Humans and animals make inferences about the world under limited time and knowledge. In contrast, many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time, knowledge, and computational might. Following H. Simon's notion of satisficing, the authors have proposed a family of algorithms based on a simple psychological mechanism: one-reason decision making. These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up nor integrate all information. By computer simulation, the (...) authors held a competition between the satisficing "Take The Best" algorithm and various "rational" inference procedures. The Take The Best algorithm matched or outperformed all competitors in inferential speed and accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance in the real world do not need to satisfy the classical norms of rational inference. (shrink)

According to doxastic pragmatism, certain perceived practical factors, such as high stakes and urgency, have systematic effects on normal subjects’ outright beliefs. Upholders of doxastic pragmatism have so far endorsed a particular version of this view, which we may call threshold pragmatism. This view holds that the sensitivity of belief to the relevant practical factors is due to a corresponding sensitivity of the threshold on the degree of credence necessary for outright belief. According to an alternative but yet unrecognised version (...) of doxastic pragmatism, practical factors affect credence rather than the threshold on credence. Let’s call this alternative view credal pragmatism. In this paper, I argue that credal pragmatism is more plausible than threshold pragmatism. I show that the former view better accommodates a cluster of intuitive and empirical data. I conclude by considering the issue of whether our doxastic attitudes’ sensitivity to practical factors can be considered rational, and if yes, in what sense. (shrink)

Recent work in economics has rediscovered the importance of belief-based utility for understanding human behaviour. Belief ‘choice’ is subject to an important constraint, however: people can only bring themselves to believe things for which they can find rationalizations. When preferences for similar beliefs are widespread, this constraint generates rationalization markets, social structures in which agents compete to produce rationalizations in exchange for money and social rewards. I explore the nature of such markets, I draw on political media to illustrate their (...) characteristics and behaviour, and I highlight their implications for understanding motivated cognition and misinformation. (shrink)

I clarify and defend the hypothesis that human belief formation is sensitive to social rewards and punishments, such that beliefs are sometimes formed based on unconscious expectations of their likely effects on other agents – agents who frequently reward us when we hold ungrounded beliefs and punish us when we hold reasonable ones. After clarifying this phenomenon and distinguishing it from other sources of bias in the psychological literature, I argue that the hypothesis is plausible on theoretical grounds and I (...) show how it illuminates and unifies a range of psychological phenomena, including confabulation and rationalisation, positive illusions, and identity-protective cognition. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame these interactions (...) as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

Recent work in computational and cognitive neuroscience depicts the brain as an ever‐active prediction machine: an inner engine continuously striving to anticipate the incoming sensory barrage. I briefly introduce this class of models before contrasting two ways of understanding the implied vision of mind. One way (Conservative Predictive Processing) depicts the predictive mind as an insulated inner arena populated by representations so rich and reconstructive as to enable the organism to ‘throw away the world’. The other (Radical Predictive Processing) stresses (...) the use of fast and frugal, action‐involving solutions of the kind highlighted by much work in robotics and embodied cognition. But it goes further, by showing how predictive schemes can combine frugal and more knowledge‐intensive strategies, switching between them fluently and continuously as task and context dictate. I end by exploring some parallels with work in enactivism, and by noting a certain ambivalence concerning internal representations and their role in the predictive mind. (shrink)

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this “Now-or-Never” bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must “eagerly” recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build (...) a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with “Right-First-Time”; once the original input is lost, there is no way for the language system to recover. This is “Chunk-and-Pass” processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution. (shrink)

p. cm. — (Zeuthen lecture book series) Includes bibliographical references (p. ) and index. ISBN 0-262-18187-8 (hardcover : alk. paper). — ISBN 0-262-68100-5 (pbk. : alk. paper) 1. Decision-making. 2. Economic man. 3. Game theory. 4. Rational expectations (Economic theory) I. Title. II. Series.

Behaviourism is the view that preferences, beliefs, and other mental states in social-scientific theories are nothing but constructs re-describing people's behaviour. Mentalism is the view that they capture real phenomena, on a par with the unobservables in science, such as electrons and electromagnetic fields. While behaviourism has gone out of fashion in psychology, it remains influential in economics, especially in ‘revealed preference’ theory. We defend mentalism in economics, construed as a positive science, and show that it fits best scientific practice. (...) We distinguish mentalism from, and reject, the radical neuroeconomic view that behaviour should be explained in terms of brain processes, as distinct from mental states. (shrink)

Libertarian paternalism's notion of “nudging” refers to steering individual decision making so as to make choosers better off without breaching their free choice. If successful, this may offer an ideal synthesis between the duty to respect patient autonomy and that of beneficence, which at times favors paternalistic influence. A growing body of literature attempts to assess the merits of nudging in health care. However, this literature deals almost exclusively with health policy, while the question of the potential benefit of nudging (...) for the practice of informed consent has escaped systematic analysis. This article focuses on this question. While it concedes that nudging could amount to improper exploitation of cognitive weaknesses, it defends the practice of nudging in a wide range of other conditions. The conclusion is that, when ethically legitimate, nudging offers an important new paradigm for informed consent, with a special potential to overcome the classical dilemma between paternalistic beneficence and respect for autonomy. (shrink)

Pascal’s Wager involves expected utilities. In this chapter, we examine the Wager in light of two main features of expected utility theory: utilities and probabilities. We discuss infinite and finite utilities, and zero, infinitesimal, extremely low, imprecise, and undefined probabilities. These have all come up in recent literature regarding Pascal’s Wager. We consider the problems each creates and suggest prospects for the Wager in light of these problems.

When does it make sense to act randomly? A persuasive argument from Bayesian decision theory legitimizes randomization essentially only in tie-breaking situations. Rational behaviour in humans, non-human animals, and artificial agents, however, often seems indeterminate, even random. Moreover, rationales for randomized acts have been offered in a number of disciplines, including game theory, experimental design, and machine learning. A common way of accommodating some of these observations is by appeal to a decision-maker’s bounded computational resources. Making this suggestion both precise (...) and compelling is surprisingly difficult. Toward this end, I propose two fundamental rationales for randomization, drawing upon diverse ideas and results from the wider theory of computation. The first unifies common intuitions in favour of randomization from the aforementioned disciplines. The second introduces a deep connection between randomization and memory: access to a randomizing device is provably helpful for an agent burdened with a finite memory. Aside from fit with ordinary intuitions about rational action, the two rationales also make sense of empirical observations in the biological world. Indeed, random behaviour emerges more or less where it should, according to the proposal. (shrink)

The Cognitive Reflection Test (CRT; Frederick, 2005) is designed to measure the tendency to override a prepotent response alternative that is incorrect and to engage in further reflection that leads to the correct response. It is a prime measure of the miserly information processing posited by most dual process theories. The original three-item test may be becoming known to potential participants, however. We examined a four-item version that could serve as a substitute for the original. Our data show that it (...) displays a.58 correlation with the original version and that it has very similar relationships with cognitive ability, various thinking dispositions, and with several other rational thinking tasks. Combining the two versions into a seven-item test resulted in a measure of miserly processing with substantial reliability (.72). The seven-item version was a strong independent predictor of performance on rational thinking tasks after the variance accounted for by cognitive ability and thinking dispositions had been partialled out. (shrink)

ABSTRACTHumans are cognitive misers because their basic tendency is to default to processing mechanisms of low computational expense. Such a tendency leads to suboptimal outcomes in certain types of hostile environments. The theoretical inferences made from correct and incorrect responding on heuristics and biases tasks have been overly simplified, however. The framework developed here traces the complexities inherent in these tasks by identifying five processing states that are possible in most heuristics and biases tasks. The framework also identifies three possible (...) processing defects: inadequately learned mindware; failure to detect the necessity of overriding the miserly response; and failure to sustain the override process once initiated. An important insight gained from using the framework is that degree of mindware instantiation is strongly related to the probability of successful detection and override. Thus, errors on such tasks cannot be unambiguously attributed to miserly processing – and corre... (shrink)

Satisficing Consequentialism is often rejected as hopeless. Perhaps its greatest problem is that it risks condoning the gratuitous prevention of goodness above the baseline of what qualifies as "good enough". I propose a radical new willpower-based version of the view that avoids this problem, and that better fits with the motivation of avoiding an excessively demanding conception of morality. I further demonstrate how, by drawing on the resources of an independent theory of blameworthiness, we may obtain a principled specification of (...) what counts as "good enough". (shrink)

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...) fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling-based approximations are a common way to implement Bayesian (...) inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst-case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling-based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource-constrained cognition. (shrink)

Despite numerous and increasing attempts to define what life is, there is no consensus on necessary and sufficient conditions for life. Accordingly, some scholars have questioned the value of definitions of life and encouraged scientists and philosophers alike to discard the project. As an alternative to this pessimistic conclusion, we argue that critically rethinking the nature and uses of definitions can provide new insights into the epistemic roles of definitions of life for different research practices. This paper examines the possible (...) contributions of definitions of life in scientific domains where such definitions are used most (e.g., Synthetic Biology, Origins of Life, Alife, and Astrobiology). Rather than as classificatory tools for demarcation of natural kinds, we highlight the pragmatic utility of what we call operational definitions that serve as theoretical and epistemic tools in scientific practice. In particular, we examine contexts where definitions integrate criteria for life into theoretical models that involve or enable observable operations. We show how these definitions of life play important roles in influencing research agendas and evaluating results, and we argue that to discard the project of defining life is neither sufficiently motivated, nor possible without dismissing important theoretical and practical research. (shrink)

Deontologists have long been upbraided for lacking an account of justified decision- making under risk and uncertainty. One response is to develop a deontological decision theory—a set of necessary and sufficient conditions for an act’s being permissible given an agent’s imperfect information. In this article, I show that deontologists can make more use of regular decision theory than some might have thought, but that we must adapt decision theory to accommodate agent- centered options—permissions to favor or sacrifice our own interests, (...) when doing so is overall morally worse. Accommodating options requires more than just amend- ing the decision-theoretic ‘value function’. We must change the decision rule as well. (shrink)

Information is a central notion for cognitive sciences and neurosciences, but there is no agreement on what it means for a cognitive system to acquire information about its surroundings. In this paper, we approximate three influential views on information: the one at play in ecological psychology, which is sometimes called information for action; the notion of information as covariance as developed by some enactivists, and the idea of information as minimization of uncertainty as presented by Shannon. Our main thesis is (...) that information for action can be construed as covariant information, and that learning to perceive covariant information is a matter of minimizing uncertainty through skilled performance. We argue that the agent’s cognitive system conveys information for acting in an environment by minimizing uncertainty about how to achieve her intended goals in that environment. We conclude by reviewing empirical findings that support our view and by showing how direct learning, seen as instance of ecological rationality at work, is how mere possibilities for action are turned into embodied know-how. Finally, we indicate the affinity between direct learning and sense-making activity. (shrink)

If citizens’ behavior threatens to harm others or seems not to be in their own interest, it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end. Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences. Two contrasting approaches to behavior change are nudge policies and boost policies. (...) These policies rest on fundamentally different research programs on bounded rationality, namely, the heuristics and biases program and the simple heuristics program, respectively. This article examines the policy–theory coherence of each approach. To this end, it identifies the necessary assumptions underlying each policy and analyzes to what extent these assumptions are implied by the theoretical commitments of the respective research program. Two key results of this analysis are that the two policy approaches rest on diverging assumptions and that both suffer from disconnects with the respective theoretical program, but to different degrees: Nudging appears to be more adversely affected than boosting does. The article concludes with a discussion of the limits of the chosen evaluative dimension, policy–theory coherence, and reviews some other benchmarks on which policy programs can be assessed. (shrink)

While Bayesian models have been applied to an impressive range of cognitive phenomena, methodological challenges have been leveled concerning their role in the program of rational analysis. The focus of the current article is on computational impediments to probabilistic inference and related puzzles about empirical confirmation of these models. The proposal is to rethink the role of Bayesian methods in rational analysis, to adopt an independently motivated notion of rationality appropriate for computationally bounded agents, and to explore broad conditions under (...) which Bayesian agents would be rational. The proposal is illustrated with a characterization of costs inspired by thermodynamics. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we (...) explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

To understand something involves some sort of commitment to a set of propositions comprising an account of the understood phenomenon. Some take this commitment to be a species of belief; others, such as Elgin and I, take it to be a kind of cognitive policy. This paper takes a step back from debates about the nature of understanding and asks when this commitment involved in understanding is epistemically appropriate, or ‘acceptable’ in Elgin’s terminology. In particular, appealing to lessons from the (...) lottery and preface paradoxes, it is argued that this type of commitment is sometimes acceptable even when it would be rational to assign arbitrarily low probabilities to the relevant propositions. This strongly suggests that the relevant type of commitment is sometimes acceptable in the absence of epistemic justification for belief, which in turn implies that understanding does not require justification in the traditional sense. The paper goes on to develop a new probabilistic model of acceptability, based on the idea that the maximally informative accounts of the understood phenomenon should be optimally probable. Interestingly, this probabilistic model ends up being similar in important ways to Elgin’s proposal to analyze the acceptability of such commitments in terms of ‘reflective equilibrium’. (shrink)

We argue that two long-term goals of AI research stand in tension with one another. The first involves creating AI that is safe, where this is understood as solving the problem of value alignment. The second involves creating artificial general intelligence, meaning AI that operates at or beyond human capacity across all or many intellectual domains. Our argument focuses on the human capacity to make what we call “existential choices”, choices that transform who we are as persons, including transforming what (...) we most deeply value or desire. It is a capacity for a kind of value misalignment, in that the values held prior to making such choices can be significantly different from (misaligned with) the values held after making them. Because of the connection to existentialist philosophers who highlight these choices, we call the resulting form of risk “existentialist risk.” It is, roughly, the risk that results from AI taking an active role in authoring its own values rather than passively going along with the values given to it. On our view, human-like intelligence requires a human-like capacity for value misalignment, which is in tension with the possibility of guaranteeing value alignment between AI and humans. (shrink)

Consequentialists often assume rational monism: the thesis that options are always made rationally permissible by the maximization of the selfsame quantity. This essay argues that consequentialists should reject rational monism and instead accept rational pluralism: the thesis that, on different occasions, options are made rationally permissible by the maximization of different quantities. The essay then develops a systematic form of rational pluralism which, unlike its rivals, is capable of handling both the Newcomb problems that challenge evidential decision theory and the (...) unstable problems that challenge causal decision theory. (shrink)

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of (...) rational action. Theories are specified as optimal program problems, defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches. (shrink)

Theories of rational decision making often abstract away from computational and other resource limitations faced by real agents. An alternative approach known as resource rationality puts such matters front and center, grounding choice and decision in the rational use of finite resources. Anticipated by earlier work in economics and in computer science, this approach has recently seen rapid development and application in the cognitive sciences. Here, the theory of rationality plays a dual role, both as a framework for normative assessment (...) and as a source of scientific hypotheses about how mental processes in fact work. The latter project, often called rational analysis, depends for its success on a fine-grained characterization of the computational problem facing a decision maker, which may in turn depend on realistic assumptions about what the relevant agent is like. As a consequence, resource rationality involves a delicate, but often fruitful interplay between the normative and the descriptive. (shrink)

A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make progress in this debate, I (...) argue that it is fruitful to focus not on specific experimental results but rather on what I call the ‘sources of epistemic irrationality’ in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: processing costs, evolutionary suboptimality, motivated cognition, and error management. 1 Introduction 2 The Bayesian Brain 3 The Problem of Epistemic Irrationality 3.1 Bayesian inference and rationality 3.2 Intuitive Bayesian inference 4 Sources of Epistemic Irrationality 4.1 Processing costs 4.2 Evolutionary suboptimality 4.3 Motivational influences 4.4 Error management 5 Conclusion. (shrink)

Inspired by, and in close relation with, the contributions of this special issue, Kuperberg elegantly links event comprehension, production, and learning. She proposes an overarching hierarchical generative framework of processing events enabling us to make sense of the world around us and to interact with it in a competent manner.

We propose the Selfish Goal model, which holds that a person's behavior is driven by psychological processes called goals that guide his or her behavior, at times in contradictory directions. Goals can operate both consciously and unconsciously, and when activated they can trigger downstream effects on a person's information processing and behavioral possibilities that promote only the attainment of goal end-states (and not necessarily the overall interests of the individual). Hence, goals influence a person as if the goals themselves were (...) selfish and interested only in their own completion. We argue that there is an evolutionary basis to believe that conscious goals evolved from unconscious and selfish forms of pursuit. This theoretical framework predicts the existence of unconscious goal processes capable of guiding behavior in the absence of conscious awareness and control (theautomaticity principle), the ability of the most motivating or active goal to constrain a person's information processing and behavior toward successful completion of that goal (thereconfiguration principle), structural similarities between conscious and unconscious goal pursuit (thesimilarity principle), and goal influences that produce apparent inconsistencies or counterintuitive behaviors in a person's behavior extended over time (theinconsistency principle). Thus, we argue that a person's behaviors are indirectly selected at the goal level but expressed (and comprehended) at the individual level. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?

This article proposes a cost-benefit analysis of argumentation, with the aim of highlighting the strategic considerations that govern the agent's decision to argue or not. In spite of its paramount importance, the topic of argumentative decision-making has not received substantial attention in argumentation theories so far. We offer an explanation for this lack of consideration and propose a tripartite taxonomy and detailed description of the strategic reasons considered by arguers in their decision-making: benefits, costs, and dangers. We insist that the (...) implications of acknowledging the strategic dimension of arguing are far-reaching, including promising insights on how to develop better argumentation technologies. (shrink)

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, the hierarchical PP (...) framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

Conflict can profoundly affect individuals and their groups. Oftentimes, conflict involves a clash between one side seeking change and increased gains through victory and the other side defending the status quo and protecting against loss and defeat. However, theory and empirical research largely neglected these conflicts between attackers and defenders, and the strategic, social, and psychological consequences of attack and defense remain poorly understood. To fill this void, we model (1) the clashing of attack and defense as games of strategy (...) and reveal that (2) attack benefits from mismatching its target's level of defense, whereas defense benefits from matching the attacker's competitiveness. This suggests that (3) attack recruits neuroendocrine pathways underlying behavioral activation and overconfidence, whereas defense invokes neural networks for behavioral inhibition, vigilant scanning, and hostile attributions; and that (4) people invest less in attack than defense, and attack often fails. Finally, we propose that (5) in intergroup conflict, out-group attack needs institutional arrangements that motivate and coordinate collective action, whereas in-group defense benefits from endogenously emerging in-group identification. We discuss how games of attack and defense may have shaped human capacities for prosociality and aggression, and how third parties can regulate such conflicts and reduce their waste. (shrink)

Individuals’ propensity not to override the first answer that comes to mind is thought to be a crucial cause behind many failures in reasoning. In the present study, we aimed to explore the strategies used and the abilities employed when individuals solve the cognitive reflection test, the most widely used measure of this tendency. Alongside individual differences measures, protocol analysis was employed to unfold the steps of the reasoning process in solving the CRT. This exploration revealed that there are several (...) ways people solve or fail the test. Importantly, 77% of the cases in which reasoners gave the correct final answer in our protocol analysis, they started their response with the correct answer or with a line of thought which led to the correct answer. We also found that 39% of the incorrect responders reflected on their first response. The findings indicate that the suppression of the first answer may not be the only crucial feature of reflectivity in the CRT and that the lack of relevant knowledge is a prominent cause of the reasoning errors. Additionally, we confirmed that the CRT is a multi-faceted construct: both numeracy and reflectivity account for performance. The results can help to better apprehend the “whys and whens” of the decision errors in heuristics and biases tasks and to further refine existing explanatory models. (shrink)

This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.

Decision-making typically requires judgments about causal relations: we need to know the causal effects of our actions and the causal relevance of various environmental factors. We investigate how several individuals' causal judgments can be aggregated into collective causal judgments. First, we consider the aggregation of causal judgments via the aggregation of probabilistic judgments, and identify the limitations of this approach. We then explore the possibility of aggregating causal judgments independently of probabilistic ones. Formally, we introduce the problem of causal-network aggregation. (...) Finally, we revisit the aggregation of probabilistic judgments when this is constrained by prior aggregation of qualitative causal judgments. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 467-491, July 2022.