This work is an initial step toward developing a cognitive theory of cyber deception. While widely studied, the psychology of deception has largely focused on physical cues of deception. Given that present‐day communication among humans is largely electronic, we focus on the cyber domain where physical cues are unavailable and for which there is less psychological research. To improve cyber defense, researchers have used signaling theory to extended algorithms developed for the optimal allocation of limited defense resources by using deceptive (...) signals to trick the human mind. However, the algorithms are designed to protect against adversaries that make perfectly rational decisions. In behavioral experiments using an abstract cybersecurity game (i.e., Insider Attack Game), we examined human decision‐making when paired against the defense algorithm. We developed an instance‐based learning (IBL) model of an attacker using the Adaptive Control of Thought‐Rational (ACT‐R) cognitive architecture to investigate how humans make decisions under deception in cyber‐attack scenarios. Our results show that the defense algorithm is more effective at reducing the probability of attack and protecting assets when using deceptive signaling, compared to no signaling, but is less effective than predicted against a perfectly rational adversary. Also, the IBL model replicates human attack decisions accurately. The IBL model shows how human decisions arise from experience, and how memory retrieval dynamics can give rise to cognitive biases, such as confirmation bias. The implications of these findings are discussed in the perspective of informing theories of deception and designing more effective signaling schemes that consider human bounded rationality. (shrink)

Much of what we believe we know, we know through the testimony of others. While there has been long-standing evidence that people are sensitive to the characteristics of the sources of testimony, for example in the context of persuasion, researchers have only recently begun to explore the wider implications of source reliability considerations for the nature of our beliefs. Likewise, much remains to be established concerning what factors influence source reliability. In this paper, we examine, both theoretically and empirically, the (...) implications of using message content as a cue to source reliability. We present a set of experiments examining the relationship between source information and message content in people's responses to simple communications. The results show that people spontaneously revise their beliefs in the reliability of the source on the basis of the expectedness of a source's claim and, conversely, adjust message impact by perceived reliability; hence source reliability and message content have a bi-directional relationship. The implications are discussed for a variety of psychological, philosophical and political issues such as belief polarization and dual-route models of persuasion. (shrink)

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

Richard Nisbett's The Geography of Thought is one of several recent works that have highlighted purported differences in thinking patterns between East Asians and Westerners on the basis of empirical research. This has implications for teaching and for other issues such as cultural integration. Based on a framework consisting of three distinct notions of rationality, this paper argues that some of the differences alleged by Nisbett are either not real or exaggerated, and that his geography of thought fails to provide (...) an adequate account of thinking styles across cultures. The paper concludes with a discussion of the implications for teaching and learning critical thinking that can be drawn from the framework developed. (shrink)

Cognitive science views thought as computation; and computation, by its very nature, can be understood in both rational and mechanistic terms. In rational terms, a computation solves some information processing problem (e.g., mapping sensory information into a description of the external world; parsing a sentence; selecting among a set of possible actions). In mechanistic terms, a computation corresponds to causal chain of events in a physical device (in engineering context, a silicon chip; in biological context, the nervous system). The discipline (...) is thus at the interface between two very different styles of explanation—as the papers in the current special issue well illustrate, it explores the interplay of rational and mechanistic forces. (shrink)

This paper develops a formal model of the subtle meaning differences that exist between grammatical alternatives in socially conditioned variation and how these variants can be used by speakers as resources for constructing personal linguistic styles. More specifically, this paper introduces a new formal system, called social meaning games, which allows for the unification of variationist sociolinguistics and game-theoretic pragmatics, two fields that have had very little interaction in the past. Although remarks have been made concerning the possible usefulness of (...) game-theoretic tools in the analysis of certain kinds of socially conditioned linguistic phenomena :645–668, 1977; Dror et al. in Lang Linguist Compass 7:561–579, 2013; in Lang Linguist Compass 8:230–242, 2014; Clark in Meaningful games: Exploring language with game theory, MIT Press, Cambridge, MA, 2014, among others), a general framework uniting game-theoretic pragmatics and quantitative sociolinguistics has yet to be developed. This paper constructs such a framework through giving a formalization of the Third Wave approach to the meaning of variation using signalling games and a probabilistic approach to speaker/listener beliefs of the kind commonly used in the Bayesian game-theoretic pragmatics framework :3–44, 2016, for recent overviews). (shrink)

Recently in these pages it has been argued that a relatively straightforward version of an old argument based on evolutionary biology and psychology can be employed to support the view that innate ideas are a naturalistic source of metaphysical knowledge. While sympathetic to the view that the “evolutionary argument” is pregnant with philosophical implications, I show in this paper how it needs to be developed and deployed in order to avoid serious philosophical difficulties and unnecessary complications. I sketch a revised (...) version of the evolutionary argument, place it in a new context, and show that this version in this context is not vulnerable to the standard criticisms levelled against arguments of this general type. The philosophical import of this version of the argument lies not in any metaphysical conclusions it sanctions directly, but in the support it lends to the metaphilosophy of commonsense. (shrink)

I provide an interpretation of Wittgenstein's much criticized remarks on Gödel's First Incompleteness Theorem in the light of paraconsistent arithmetics: in taking Gödel's proof as a paradoxical derivation, Wittgenstein was right, given his deliberate rejection of the standard distinction between theory and metatheory. The reasoning behind the proof of the truth of the Gödel sentence is then performed within the formal system itself, which turns out to be inconsistent. I show that the models of paraconsistent arithmetics (obtained via the Meyer-Mortensen (...) collapsing filter on the standard model) match with many intuitions underlying Wittgensteins philosophy of mathematics, such as its strict finitism and the insistence on the decidability of any meaningful mathematical question. (shrink)

This paper presents a position called Scheme-based Alethic Realism, which reconciles a realist position on the nature of truth with a pluralistic Kantian perspective that allows for multiple environments in which truthmaking relationships are established. We argue that truthmaking functions are constrained by a stable phenomenal world and a stable cognitive architecture. This account takes truth as normatively distinct from epistemic justification while relativizing the truth conditions of our statements to what we call Frameworks. The pluralistic aspect allows that these (...) stable elements, while constraining representational and linguistic schemes, do not define a single framework for truthmaking relations. We strengthen this position by considering themes on situated rational agency from cognitive science and artificial intelligence, arguing that whatever enables or supports rational action within a particular environment must figure into some account of truth and truthmaking, and vice versa. (shrink)

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we (...) illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality. (shrink)

We propose a critique ofnormativism, defined as the idea that human thinking reflects a normative system against which it should be measured and judged. We analyze the methodological problems associated with normativism, proposing that it invites the controversial “is-ought” inference, much contested in the philosophical literature. This problem is triggered when there are competing normative accounts (the arbitration problem), as empirical evidence can help arbitrate between descriptive theories, but not between normative systems. Drawing on linguistics as a model, we propose (...) that a clear distinction between normative systems and competence theories is essential, arguing that equating them invites an “is-ought” inference: to wit, supporting normative “ought” theories with empirical “is” evidence. We analyze in detail two research programmes with normativist features – Oaksford and Chater's rational analysis and Stanovich and West's individual differences approach – demonstrating how, in each case, equating norm and competence leads to an is-ought inference. Normativism triggers a host of research biases in the psychology of reasoning and decision making: focusing on untrained participants and novel problems, analyzing psychological processes in terms of their normative correlates, and neglecting philosophically significant paradigms when they do not supply clear standards for normative judgement. For example, in a dual-process framework, normativism can lead to a fallacious “ought-is” inference, in which normative responses are taken as diagnostic of analytic reasoning. We propose that little can be gained from normativism that cannot be achieved by descriptivist computational-level analysis, illustrating our position with Hypothetical Thinking Theory and the theory of the suppositional conditional. We conclude that descriptivism is a viable option, and that theories of higher mental processing would be better off freed from normative considerations. (shrink)

Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...) of intractability is solved by recourse to ‘as if’ explanations critically depends, inter alia, on the semantics of the ‘as if’ connective. We examine the five most sensible explications in the literature, and conclude that none of them circumvents the problem. As a result, rational ‘as if’ explanations must obey the minimal computational constraint of tractability. (shrink)

This paper studies the learnability of natural concepts in the context of the conceptual spaces framework. Previous work proposed that natural concepts are represented by the cells of optimally partitioned similarity spaces, where optimality was defined in terms of a number of constraints. Among these is the constraint that optimally partitioned similarity spaces result in easily learnable concepts. While there is evidence that systems of concepts generally regarded as natural satisfy a number of the proposed optimality constraints, the connection between (...) naturalness and learnability has been less well studied. To fill this gap, we conduct a computational study employing two standard models of concept learning. Applying these models to the learning of color concepts, we examine whether natural color concepts are more readily learned than nonnatural ones. Our findings warrant a positive answer to this question for both models employed, thus lending empirical support to the notion that learnability is a distinctive characteristic of natural concepts. (shrink)

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...) recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory. (shrink)

In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse. -/- In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the (...) syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module. (shrink)

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and (...) shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

This book is a defence of the philosophy of common sense in the spirit of Thomas Reid and G.E. Moore, drawing on the work of Aristotle, evolutionary biology and psychology, and historical studies on the origins of early modern philosophy. It defines and explores common sense beliefs, and defends them from challenges from prominent philosophers.

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (shrink)

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...) in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. (shrink)

On appelle « biais de confirmation » la tendance supposée des humains à sélectionner les informations qui vont dans le sens de ce qu’ils croient (ou veulent croire) et à interpréter celles dont ils disposent en faveur de leurs hypothèses favorites. Cet article vise à porter un regard critique sur certains usages de cette notion, et plus généralement sur le recours aux « biais cognitifs » pour expliquer tout un ensemble de phénomènes sociaux interprétés comme les marques d’une forme d’irrationalité. (...) Remettant en question le caractère probant des résultats expérimentaux destinés à mettre en évidence une déviation qui nous conduirait systématiquement à des représentations inexactes, je suggère que l’approche bayésienne en psychologie du raisonnement permet de rendre compte de certains de ces résultats en faisant l’hypothèse que nos inférences se conforment à une telle norme. (shrink)

In referential communication, Grice's Maxim of Quantity is thought to imply that utterances conveying unnecessary information should incur comprehension difficulties. There is, however, considerable evidence that speakers frequently encode redundant information in their referring expressions, raising the question as to whether such overspecifications hinder listeners’ processing. Evidence from previous work is inconclusive, and mostly comes from offline studies. In this article, we present two event‐related potential (ERP) experiments, investigating the real‐time comprehension of referring expressions that contain redundant adjectives in complex (...) visual contexts. Our findings provide support for both Gricean and bounded‐rational accounts. We argue that these seemingly incompatible results can be reconciled if common ground is taken into account. We propose a bounded‐rational account of overspecification, according to which even redundant words can be beneficial to comprehension to the extent that they facilitate the reduction of listeners’ uncertainty regarding the target referent. (shrink)

Oaksford & Chater (O&C) focus on patterns of typical adult reasoning from a probabilistic perspective. We discuss implications of extending the probabilistic approach to lifespan development, considering the role of working memory, strategy use, and expertise. Explaining variations in human reasoning poses a challenge to Bayesian rational analysis, as it requires integrating knowledge about cognitive processes.

Suppose that two competing norms, N1 and N2, can be identified such that a given person’s response can be interpreted as correct according to N1 but incorrect according to N2. Which of these two norms, if any, should one use to interpret such a response? In this paper we seek to address this fundamental problem by studying individual variation in the interpretation of conditionals by establishing individual profiles of the participants based on their case judgments and reflective attitudes. To investigate (...) the participants’ reflective attitudes we introduce a new experimental paradigm called the Scorekeeping Task. As a case study, we identify the participants who follow the Suppositional Theory of conditionals (N1) versus Inferentialism (N2) and investigate to what extent internally consistent competence models can be reconstructed for the participants on this basis. After extensive empirical investigations, an apparent reasoning error with and-to-if inferences was found in one of these two groups. The implications of this case study for debates on the proper role of normative considerations in psychology are discussed. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals result in (...) different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

Tetris provides a difficult, dynamic task environment within which some people are novices and others, after years of work and practice, become extreme experts. Here we study two core skills; namely, choosing the goal or objective function that will maximize performance and a feature-based analysis of the current game board to determine where to place the currently falling zoid so as to maximize the goal. In Study 1, we build cross-entropy reinforcement learning models to determine whether different goals result in (...) different feature weights. Two of these optimization strategies quickly rise to performance plateaus, whereas two others continue toward higher but more jagged heights. In Study 2, we compare the zoid placement decisions made by our best CERL models with those made by 67 human players. Across 370,131 human game episodes, two CERL models picked the same zoid placements as our lowest scoring human for 43% of the placements and as our three best scoring experts for 65% of the placements. Our findings suggest that people focus on maximizing points, not number of lines cleared or number of levels reached. They also show that goal choice influences the choice of zoid placements for CERLs and suggest that the same is true of humans. Tetris has a repetitive task structure that makes Tetris more tractable and more like a traditional experimental psychology paradigm than many more complex games or tasks. Hence, although complex, Tetris is not overwhelmingly complex and presents a right-sized challenge to cognitive theories, especially those of integrated cognitive systems. (shrink)

One of the most discussed issues in psychology—presently and in the past—is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join (...) forces and work toward a new foundation for the definition of rational cognition. We take a first step in this direction by proposing that the rationality of both cognitive and normative systems can be measured in terms of their cognitive success. Cognitive success can be defined and gauged in terms of two factors: ecological validity (the system's validity in conditions in which it is applicable) and the system's applicability (the scope of conditions under which it can be applied). As we show, prominent systems of reasoning—deductive reasoning, Bayesian reasoning, uncertain conditionals, and prediction and choice—perform rather differently on these two factors. Furthermore, we demonstrate that conceptualizing rationality according to its cognitive success offers a new perspective on the time-honored relationship between the descriptive (“is”) and the normative (“ought”) in psychology and philosophy. (shrink)

Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve a special, “independent” (...) or “autonomous” level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver :283–311, 2011) suggest. This view of computational explanations implies some degree of “autonomy” for the computational level. However, as we will demonstrate that does not make this view “computationally chauvinistic” in a way that Piccinini or Kaplan :339–373, 2011) have charged it to be. (shrink)

Cognitive science has converged in many ways with cognitive psychology, but while also maintaining a distinctive interdisciplinary nature. Here we further characterize this existing state of the field before proposing how it might be reconceptualized toward a broader and more distinct, and thus more stable, position in the realm of sciences.

A historical theory of rational norms claims that, if we are supposed to think rationally, this is because it is biologically normal for us to do so. The historical theorist is committed to the view that we are supposed to think rationally only if, in the past, adult humans sometimes thought rationally. I consider whether there is any plausible model of rational norms that can be adopted by the historical theorist that is compatible with the claim that adult human beings (...) are subject to rational norms, given certain plausible empirical assumptions about our history and capabilities. I suggest that there is one such model: this model centres on the idea that a procedure is rational if it has been endorsed (or at least not rejected) by mechanisms that have the function to ensure that the subject learns to reason in a way that approaches a certain kind of optimality. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 451-466, July 2022.

Psychologism in logic is the doctrine that the semantic content of logical terms is in some way a feature of human psychology. We consider the historically influential version of the doctrine, Psychological Individualism, and the many counter-arguments to it. We then propose and assess various modifications to the doctrine that might allow it to avoid the classical objections. We call these Psychological Descriptivism, Teleological Cognitive Architecture, and Ideal Cognizers. These characterizations give some order to the wide range of modern views (...) that are seen as psychologistic because of one or another feature. Although these can avoid some of the classic objections to psychologism, some still hold. (shrink)

A recent development in the cognitive science of reasoning has been the emergence of a probabilistic approach to the behaviour observed on ostensibly logical tasks. According to this approach the errors and biases documented on these tasks occur because people import their everyday uncertain reasoning strategies into the laboratory. Consequently participants' apparently irrational behaviour is the result of comparing it with an inappropriate logical standard. In this article, we contrast the probabilistic approach with other approaches to explaining rationality, and then (...) show how it has been applied to three main areas of logical reasoning: conditional inference, Wason's selection task and syllogistic reasoning. (shrink)

Human cognition requires coping with a complex and uncertain world. This suggests that dealing with uncertainty may be the central challenge for human reasoning. In Bayesian Rationality we argue that probability theory, the calculus of uncertainty, is the right framework in which to understand everyday reasoning. We also argue that probability theory explains behavior, even on experimental tasks that have been designed to probe people's logical reasoning abilities. Most commentators agree on the centrality of uncertainty; some suggest that there is (...) a residual role for logic in understanding reasoning; and others put forward alternative formalisms for uncertain reasoning, or raise specific technical, methodological, or empirical challenges. In responding to these points, we aim to clarify the scope and limits of probability and logic in cognitive science; explore the meaning of the explanation of cognition; and re-evaluate the empirical case for Bayesian rationality. (shrink)

Mere facts about how the world is cannot determine how we ought to think or behave. Elqayam & Evans (E&E) argue that this undercuts the use of rational analysis in explaining how people reason, by ourselves and with others. But this presumed application of the fallacy is itself fallacious. Rational analysis seeks to explain how people do reason, for example in laboratory experiments, not how they ought to reason. Thus, no ought is derived from an is; and rational analysis is (...) unchallenged by E&E's arguments. (shrink)

According to Aristotle, humans are the rational animal. The borderline between rationality and irrationality is fundamental to many aspects of human life including the law, mental health, and language interpretation. But what is it to be rational? One answer, deeply embedded in the Western intellectual tradition since ancient Greece, is that rationality concerns reasoning according to the rules of logic – the formal theory that specifies the inferential connections that hold with certainty between propositions. Piaget viewed logical reasoning as defining (...) the end-point of cognitive development; and contemporary psychology of reasoning has focussed on comparing human reasoning against logical standards. (shrink)

Knauff and Gazzo Castañeda (2022) object to using the term “new paradigm” to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term “paradigm” may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of (...) mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved. (shrink)

The incorporation of post-event testimonial information into an agent’s memory representation of the event via constructive memory processes gives rise to the misinformation effect, in which the incorporation of inaccurate testimonial information results in the formation of a false memory belief. While psychological research has focussed primarily on the incorporation of inaccurate information, the incorporation of accurate information raises a particularly interesting epistemological question: do the resulting memory beliefs qualify as knowledge? It is intuitively plausible that they do not, for (...) they appear to be only luckily true. I argue, however, that, despite its intuitive plausibility, this view is mistaken: once we adopt an adequate (modal) conception of epistemic luck and an adequate (adaptive) general approach to memory, it becomes clear that memory beliefs resulting from the incorporation of accurate testimonial information are not in general luckily true. I conclude by sketching some implications of this argument for the psychology of memory, suggesting that the misinformation effect would better be investigated in the context of a broader “information effect”. (shrink)

The default view in the epistemology of forgetting is that human memory would be epistemically better if we were not so susceptible to forgetting—that forgetting is in general a cognitive vice. In this paper, I argue for the opposed view: normal human forgetting—the pattern of forgetting characteristic of cognitively normal adult human beings—approximates a virtue located at the mean between the opposed cognitive vices of forgetting too much and remembering too much. I argue, first, that, for any finite cognizer, a (...) certain pattern of forgetting is necessary if her memory is to perform its function well. I argue, second, that, by eliminating clutter from her memory store, this pattern of forgetting improves the overall shape of the subject’s total doxastic state. I conclude by reviewing work in psychology which suggests that normal human forgetting approximates this virtuous pattern of forgetting. (shrink)

Clark and Chalmers claim that an external resource satisfying the following criteria counts as a memory: the agent has constant access to the resource; the information in the resource is directly available; retrieved information is automatically endorsed; information is stored as a consequence of past endorsement. Research on forgetting and metamemory shows that most of these criteria are not satisfied by biological memory, so they are inadequate. More psychologically realistic criteria generate a similar classification of standard putative external memories, but (...) the criteria still do not capture the function of memory. An adequate account of memory function, compatible with its evolution and its roles in prospection and imagination, suggests that external memory performs a function not performed by biological memory systems. External memory is thus not memory. This has implications for: extended mind theorizing, ecological validity of memory research, the causal theory of memory. (shrink)

In this paper I discuss philosophical and psychological treatments of the question "how do we decide that an occurrent mental state is a memory and not, say a thought or imagination?" This issue has proven notoriously difficult to resolve, with most proposed indices, criteria and heuristics failing to achieve consensus. Part of the difficulty, I argue, is that the indices and analytic solutions thus far offered seldom have been situated within a well-specified theory of memory function. As I hope to (...) show, when such an approach is adopted, not only does a new, functionally-grounded answer emerge; we also gain insight into the adaptive significance of the process proposed to underwrite our belief in the memorial status of a mental state (i.e.,autonoetic awareness). (shrink)