of insistence, often linked to fragile or unstable commitments. Avoidant patterns involve the attenuation or suppression of commitment-related motivators, often through gating mechanisms at reactive or deliberative levels. Secure patterns reflect a more adaptive configuration in which commitments are stable yet flexible, motivators are appropriately responsive, and insistence is effectively regulated.

Finally, love emerges as a multi-system integration involving attachment, caregiving, erotic, and exploratory subsystems. Commitments play a central coordinating role in this integration, stabilizing interactions over time and aligning multiple processes toward shared ends. The particular configuration of commitments varies across relational forms: parental love involves asymmetrical commitments centered on care and development; friendships involve reciprocal and negotiated commitments; romantic love integrates attachment, caregiving, and erotic processes within a dense web of shared commitments. Across these forms, the quality of love depends on how effectively these commitment-structured processes are configured, integrated, and regulated over time.

From this perspective, a design-oriented theory of love treats love not as a monolithic state or a transient feeling, but as a dynamically regulated, multi-layer, commitment-organized mode of functioning—one that can be analyzed, supported, and, in principle, improved through a deeper understanding of its underlying architecture.

Notes

1. If you’d like to learn in depth about attachment from this perspective, then read our lengthy 2017 peer-reviewed paper, Attachment Modelling: From Observations to Scenarios to Designs | Springer Nature Link (pp 227–271) part of the book, Computational Neurology and Psychiatry | Springer Nature Link.

2. In my next post I will publish an integrative design-oriented theory of the nature of grief and why grief endures.

I consider mental perturbance to be one of the key concepts necessary to understand the human mind. The situation with understanding perturbance is like the proverbial apple that fell on Newton’s head. Everyone knows repetitive thought is a key feature of human mind, but few people recognize that these phenomena can be understood as manifestations of a common architectural condition—mental perturbance. The situation is similar with respect to understanding art: everyone knows that repetition is a key feature of music, poetry and song. But few people realize repetition is used by artists because it resembles a key feature of mind: perturbance. Repetition in art can sustain activation of concerns or expectations without resolution, thereby inducing a controlled, aesthetic form of perturbance-like attentional capture. To understand mental perturbance requires an integrative design-oriented approach. It is defined in integrative design-oriented terms. (I’m not suggesting that the concept of perturbance has the same rigoror or maturity as Newton’s laws of motion.)

Margaret Boden (accolades of her here) recognized the importance of mental perturbance in her commentary on our 1996 paper:

The theoretical work of Wright, Sloman, and Beaudoin is a significant contribution to our understanding of the nature and function of emotions, and potentially also to therapeutic method. Their message that emotions, as controlling and scheduling mechanisms, are essential to any complex intelligent system (that is: one with multiple and potentially conflicting motives, and situated in a changing and unfriendly world) is important. It encourages cognitive psychologists to regard emotions as a central aspect of intelligence, not to sideline (or even to forget) them, as is too often done. In addition, it shows—at least in general outline—how emotions might be understood in computational terms.

Critics of computational psychology too often assume that emotions and motivation must lie outside its scope. This is not so, although it is true that relatively little computational work has been done in this area. Interestingly, the very early days of AI saw a comparatively high proportion of models of multiple goal-seeking, affective processes, and even personality (see, for example, the papers in Tomkins and Messick 1963). But, inevitably, these were so crude as to be almost useless in illuminating the nature of emotion and motivation. As AI-workers and their psychologist colleagues found to their cost, it is difficult enough to model the pursuit of one goal, never mind the pursuit of several mutually conflicting goals. Accordingly, personality and emotion soon faded from the scene as preferred topics of computational research—but not before I too had argued that they could, in principle, be so addressed (Boden 1965; 1972).

I suggested—in the sketchiest terms—that the concepts of William McDougall’s personality theory could be interpreted in a computational fashion (Boden 1965; 1972). His concept of sentiment, for example, marks what Wright et al. call “goal-sets,” and his master sentiment of self-regard relates to the descriptive and normative aspects of the self-image to which they draw our attention. He even described emotions as (conscious) control mechanisms, enabling the subject to monitor the incipient excitement of distinct types of motivation, and (sometimes) to adjust behavior so as to facilitate or inhibit the otherwise-inevitable response. What the target-authors have done is to put subtle and sophisticated computational flesh onto these minimalist theoretical bones.

McDougall himself, of course, will be turning in his grave. He saw affect as partly conscious (as well as dispositional), and argued that consciousness—and purposive behavior too—cannot possibly [End Page 135] be given a mechanistic, or physicalist, explanation. Despite Wright et al.’s persuasive anti-reductionist remarks about the necessity for highly abstract (psychological) levels of explanation, he would not have accepted that such explanations could be computationally grounded. He even insisted that the neurophysiological ground of goal-seeking behavior involved a special sort of energy, or horme, intrinsically directed to specific ends. Presumably, no professional reader today would agree with McDougall on that point. But many may agree with him about consciousness. And many more will insist that consciousness is a crucial aspect—perhaps the crucial aspect—of emotion. Such critics will doubtless point out that our three authors explicitly “factor out,” or refuse to discuss, consciousness as such.

Wright et al.’s rhetorical reserve on this point is defensible on two counts. First, even though emotions do involve conscious sensations, the dispositional and control aspects of affect are at least as important. Indeed, it is evident from the quotations from individuals reacting to bereavement that people often describe the phenomenology of grief in terms of these sorts of factors, rather than in a “purely qualitative” fashion (whatever that might be). Even so, these dispositional aspects of emotion are often overlooked, or regarded as less significant than the conscious phenomenology. And second, theoretical psychologists in general sideline consciousness: either they ignore it entirely, or they take it for granted philosophically and ask about the conditions under which it appears or disappears. They are well advised to do so, since we do not yet understand this concept (more accurately: this mixed bag of related concepts) at the philosophical level. Even a journal whose title has “Philosophy” as its leading word need not insist that every time consciousness is mentioned it must be philosophically discussed.

The specific example of grief, as the authors show, is a richly structured phenomenon, broadly distributed within the mind as a whole. As they also point out, it is essentially connected with attachment and personal love, from whose psychological complexity the complexity of grief is derived. Wright et al. are by no means the first to point out the complexities of love. Quite apart from the many intuitive presentations in literature, some philosophers who have analyzed the concept of personal love have shown that our everyday understanding of this concept assumes the presence of the sorts of dispositional factors highlighted in this paper (Fisher 1990). Similarly, these authors are not the first to describe the phenomenology and behavioral expressions of grief.

What they have done is to show–in relatively clear, and potentially testable terms–how this sort of psychological phenomenon is possible, and how the cognitive, affective, and motivational aspects of the mind are intimately combined in its very structure. That is, they have shown what sort of information-processing system a mind must be, in order that it may evince emotional phenomena of this type. In doing so, they have laid the groundwork for a systematic approach to therapy. And they have whetted our appetites–or at least, mine–for the implementational studies that are already being commenced in their research program.

Boden’s analysis highlights something that remains underdeveloped even today: the need to understand emotion as an architectural phenomenon involving executive function being captured by insistent motivators; that means the mind contains mechanisms to generate and activate motives (we call them “motive generators”.) The concept of mental perturbance extends this line of thought by explaining how such systems can enter sustained, self-maintaining states of attentional capture.

I mention this in the hope that people — in the cognitive/affective science and elsewhere — realize there’s a reason why apples fall from trees, and they experience repetitive thought and related phenomena. As it stands, most emotion theories do not deal with such attentional aspects of emotion. What is missing is not recognition of emotion, motivation, or even attention in isolation, but an account of how insistent concerns can dynamically capture and disrupt executive control over time. What is missing across these approaches is an account of how insistent concerns dynamically capture and disrupt executive control over time. For instance, Klaus Scherer’s Componential Process Model of Emotions does not include perturbance. I asked him at a conference whether he felt attentional aspects of emotion were important and he seemed to answer in the negative. I don’t mean to single him out, it’s just an example.

Herbert Simon, in a seminal paper on emotion, and Keith Oatley got into the ballpark of perturbance. However, they did not explicitly address emotion in integrative design-oriented terms, nor capture the dispositional properties of perturbance. Simon famously departed from his colleague, Alan Newell, and rejected the concept of information-processing architectures. Aaron Sloman, who with Monica Croucher in 1981 first developed the concept of perturbance (initially calling it emotion), was among the first to explicitly develop and emphasize the notion of information-processing architectures in this context.

Panksepp also got close with his distinction between primary, secondary, and tertiary processes, which implicitly points to an architectural layering of affective and cognitive systems. In describing tertiary process he alludes to the role of attention. However, his framework does not articulate how higher-level processes can give rise to sustained, control-disruptive states such as perturbance, nor does it develop an explicit information-processing architectural account of these dynamics.

What is missing is not recognition of emotion, motivation, or even attention in isolation, but an account of how insistent concerns can dynamically capture and disrupt executive control over time.

In sum, perturbance is not an optional feature of mind, but an emergent consequence of architectures that support multiple competing motives and flexible control. It is the architectural key to understanding how concerns become insistent, capture attention, and shape the flow of human experience over time.

We carry around a great deal of knowledge, projects, and unresolved questions. But when a pocket of time opens up—standing in line, walking, waiting—we often default to whatever is most salient or insistent. We can be captured by our current perturbance. That’s rarely what is most valuable.

So I use a very simple system.

Lists of Cognitive Tasks

I maintain several lists of what I call cognitive tasks—thinking tasks.

These are not “to-dos” in the usual sense. They are prompts such as:

• questions I want to think through

• problems that need deep thinking

• ideas worth developing

• connections I suspect might be fruitful

I keep this these lists in a few plain text files using nvUltra on my Mac. nvUltra is fast, frictionless, and always under my control. The files are synced via iCloud, so I can read them on my iPhone using 1writer. I just need to type “cgvTasks” (the tag I use in the title of the file) into the search bar of either app and my lists surface.

When I have a spare moment on the go and can’t recall what I need to think through, I consult one of the lists. That’s the key move. Instead of asking, “What should I do now?” I ask, “What should I think about?”

I maintain three lists: one for Hookmark tasks, one for academic tasks, and one for everything else.

Why This Matters

I do this because I’m into mobile cognitive productivity. I want to make the best use of my time and attention on the go. I run many projects requiring deep thought at each of Simon Fraser University, CogZest (where I write books) and CogSci Apps. An office computer is not the best place to think about hard problems requiring creative solutions. It’s best to think about them while walking or commuting. This is partly how the creative incubation process works. Your work desk primes previous cognitions — new cognitions are not as likely to surface there. Walking away from the desk decreases the activation of old cognition, enabling new cognitions to emerge because they’re not competing with as many previous cognitions.

(I will write more about priming, the cue overload effect and creativity later this year.)

Mobile Time: Two Modes

Thus, I split some of my mobile time between two kinds of activity:

1. Productive practice — rehearsing, recalling, refining knowledge using Anki

2. Directed cognitive tasks — thinking through items on my list

Both are important. But the second is often neglected.

We consume information constantly—podcasts, articles, feeds. Yet without time devoted to thinking with knowledge, much of it remains unused.

The cognitive task list is my way of reclaiming that balance.

Dictation as Capture

When something useful occurs to me as I’m thinking on the go, I dictate notes on my phone. This keeps the loop tight:

• select a cognitive task

• think about it

• capture insights immediately

The goal is not polished writing. It’s to preserve fragile, intermediate thoughts before they vanish.

Productive practice guiding cognition

Productive practice is another source of cognitive tasks. Most of my challenges (flashcards) call for short answers. But some of them call for open-ended thinking. I tag some of them with “cgvTasks”, a unique identifier that I can easily search for in case I want to schedule my deliberation around them for now. But normally, I just let the Anki algorithm decide what flashcards to challenge me with.

Walking, Thinking, and Not Quite Mindfulness

I get some pushback from people when I tell them about my deliberate using of “spare” time. They prefer “being here now”. That’s fine—I’m not trying to convert people. But I should add: I’m not particularly into environmental mindfulness while on the go, i.e., paying attention to my surroundings — being here now. I’m like the head of the King of the Moon (The Adventures of Baron Munchausen scene with Robin Williams).

I do meditate—sitting, and sometimes walking. Walking meditation is itself a valuable use of mobile time.

But much of my time outside is devoted to directed thinking.

This is a deliberate choice. I run so many projects that I need to prioritize my thinking, and I enjoy doing that.

Rather than clearing the mind, I often want to engage it with purpose—to apply knowledge, explore ideas, and work through problems.

Examples

For example, here are some of the entries in my cognitive tasks list:

• Prepare topics for the discontinuities chapter of my Discontinuities book,

• reflect on David Carr’s The Night of the Gun book that I am reading.

• plan the diagrammatic reasoning humanistic meeting I’m chairing (May 26) and which I will write about.

• plan my paper on the IDO basis for psychotherapy and self-help.

That’s a long enough list to give me flexibility in what I want to think about. Notice it includes a review of reading task.

Software

One could of course use different software than I do for this. If you use OmniFocus, you can tag tasks with a unique tag such as “cgvTasks” and create a Perspective for them. Then on the go you can simply open that Perspective. Or you could use Apple Notes or other software that supports tags. TaskPaper (for Mac) and PaperTrail (iPhone and iPad) are also great tools for plain text task management.

The key is that it needs to be super fast and easy to access your cognitive task list.

Ideally while on the go, one could ask Siri “Read me my 3 next cognitive tasks”. However, as easy as using iOS Shortcuts are, I haven’t gotten there yet.

Designing for Thinking Opportunities

Modern life gives us many fragments of time. These are often treated as gaps to be filled passively. I treat them as thinking opportunities. But without preparation, those opportunities are wasted. You won’t necessarily reliably recall the most valuable problem to think about in the moment.

That’s why lists matters. They are a small piece of cognitive infrastructure—but the enable a productive relationship to time, knowledge, and thought.

Read more

for more on cognitive productivity check out:

• Cognitive Productivity: Using Knowledge to Become Profoundly Effective, and

• Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge

If your answer is something like 10 to 15 years—from undergraduate degree through PhD and a few postdoctoral projects—then Aaron Sloman thinks you are underestimating the problem.

Is our educational system producing thinkers of the kind that are required for solving some of the hardest problems in cognitive science and AI? In a remarkable Oxford interview, Aaron Sloman argues that it is not. Aaron Sloman is an Honorary Professor of Computer Science at Birmingham. He won the 2020 Jon Barwise Prize for his sustained contributions to philosophy and computing.

Sloman emphasizes two interrelated problems. First, key modes of reasoning—such as learning to construct and verify geometric proofs—have largely disappeared from standard education, replaced by more superficial or narrowly formal approaches. Second, and more fundamentally, the timeline of education itself has become far too compressed. We expect students to become productive researchers within a few years of leaving school, under intense pressure to publish, secure grants, and accumulate citations. In his view, this system produces competent specialists but not the kind of broadly educated, deeply reflective thinkers required for major conceptual breakthroughs.

Sloman argues instead for a radically extended educational trajectory—one that allows for sustained exploration across disciplines, including mathematics, philosophy, psychology, neuroscience, linguistics, and AI, with far less pressure to produce early outputs. He suggests assistant professors be given a decade to continue their learning and research without pressure to publish.

I experienced something close to that ideal. In my undergraduate degree, I took many courses in psychology, philosophy, computer science, mathematics, linguistics, neuroscience and English literature. (Yes, even English literature contributed to my understanding of mind. For instance, I took a course on Literature and Psychology. Compare this project.) Though formally I got a degree in Psychology, it was equivalent to a degree in cognitive science, which is by definition interdisciplinary (not just multidisciplinary). I did my PhD under Aaron Sloman at the University of Birmingham, England. I was formally in its School of Computer Science (which recently has been ranked as the top computer science program in the UK) though my Ph.D. was in Cognitive Science being co-supervised by Glyn Humphreys of Psychology. I read widely across disciplines. I did not suffer from the now-standard “publish or perish” pressures. I spent long days—and many nights, often into the wee hours—thinking, modeling, and programming in AI. The emphasis was on understanding, not output; on grappling with deep problems rather than optimizing for publication metrics. That environment made a lasting impression on my approach to research and intellectual work. Even as an adjunct professor, I am not under intense pressure to publish.

My research uses an integrative design-oriented (IDO) approach that I have been developing and advocating. As outlined in A Manifesto for an Integrative Design-oriented Approach to Understanding Humans as Autonomous Agents, the IDO framework explicitly calls for an education that spans multiple interacting domains. Such an integrative framework is not something that can be mastered quickly or within narrowly defined disciplinary tracks. It obviously requires a longer, broader, and more exploratory educational process.

Aaron Sloman was asked:

KI: Do you think then that we have the right approach to develop robots? I mean, we often take the most advanced methods in sensing, planning, reasoning, acting, language processing and so on and we try to integrate them into a single system. Should we instead rather adopt a more developmental approach and learn things incrementally?

He answered:

Perhaps my answer will surprise you. I think the main challenge is our educational system. It is not producing thinkers of the kind that are required for some of the hardest problems. There are various reasons for this. One is that the teaching of geometry as I learnt it as a child has changed. Bright school- children used to learn how to solve construction problems and prove theorems in Euclidean geometry using diagrams: it was a standard part of academic education. It was part of Immanuel Kant’s education. Some of his views about the nature of human minds were based on that sort of background.

Now many school-leavers may have learnt a little logic, set theory and algebra, and perhaps learnt to reason formally from axioms expressed using logic, but they have learnt only very shallow subsets of geometry and topology. E.g., I meet graduates who tell me that at school they simply memorised facts, such as the triangle sum theorem, or Pythagoras’ theorem, but have never learnt to find and check proofs. And if they do learn to prove theorems in geometry, for instance, at a university, they may only learn it in a logical framework, e.g. starting from something like Hilbert’s axioms.

I did not learn logic and abstract set theory until I was a graduate student, but I don’t think that hampered my early mathematical development, any more than not knowing such things hampered Archimedes, or Zeno. At school, before going to university, I benefited enormously from learning to find and check geometric proofs or constructions.

There are now impressive geometry theorem provers that start from logicised versions (or variants) of Euclid’s axioms, and then use formal reasoning to produce conclusions, e.g. (Chou, Gao, & Zhang, 1994). But that’s not what the ancient mathematicians did. For example, ancient mathematicians discovered the concepts and axioms for Euclidean geometry without deriving them from axioms! They also made discoveries that went beyond Euclid’s axioms, such as the neusis construction that enables arbitrary angles to be trisected—impossible in Euclidean geometry.

However, no current AI geometry theorem provers that I know of can make such discoveries because they can only start from (possibly extended versions of) Euclid’s axioms and work out logical consequences. They can do logical and arithmetic reasoning but not spatial reasoning or make discoveries of the sorts that originally led to axiomatised geometry, including discoveries like the neusis construction (see Neusis construction and How to trisect an angle (Using P-Geometry)), which was known to ancient mathematicians (e.g. Archimedes) but was excluded from the teaching of Euclidean geometry, apparently on the grounds that it combines properties of straight edges with properties of compasses, which reduced the purity of geometry. But excluding it reduced the power of geometrical reasoners!

The point of all this is to indicate how the creative mathematical power of biological minds (a) exceeds the power of statistics/probability based learning systems, which cannot discover, represent or reason about impossibility and necessity, as Kant seems to have understood, long ago, and (b) exceeds the heuristic power of logic+algebra based formal systems in certain domains of reasoning and problem solving concerned with impossibility and necessity in spatial structures and processes — despite the fact that sophisticated logic-based theorem provers outperform all humans on certain tasks, just as computers have outperformed humans on arithmetic tasks, sorting tasks, searching tasks, and others, for several decades. But I am not aware of any computer based machine that can start with something like the knowledge at birth of a baby human and achieve the understanding of numbers of a six year old child.

The fact that the old powerful ways of thinking are no longer a standard part of education, will inevitably restrict the abilities of future AI researchers attempting to find ways to replicate human mathematical creativity. And that is likely to restrict the AI systems they develop.

Another factor relevant to progress on hard research problems, is the enormous growth of human knowledge that is now available to be learnt by potential researchers, who need a much extended education to provide the breadth and depth of understanding required for important advances. We still expect students to leave school at about 18, spend three or four years getting a first degree, and then after five or six years of post-graduate education to be ready to become successful researchers, as demonstrated by ability to produce highly cited publications and win grants. Instead, the most able potential researchers need to spend at least another decade after their first degree broadening and deepening their knowledge, including knowledge of the history and philosophy of science and mathematics to help develop their judgement. Highly accelerated pressures on young researchers to publish, get grants and attract citations (pressures I did not encounter as a young university lecturer) seriously interfere with the continued learning and development required to produce ground-breaking thinkers who can significantly advance human knowledge, though the current system may train humans to be machines for generating conference and journal papers in restricted domains, often based on groups that form efficient mutual citing communities.

I believe this educational system is inadequate to produce the kinds of researchers needed for the deepest and most difficult ground-breaking advances in knowledge, as opposed to fairly shallow extensions of current knowledge flooding journals and conferences. In the UK, the problem was hugely exacerbated by the decision around 1990 to abolish polytechnics, which were performing important educational and industrial/ commercial training functions by turning them all into universities, thereby seriously diluting resources for funding university-level research, and depriving the nation of an important post-school educational resource: its polytechnics!

On the whole, current educational systems tend not to produce graduate researchers and university lecturers with the kind of broad and deep education needed for them to perform the future-oriented functions of universities, including inspiring and guiding future ground-breaking researchers. In particular, research on understanding cognition in all its forms requires an education encompassing mathematics, chemistry, physics, biology, neuroscience, psychology, philosophy (e.g. philosophy of mathematics, of science, of language, of mind) as well as a broad and deep understanding of varieties of forms of computation and their strengths and weaknesses. A lot of the education should be project-based, but without pressure to get publications or high citation counts, as opposed to critical and constructive reviews by supervisors and peers.

We need more bright learners leaving school to be exposed to a variety of additional disciplines, learning to combine information of very different kinds when appropriate, and working on deep and difficult projects without pressure to publish and attract funds. That may be slowly happening to a very small (lucky) subset of researchers. But it’s not happening on a sufficiently wide scale, and I suspect it is not happening to enough people to generate the new thinkers who can come up with ideas that will enable us to make deep new advances and also educate the next generation to continue the process.

I was very lucky as a young graduate, because I was allowed to switch from mathematics to philosophy, and later, as a young lecturer, to switch from philosophy to AI, without anybody chasing me to get grants or to produce publications. It took me a long time. My undergraduate degree (in mathematics and physics) lasted from 1953 to 1956. As a graduate student (1957-62) I moved from mathematics to logic, to philosophy of mathematics, then became a lecturer in philosophy. Later, thanks to psychology seminars where I met Max Clowes, I encountered AI in 1969 and began to do theoretical work in AI in 1972. My first substantial AI development project between 1975 and 1978 (with David Owen, Frank O’Gorman and Geoffrey Hinton) tested ideas about vision, reported in (Sloman et al., 1978). So, between 1953 and 1978 I was lucky to experience an extended educational process in which I was mostly learning, including learning about philosophy, biology, psychology, and linguistics, then programming and AI. The learning continued long after that, and accelerated after I (formally) retired, around 2002. Far more young researchers should have that kind of breadth of education without pressures to produce anything in particular, except to go on learning, teaching, and demonstrating progress to peers and mentors, with cross-institutional reviews (but no league tables) to maintain standards. Such a culture could encourage experienced researchers to share very hard unsolved problems with younger colleagues (as Max Clowes did with me), with the possibility of triggering something new and deep, even if it takes far longer than the duration of a typical grant or a temporary research fellowship.

Deep new advances in knowledge may emerge that, despite astounding advances in technology and physical and biological sciences, our current system does not encourage, as indicated by the widespread neglect of Kant’s deep ideas among researchers in AI, psychology and neuroscience. I wonder how many other cross-disciplinary bridges are waiting to be built that can support deep new advances. Is this already happening, without my knowing about it?

If the above intrigues you, I recommend taking the time to read the entire interview.

What are hard problem in cognitive science and AI?

Yo might be curious to know what Aaron and I mean by “hard problems” in cognitive science and AI. They include understanding capabilities (not just making predictions but modeling functionality) of the human mind and brain from an integrative design-oriented perspective. In 2014, Springer published a book of papers written by former students and colleagues of Aaron Sloman, including myself, on hard topics in AI. They were written based on presentations we gave at Aaron Sloman’s festschrift in Birmingham in 2011. The book is From Animals to Robots and Back: Reflections on Hard Problems in the Study of Cognition: A Collection in Honour of Aaron Sloman (J. L. Wyatt, D. Petters, and D. Hogg (eds.))

My paper in that book is Developing Expertise with Objective Knowledge: Motive Generators and Productive Practice. The late Maggie Boden wrote the foreword to the book: Aaron Sloman: A Bright Tile in AI’s Mosaic which is well worth reading.

Conclusion

Sloman’s reflections are not merely nostalgic; they are diagnostic. They point to a structural mismatch between the kinds of minds we need and the kinds of educational systems we have built.

If we take seriously the goal of understanding natural intelligence—and building artificial systems that approach its richness—then we must also take seriously the need for longer, broader, and less pressurized forms of education. Without that, we risk producing ever more technically proficient researchers who are nonetheless constrained by narrow training and limited conceptual reach.

I will return to some of these themes in future articles here on my Substack. In particular, next week I plan to explore in more detail the cognitive science of geometry and diagrammatic reasoning—topics that Sloman researched from his own Ph.D. onwards, and that I believe are central to understanding both human cognition and the limitations of current AI systems.

Music, poetry and song are an endless source of material from which to learn about mental perturbance. They can both generate and represent perturbance, often through their use of repetition.

Repetition is not merely an aesthetic device. It is a cognitive one. It can simulate, evoke, and even induce the very phenomenon that defines perturbance: the return of the same thought, image, or concern, again and again, despite attempts to move on.

Love songs provide especially clear examples. Many portray limerence—the intrusive, persistent focus on a desired person—through repeated phrases, refrains, or motifs that mirror the structure of the underlying mental state. Consider a song like The Police’s “Every Breath You Take” (1983). The repeated lines—“Every breath you take / Every move you make”—return insistently to the same object of attention. The structure of the song mirrors the cognitive pattern of limerence: a narrowing and persistence of attention repeatedly drawn back to the same person. The repetition does not merely describe the state; it enacts it, simulating the difficulty of disengagement. Each recurrence functions like a perturbant loop: attention is recaptured, and disengagement is delayed.

Similarly, songs about grief often circle around a loss, revisiting the same absence from slightly different angles. A refrain may return with minimal variation, each time reinforcing the persistence of the concern. The listener is drawn into a simulation of perturbance: attention is guided back, again and again, to what cannot be resolved. To choose a concrete example, consider Stan Rogers’ song “White Squall” (1975). The refrain returns with the line, “Tonight, some red-eyed Wiarton girl lies staring at the wall. And her lover's gone into a white squall,” circling back again and again to the moment of loss. The repetition does not resolve the loss; it re-enacts it, drawing attention repeatedly to the absence and the speaker’s relation to it. Each return functions as a perturbant loop, reactivating the same concern and sustaining the listener’s focus on the loss.

This is one example of how we can engage in self-directed learning from stories: using cultural artifacts—songs, poems, narratives—not just to feel, but to analyze the mechanisms that generate those feelings, including the dynamics of mental perturbance.

What other songs or other stories can you think of in which repetition reflects an inability to disengage?

Why does your mind return, again and again, to the same person or concern—sometimes for days, weeks, or even months?

They are examples of what I call mental perturbance: states in which insistent concerns capture or disrupt executive control, making attention difficult to regulate. In other words, perturbance is what happens when something matters so much that it interferes with your ability to control your own mind.

In 1996, I became third author of a paper on emotions as perturbance that drew heavily on my PhD work. It introduced the concept of perturbance in the context of a “design-based” approach to mind. I’m pleased to report that this paper has now been selected for inclusion in the 2026 four-volume reference work Artificial Intelligence: Critical Concepts in Cognitive Science—a collection intended to map the intellectual development of AI as a field contributing to cognitive science.

Here is the abstract of our reprised 1996 paper:

The design-based approach is a methodology for investigating mechanisms capable of generating mental phenomena, whether introspectively or externally observed, and whether they occur in humans, other animals or robots. The study of designs satisfying requirements for autonomous agency can provide new deep theoretical insights at the information processing level of description of mental mechanisms. Designs for working systems (whether on paper or implemented on computers) can systematically explicate old explanatory concepts and generate new concepts that allow new and richer interpretations of human phenomena. To illustrate this, some aspects of human grief are analyzed in terms of a particular information processing architecture being explored in our research group. We do not claim that this architecture is part of the causal structure of the human mind; rather, it represents an early stage in the iterative search for a deeper and more general architecture, capable of explaining more phenomena. However even the current early design provides an interpretative ground for some familiar phenomena, including characteristic features of certain emotional episodes, particularly the phenomenon of perturbance (a partial or total loss of control of attention).

The paper attempts to expound and illustrate the design-based approach to cognitive science and philosophy, to demonstrate the potential effectiveness of the approach in generating interpretative possibilities, and to provide first steps towards an information processing account of “perturbant,” emotional episodes.

Sylwia Hyniewska, Monika Pudlo and I published an update to this paper in 2020. Our paper was titled Mental perturbance: An integrative design-oriented concept for understanding repetitive thought, emotions and related phenomena involving a loss of control of executive functions. Our paper modernizes the concept of perturbance, relating it to the fields of study of repetitive thought, obsession and related phenomena involving disturbances of attention. The 2020 paper uses grief and limerence (romantic ‘love’) as examples of perturbance. Limerence is a term coined by psychologist Dorothy Tennov in 1979 in her book titled Love and Limerence: The Experience of Being in Love. She argued that limerence involves intrusive, involuntary, and persistent attention to the limerent object, combined with difficulty disengaging and heightened sensitivity to signs of reciprocation. In my terms, this is a prototypical case of perturbance: a motivator whose insistence captures executive processes—even when we attempt to disengage. It’s a loss of control of executive functions. Grief falls in the same category.

After I completed my Ph.D. in Birmingham in 1996, Aaron Sloman stopped using the term “perturbance” in favor of “tertiary emotion”, for instance in his extensive paper “How many separately evolved emotional beasties live within us?. I understand Sloman’s reasons for assimilating mental perturbance in a taxonomy of three forms of “emotion”. However, I strongly disagree with his move. The reason I coined the term “perturbance” (in 1992) in the first place (and specified it in my 1994 Ph.D. thesis) was because there is too much debate and confusion about the term “emotion”. See this paper for instance: The Many Meanings/Aspects of Emotion: Definitions, Functions, Activation, and Regulation by Carroll E. Izard, 2010. For example, it is now agreed in the emotion literature that emotions are necessarily short-lived episodes. There’s no such thing as an emotion that lasts for days. In contrast limerence, grief and other forms of perturbance can in fact last for months! Using a technical term such as “perturbance” allows us to avoid needless debate about whether we are actually discussing emotions or not.

The abstract of our 2000 paper on perturbance is:

Understanding intrusive mentation, rumination, obsession, and worry, known also as “repetitive thought” (RT), is important for understanding cognitive and affective processes in general. RT is of transdiagnostic significance—for example obsessive-compulsive disorder, insomnia and addictions involve counterproductive RT. It is also a key but under-acknowledged feature of emotional episodes. We argue that RT cannot be understood in isolation but must rather be considered within models of whole minds and for this purpose we suggest an integrative design- oriented (IDO) approach. This approach involves the design stance of theoretical Artificial Intelligence (the central discipline of cognitive science), augmented by systematic conceptual analysis, aimed at explaining how autonomous agency is possible. This requires developing, exploring and implementing cognitive-affective-conative information-processing architectures. Empirical research on RT and emotions needs to be driven by such theories, and theorizing about RT needs to consider such data. Mental perturbance is an IDO concept that, we argue, can help characterize, explain, and theoretically ground the concept of RT. Briefly, perturbance is a mental state in which motivators tend to disrupt, or otherwise influence, executive processes even if reflective processes were to try to prevent or minimize the motivators’ influence. We draw attention to an IDO architecture of mind, H-CogAff, to illustrate the IDO approach to perturbance. We claim, further, that the intrusive mentation of some affective states— including grief and limerence (the attraction phase of romantic love) — should be conceptualized in terms of perturbance and the IDO architectures that support perturbance. We call for new taxonomies of RT and emotion in terms of IDO architectures such as H-CogAff. We point to areas of research in psychology that would benefit from the concept of perturbance.

Its Keywords are: repetitive thought, emotions, executive functions, cognitive architectures, autonomous agents, affective computing

Speaking of terminology, I moved away from the expression “design-based modeling” because design-based research has taken on a different meaning in the social sciences. I replaced it with the term integrative design-oriented (IDO) R&D. I refined the concept in the 2020 paper and in A Manifesto for an Integrative Design-oriented Approach to Understanding Humans as Autonomous Agents. In an upcoming paper I apply the IDO approach and concept of mental perturbance to the problem of understanding sleep onset and insomnolence: [Beaudoin, L.P. & Guloy, S. (in press). Towards a somnolent information-processing theory: Understanding the human sleep-onset control system from an integrative design-oriented perspective. In J. Dzierzewski, D. Kay & S. J. Aton (Eds), The Cambridge Handbook of Sleep Theories and Models. Cambridge University Press.]

I hope inclusion in the Artificial Intelligence: Critical Concepts in Cognitive Science series of our 1996 paper will increase the visibility of the concept of perturbance. Here’s a blog post of mine, published in Sept 2020, which explains the concept of perturbance: Attention! Have you lost it?

Fictopoeisis is the creation or reading of forward-looking stories, stories with which to think, explore, create, and experience future worlds and peoples to come. One can “consume” fictopoeitic works created by oneself or others. Story-based bibliotherapy is a form of self-directed or therapist-assisted therapy that utilizes stories of others. This presentation will focus on bibliotherapy utilizing fictopoeitic works created by others. In the Knowledge Age people process so much information, be it fiction or non-fiction, that it is difficult to learn from any such information. Productive practice is a form of spaced learning involving posing challenges (questions, etc.) and answering them at spaced-learning using flashcard software such as Anki and Remnote. It is a form of learning based on research on deliberate practice (expertise), memory retrieval and testing effects, and test-enhanced learning. According to the heuristic relevance-signaling hypothesis, which is based on the foregoing cognitive science literature, information is more likely not to remain inert but to be psychologically accessible if one practices retrieving and utilizing the information at spaced intervals. We will explore how cognitive-behavioral and metacognitive therapy homework can involve productive practice of fictopoeitic works. This will draw on my two Cognitive Productivity and Discontinuities: Love, Art, Mind books.

I welcome feedback on this draft. The deadline for submission is April 30 2026.

Here I describe the motivation behind BSBM+, explain how it is meant to work, and situate it relative to other meditation practices that have received empirical study. While BSBM+ draws on findings from cognitive science and contemplative research, it has not yet been directly tested. My hope is that this article will spur discussion and empirical investigation.

Why Single-Anchor Meditation and Open Monitoring Can Be Difficult

Many people who try meditation, whether or not they have ADHD, find it difficult to remain focused on the object of their meditation. One reason may lie in the structure of the practices themselves. Most commonly taught meditations rely on a single anchor of attention. In breath meditation, the anchor is the breath. In body-scan meditation, the anchor is one body part at a time. In mantra meditation, the anchor is the repeated word or phrase.

According to Baddeley’s influential model of working memory, cognition relies on several interacting subsystems: the phonological loop, the visuospatial sketchpad, the episodic buffer, and the central executive, which coordinates attention, evaluation, and decision-making. Single-anchor meditations tend to engage only a subset of these systems. For example, mantra meditation primarily engages the phonological loop with relatively light executive demand. Breath meditation draws on the central executive and semantic processing of bodily signals. Body-scan meditation relies mainly on visuospatial and somatic representations coordinated by executive control.

When only one subsystem is lightly engaged, substantial unused capacity remains available for mental perturbance — insistent concerns, memories, anticipations, and other forms of task-unrelated thought. This may help explain why many practitioners experience frequent attentional drift despite sustained effort.

Open monitoring meditations, while different in structure, can also be difficult for beginners. Because they impose minimal constraints on working-memory content, they may allow habitual concerns or repetitive thought patterns to dominate awareness, particularly when executive control is already taxed.

Multi-Anchor Meditations and Working Memory

A small number of meditation practices engage multiple working-memory subsystems simultaneously. Two such practices that have received scientific attention are Kirtan Kriya and Vajrayana (Tibetan Buddhist) meditation.

Kirtan Kriya can be described, in working-memory terms, as a practice that keeps the mind gently but broadly occupied. Practitioners repeat a short sequence of sounds (“Sa Ta Na Ma”), engaging the phonological loop; coordinate each syllable with finger movements, engaging visuospatial and somatosensory systems; and often visualize the sounds moving through the body, further loading visual imagery. These coordinated activities place only modest demands on the central executive, but together they leave little free capacity for distracting or repetitive thought. Clinical studies, particularly in aging and dementia-risk populations, suggest benefits for mood, stress regulation, and aspects of cognitive function.

Vajrayana meditation similarly employs multiple coordinated elements, though practices vary widely. These may include mantra, visualization, posture, and controlled breathing. From a design perspective, both Kirtan Kriya and Vajrayana meditation illustrate how distributing attentional demands across multiple subsystems can stabilize attention without excessive executive effort. However, Vajrayana meditation’s explicit embedding in a spiritual and ritual framework may limit its uptake in secular or clinical contexts. Kirtan Kriya, though also originating in a spiritual tradition (Kundalini Yoga), has been more widely adapted for secular research and intervention.

Controlled Breathwork and the Physiological Sigh

Recent research by Melis Yilmaz Balban and colleagues has shown that controlled breathwork—termed “structured respiration” in the original paper—can reduce physiological arousal and improve mood more effectively than simple breath-observation meditation. In particular, the physiological sigh, consisting of a deep inhale followed by a second short inhale and a long exhale, appears to be especially effective. This finding is highly relevant for meditation designs that aim to support emotional regulation and stress reduction.

What Is BSBM+?

BSBM+ is an explicitly secular, multi-anchor meditation that engages working memory more broadly than either Kirtan Kriya or Vajrayana meditation, while also incorporating body scanning and controlled breathwork. I conjecture that, once learned, BSBM+ may allow some practitioners to maintain attentional stability more effectively than single-anchor meditations and possibly more than existing multi-anchor practices. Because it incorporates the physiological sigh, I further hypothesize that it may lead to greater reductions in physiological arousal.

The breadth of engagement in BSBM+ is, however, a double-edged sword. Individuals with reduced working-memory capacity, or those experiencing intense stress, may initially find the full practice demanding. For this reason, BSBM+ is intentionally flexible: components can be simplified or omitted. The practice also typically requires more training than the other forms of meditation reviewed here.

The BSBM+ Practice

BSBM+ begins with a set of five physiological sighs. Additional sets of physiological sighs are interspersed between body-scan phases and after the final scan.

Following the initial sighs, the practitioner begins a body scan starting with the head and shoulders. During the scan, one simultaneously engages breath awareness and controlled breathing, along with mantra repetition. The mantra is typically a single syllable and is timed rhythmically to the breath, with one word repeated on the inhale (for example, “in”) and another on the exhale (for example, “out”). Practitioners are encouraged to breathe through the nose.

If the practitioner knows a second language, even superficially, the mantra terms are ideally drawn from that language. This choice is based on the hypothesis that habitual inner speech tends to occur in one’s first language, and that switching languages may help quiet verbal mental chatter.

The body scan proceeds in sections:

• head, shoulders, arms, and hands, followed by a set of physiological sighs

• torso, followed by a set of physiological sighs

• pelvic area, legs, and feet, followed by a final set of physiological sighs

Throughout the practice, mantra repetition continues during both scanning and sighing.

As in Kirtan Kriya, finger movements are also used, though not continuously. In BSBM+, fingers may be used to count breaths during the physiological sighs or to attend to sensations while scanning them, such as pressing each finger on an armrest or leg while scanning it. This adds further load to working-memory subsystems, including kinesthetic representations plausibly associated with the episodic buffer.

The “+” Phase: Flexible Phase

We have now covered the “BSBM” portion of BSBM+. The “+” refers to an optional final phase of meditation that is flexible, typically lasting two to five minutes. In this phase, practitioners may choose among unstructured open monitoring practices such as:

• reflective meditation, in which one allows the mind to wander while observing one’s thinking (see Jason Siff’s Thoughts Are Not the Enemy and The Skill of Effortless Meditation)

• an objectless open monitoring meditation, such as Shikantaza

Alternatively, practitioners may choose a further focused attention, structured meditation, such as:

• mantra repetition

• detailed scanning of a single body region

• transfer-appropriate self-regulation rehearsal, such as mentally rehearsing how one might respond through counteractive construals to a temptation (for example, overeating), or to worry (for example, by thinking “worry is the thief of joy”)

I hypothesize that rehearsing self-regulation scripts during this post-meditation phase may be especially effective, because brief mindfulness practices can acutely strengthen attention and inhibitory control. However, this specific post-meditation rehearsal advantage has not yet been directly tested.

Ideally, the type of meditation used in the “+” phase is chosen at the beginning of the BSBM+ session, allowing one to flow smoothly into it with minimal additional executive demand.

After the “+” phase, one may optionally perform one or more physiological sighs to terminate the practice.

To save time, practitioners may replace sets of five physiological sighs with single sighs, or omit the “+” phase entirely.

Conclusion

BSBM+ can be understood as a design-oriented experiment in meditation architecture rather than as a refinement of any single contemplative tradition. By deliberately engaging multiple working-memory subsystems while incorporating empirically supported controlled breathwork, it aims to stabilize attention, reduce physiological arousal, and strengthen meta-cognitive control. Its flexibility allows adaptation to individual capacities and goals, while its richness places greater demands on learning and practice. While BSBM+ may initially seem complex, with practice it should become increasingly familiar and automatic.

It is hoped and hypothesized that repeated practice of the BSBM portion of BSBM+—by placing coordinated demands on working memory and executive functions—will support the development of more robust focused attention. This may help prepare the mind for a possible open, unstructured “+” phase, consistent with the common sequencing in contemplative science from focused attention practices to open monitoring, while still allowing practitioners to choose focused attention meditation in the “+” phase if they prefer.

Although BSBM+ has not yet been empirically evaluated as a complete intervention, its components are grounded in established cognitive and contemplative science. Systematic empirical testing—particularly comparisons with single-anchor and existing multi-anchor practices—would be a valuable next step. For readers interested in meditation as a form of cognitive training rather than solely a contemplative practice, BSBM+ offers a testable design hypothesis.

Related Readings and References

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory. Trends in Cognitive Sciences, 4(11), 417–423. https://doi.org/10.1016/S1364-6613(00)01538-2

Balban, M. Y., et al. (2023). Brief structured respiration practices enhance mood and reduce physiological arousal. Cell Reports Medicine, 4(1), 100895. https://0.1016/j.xcrm.2022.100895.

Beaudoin, L. P., Pudło, M., & Hyniewska, S. (2020). Mental perturbance: An integrative design-oriented concept for understanding repetitive thought, emotions and related phenomena involving a loss of control of executive functions. SFU Educational Review, 13(1), 29-58. https://doi.org/10.21810/sfuer.v13i1.1282

Siff, J. (2014). Thoughts Are Not the Enemy. Shambhala.

Siff, J. (2021). The Skill of Effortless Meditation. Shambhala.

Khalsa, D. S. (2015). Stress, meditation, and Alzheimer’s disease prevention: Where the evidence stands. Journal of Alzheimer’s Disease, 48(1), 1–12. https://doi.org/10.3233/JAD-142766

Lutz, A., Slagter, H. A., Dunne, J. D., & Davidson, R. J. (2008). Attention regulation and monitoring in meditation. Trends in Cognitive Sciences, 12(4), 163–169. https://doi.org/10.1016/j.tics.2008.01.005

Sumantry, D., & Stewart, K. E. (2021). Meditation, mindfulness, and attention: A meta-analysis. Mindfulness, 12(6), 1332–1349. https://doi.org/10.1007/s12671-021-01593-w

NB

this is my second Substack article on this subject. This is an update to my previous “update”

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