This is a critical notice of Christos Kyriacou and Kevin Wallbridge (eds.), Skeptical Invariantism Reconsidered. New York and London: Routledge, 2021. Pp. x + 324. ISBN 978-0-367-37018-3. It discusses in some detail contributions by Nevin Climenhaga, Christos Kyriacou, Michael Hannon, Kevin Wallbridge, Annalisa Coliva, and Genia Schönbaumsfeld.

This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it according to the needs emerging in different situational scenarios, is an important (...) aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer & Todd) due to their “bounded rationality” (Simon, 1957). In this perspective, one of the requirements that should be considered for the design, the realization and the evaluation of intelligent cognitively-inspired systems should be rep- resented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This requirement, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution. (shrink)

I argue that evidentialism should be rejected because it cannot be reconciled with empirical work on bias in cognitive and social psychology.

This paper presents a practical case study showing how, despite the nowadays limited collaboration between AI and Cognitive Science (CogSci), cognitive research can still have an important role in the development of novel AI technologies. After a brief historical introduction about the reasons of the divorce between AI and CogSci research agendas (happened in the mid’80s of the last century), we try to provide evidence of a renewed collaboration by showing a recent case study on a commonsense reasoning system, built (...) by using insights from cognitive semantics. (shrink)

Minimizing the costs that others impose upon oneself and upon those in whom one has a fitness stake, such as kin and allies, is a key adaptive problem for many organisms. Our ancestors regularly faced such adaptive problems (including homicide, bodily harm, theft, mate poaching, cuckoldry, reputational damage, sexual aggression, and the infliction of these costs on one's offspring, mates, coalition partners, or friends). One solution to this problem is to impose retaliatory costs on an aggressor so that the aggressor (...) and other observers will lower their estimates of the net benefits to be gained from exploiting the retaliator in the future. We posit that humans have an evolved cognitive system that implements this strategy – deterrence – which we conceptualize as a revenge system. The revenge system produces a second adaptive problem: losing downstream gains from the individual on whom retaliatory costs have been imposed. We posit, consequently, a subsidiary computational system designed to restore particular relationships after cost-imposing interactions by inhibiting revenge and motivating behaviors that signal benevolence for the harmdoer. The operation of these systems depends on estimating the risk of future exploitation by the harmdoer and the expected future value of the relationship with the harmdoer. We review empirical evidence regarding the operation of these systems, discuss the causes of cultural and individual differences in their outputs, and sketch their computational architecture. (shrink)

Kuhn wanted to install a new research agenda in philosophy of science. I argue that the tools are now available to better articulate his paradigm and let it guide philosophical research instead of itself remaining the object of philosophical debate.

I argue for the existence of a category of practical reasons which I call "Deliberation-Volatile Reasons" or "DVRs". DVRs have the distinguishing feature that their status as reasons for action is diminished when they are weighed in deliberation by the agent. I argue that DVRs are evidence of "deliberative blind spots". I submit that an agent manifests a peculiar kind of practical irrationality in so far as she endeavours to find a deliberative path to what she has reason to do, (...) when the discovery of such a path renders the destination inaccessible. (shrink)

Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using the Recognition Heuristic (...) and Take the Best heuristic, show how the model reconciles with the ecological rationality program, and discuss how our prescriptive, computational approach could be approximated by simpler mental rules that might be more descriptive. Echoing the arguments of van Rooij et al., we stress the virtue of having a computationally tractable model of strategy selection, even if one proposes that cognizers use a simpler heuristic process to approximate it. (shrink)

In the decision-making and rationality research field, rational decision theory (RDT) has always been the main framework, thanks to the elegance and complexity of its mathematical tools. Unfortunately, the formal refinement of the theory is not accompanied by a satisfying predictive accuracy, thus there is a big gap between what is predicted by the theory and the behaviour of real subjects. Here we propose a new foundation of the RDT, which has to be based on a cognitive architecture for reason-based (...) agents, acting on the basis of their beliefs in order to achieve their goals. The decision process is a cognitive evaluation of conflicting goals, based on different beliefs and values, but also on emotions and desires. We refer to a cognitive analysis of emotions and we integrate them in this more general RDT. (shrink)

We present a novel, sophisticated intention‐based control system for a mobile robot built from an extremely inexpensive webcam and radio‐controlled toy vehicle. The system visually observes humans participating in various playground games and infers their goals and intentions through analyzing their spatiotemporal activity in relation to itself and each other, and then builds a coherent narrative out of the succession of these intentional states. Starting from zero information about the room, the rules of the games, or even which vehicle it (...) controls, it learns rich relationships between players, their goals and intentions, probing uncertain situations with its own behavior. The robot is able to watch people playing various playground games, learn the roles and rules that apply to specific games, and participate in the play. The narratives it constructs capture essential information about the observed social roles and types of activity. After watching play for a short while, the system is able to participate appropriately in the games. We demonstrate how the system acts appropriately in scenarios such as chasing, follow‐the‐leader, and variants of tag. (shrink)

Classical cognitive science has been characterized by an association with the computational theory of mind. Although this association has produced highly significant results, it has also limited the scope of scientific psychology. In this paper, we analyse the limits of the specific kind of computational model represented by the Chomskian-Fodorian tradition in the study of mind and language. In our opinion, the adhesion to the principle of formality imposed by this specific computational model has motivated the exclusion of consciousness in (...) the reflection on language and, consequently, has led to the inability to account for some aspects of language functioning at the processing level of discourse. The aim of this article is to restore the role of consciousness in discourse comprehension and production processes. Specifically, we argue that the ability to produce and understand discourses is based on individuals’ capacity for navigation in space and time. We will show that the space–time orientation is guaranteed by the projection of the self, which involves a special kind of consciousness. (shrink)

Norms—that is, specifications of what we ought to do—play a critical role in the study of informal argumentation, as they do in studies of judgment, decision-making and reasoning more generally. Specifically, they guide a recurring theme: are people rational? Though rules and standards have been central to the study of reasoning, and behavior more generally, there has been little discussion within psychology about why (or indeed if) they should be considered normative despite the considerable philosophical literature that bears on this (...) topic. In the current paper, we ask what makes something a norm, with consideration both of norms in general and a specific example: norms for informal argumentation. We conclude that it is both possible and desirable to invoke norms for rational argument, and that a Bayesian approach provides solid normative principles with which to do so. (shrink)

Rational choice theory enjoys unprecedented popularity and influence in the behavioral and social sciences, but it generates intractable problems when applied to socially interactive decisions. In individual decisions, instrumental rationality is defined in terms of expected utility maximization. This becomes problematic in interactive decisions, when individuals have only partial control over the outcomes, because expected utility maximization is undefined in the absence of assumptions about how the other participants will behave. Game theory therefore incorporates not only rationality but also common (...) knowledge assumptions, enabling players to anticipate their co-players' strategies. Under these assumptions, disparate anomalies emerge. Instrumental rationality, conventionally interpreted, fails to explain intuitively obvious features of human interaction, yields predictions starkly at variance with experimental findings, and breaks down completely in certain cases. In particular, focal point selection in pure coordination games is inexplicable, though it is easily achieved in practice; the intuitively compelling payoff-dominance principle lacks rational justification; rationality in social dilemmas is self-defeating; a key solution concept for cooperative coalition games is frequently inapplicable; and rational choice in certain sequential games generates contradictions. In experiments, human players behave more cooperatively and receive higher payoffs than strict rationality would permit. Orthodox conceptions of rationality are evidently internally deficient and inadequate for explaining human interaction. Psychological game theory, based on nonstandard assumptions, is required to solve these problems, and some suggestions along these lines have already been put forward. Key Words: backward induction; Centipede game; common knowledge; cooperation; epistemic reasoning; game theory; payoff dominance; pure coordination game; rational choice theory; social dilemma. (shrink)

Although it makes some progress, McKay & Dennett's (M&D's) proposal is limited because (1) the argument for adaptive misbelief is not new, (2) arguments overextend the evidence provided, and (3) the alleged sufficient conditions are not as prohibitive as suggested. We offer alternative perspectives and evidence, including individual differences research, indicating that adaptive misbeliefs are likely much more widespread than implied.

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and De-centralized Cogition, Real-world Robotics and Artificial Life1. Such approaches, however, are often criticized for giving certain aspects of rationality too wide a berth. They focus their attention on on such superficially poor cousins as.

Guala contests the validity of strong reciprocity as a key element in shaping social behavior by contrasting evidence from experimental games to that of natural and historic data. He suggests that in order to understand the evolution of social behavior researchers should focus on natural data and weak reciprocity. We disagree with Guala's proposal to shift the focus of the study from one extreme of the spectrum (strong reciprocity) to the other extreme (weak reciprocity). We argue that the study of (...) the evolution of social behavior must be comparative in nature, and we point out experimental evidence that shows that social behavior is not cooperation determined by a set of fixed factors. We argue for a model that sees social behavior as a dynamic interaction of genetic and environmental factors. (shrink)

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then assess some proposed heuristic solutions to (...) the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...) how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today. (shrink)

This paper discusses problems associated with the use of optimality models in human behavioral ecology. Optimality models are used in both human and non-human animal behavioral ecology to test hypotheses about the conditions generating and maintaining behavioral strategies in populations via natural selection. The way optimality models are currently used in behavioral ecology faces significant problems, which are exacerbated by employing the so-called ‘phenotypic gambit’: that is, the bet that the psychological and inheritance mechanisms responsible for behavioral strategies will be (...) straightforward. I argue that each of several different possible ways we might interpret how optimality models are being used for humans face similar and additional problems. I suggest some ways in which human behavioral ecologists might adjust how they employ optimality models; in particular, I urge the abandonment of the phenotypic gambit in the human case. (shrink)

Naive physics beliefs can be systematically mistaken. They provide a useful test-bed because they are common, and also because their existence must rely on some adaptive advantage, within a given context. In the second part of the commentary we also ask questions about when a whole family of misbeliefs should be considered together as a single phenomenon.

According to higher-order thought accounts of phenomenal consciousness it is unlikely that many non-human animals undergo phenomenally conscious experiences. Many people believe that this result would have deep and far-reaching consequences. More specifically, they believe that the absence of phenomenal consciousness from the rest of the animal kingdom must mark a radical and theoretically significant divide between ourselves and other animals, with important implications for comparative psychology. I shall argue that this belief is mistaken. Since phenomenal consciousness might be almost (...) epiphenomenal in its functioning within human cognition, its absence in animals may signify only relatively trivial differences in cognitive architecture. Our temptation to think otherwise arises partly as a side-effect of imaginative identification with animal experiences, and partly from mistaken beliefs concerning the aspects of common-sense psychology that carry the main explanatory burden, whether applied to humans or to non-human animals. (shrink)

What kinds of evidence reliably support predictions of effectiveness for health and social care interventions? There is increasing reliance, not only for health care policy and practice but also for more general social and economic policy deliberation, on evidence that comes from studies whose basic logic is that of JS Mill's method of difference. These include randomized controlled trials, case–control studies, cohort studies, and some uses of causal Bayes nets and counterfactual-licensing models like ones commonly developed in econometrics. The topic (...) of this paper is the 'external validity' of causal conclusions from these kinds of studies. We shall argue two claims. Claim, negative: external validity is the wrong idea; claim, positive: 'capacities' are almost always the right idea, if there is a right idea to be had. If we are right about these claims, it makes big problems for policy decisions. Many advice guides for grading policy predictions give top grades to a proposed policy if it has two good Mill's-method-of difference studies that support it. But if capacities are to serve as the conduit for support from a method-of-difference study to an effectiveness prediction, much more evidence, and much different in kind, is required. We will illustrate the complexities involved with the case of multisystemic therapy, an internationally adopted intervention to try to diminish antisocial behaviour in young people. (shrink)

Wegner (Wegner, D. (2002). The illusion of conscious will. MIT Press) argues that conscious will is an illusion, citing a wide range of empirical evidence. I shall begin by surveying some of his arguments. Many are unsuccessful. But one—an argument from the ubiquity of self-interpretation—is more promising. Yet is suffers from an obvious lacuna, offered by so-called ‘dual process’ theories of reasoning and decision making (Evans, J., & Over, D. (1996). Rationality and reasoning. Psychology Press; Stanovich, K. (1999). Who is (...) rational? Studies of individual differences in reasoning. Lawrence Erlbaum; Frankish, K. (2004). Mind and supermind. Cambridge University Press). I shall argue that this lacuna can be filled by a plausible a priori claim about the causal role of anything deserving to be called ‘a will.’ The result is that there is no such thing as conscious willing: conscious will is, indeed, an illusion. (shrink)

This paper explores a variety of different versions of the thesis that natural language is involved in human thinking. It distinguishes amongst strong and weak forms of this thesis, dismissing some as implausibly strong and others as uninterestingly weak. Strong forms dismissed include the view that language is conceptually necessary for thought (endorsed by many philosophers) and the view that language is _de facto_ the medium of all human conceptual thinking (endorsed by many philosophers and social scientists). Weak forms include (...) the view that language is necessary for the acquisition of many human concepts, and the view that language can serve to scaffold human thought processes. The paper also discusses the thesis that language may be the medium of _conscious_ propositional thinking, but argues that this cannot be its most fundamental cognitive role. The idea is then proposed that natural language is the medium for non-domain-specific thinking, serving to integrate the outputs of a variety of domain-specific conceptual faculties (or central-cognitive ‘quasi-modules’). Recent experimental evidence in support of this idea is reviewed, and the implications of the idea are discussed, especially for our conception of the architecture of human cognition. Finally, some further kinds of evidence which might serve to corroborate or refute the hypothesis are mentioned. The overall goal of the paper is to review a wide variety of accounts of the cognitive function of natural language, integrating a number of different kinds of evidence and theoretical consideration in order to propose and elaborate the most plausible candidate. (shrink)

Empirical work on motivated reasoning suggests that our judgments are influenced to a surprising extent by our wants, desires and preferences (Kahan 2016; Lord, Ross, and Lepper 1979; Molden and Higgins 2012; Taber and Lodge 2006). How should we evaluate the epistemic status of beliefs formed through motivated reasoning? For example, are such beliefs epistemically justified? Are they candidates for knowledge? In liberal democracies, these questions are increasingly controversial as well as politically timely (Beebe et al. 2018; Lynch forthcoming, 2018; (...) Slothuus and de Vreese 2010). And yet, the epistemological significance of motivated reasoning has been almost entirely ignored by those working in mainstream epistemology. We aim to rectify this oversight. Using politically motivated reasoning as a case study, we show how motivated reasoning gives rise to three distinct kinds of skeptical challenges. We conclude by showing how the skeptical import of motivated reasoning has some important ramifications for how we should think about the demands of intellectual humility. (shrink)

This paper argues that two of my critics (Cowie and Wilson) have become fixated on Fodor’s notion of modularity, both to their own detriment and to the detriment of their understanding of Carruthers, 2006. The paper then focuses on the supposed inadequacies of the latter’s explanations of both content flexibility and human uniqueness, alleged by Machery and Cowie respectively.

Four different accounts of the relationship between third-person mindreading and first-person metacognition are compared and evaluated. While three of them endorse the existence of introspection for propositional attitudes, the fourth (defended here) claims that our knowledge of our own attitudes results from turning our mindreading capacities upon ourselves. Section 1 of this target article introduces the four accounts. Section 2 develops the “mindreading is prior” model in more detail, showing how it predicts introspection for perceptual and quasi-perceptual (e.g., imagistic) mental (...) events while claiming that metacognitive access to our own attitudes always results from swift unconscious self-interpretation. This section also considers the model's relationship to the expression of attitudes in speech. Section 3 argues that the commonsense belief in the existence of introspection should be given no weight. Section 4 argues briefly that data from childhood development are of no help in resolving this debate. Section 5 considers the evolutionary claims to which the different accounts are committed, and argues that the three introspective views make predictions that are not borne out by the data. Section 6 examines the extensive evidence that people often confabulate when self-attributing attitudes. Section 7 considers “two systems” accounts of human thinking and reasoning, arguing that although there are introspectableeventswithin System 2, there are no introspectableattitudes. Section 8 examines alleged evidence of “unsymbolized thinking”. Section 9 considers the claim that schizophrenia exhibits a dissociation between mindreading and metacognition. Finally, section 10 evaluates the claim that autism presents a dissociation in the opposite direction, of metacognition without mindreading. (shrink)

Wolfgang Spohn’s Frege prize lecture, like the work on which it is based, is a tour de force of rich, elegant, coherent argument about how the projected world that we experience is constructed. But we do not live in this projected world nor reason about it. The things Spohn constructs are there from the start—or so my Stanford School pragmatism teaches. This paper explores a deep difference in philosophical approaches—Spohn’s elegant proofs versus the stocky, tangled arguments I advocate—and illustrates how (...) these play out in far more detailed disputes about the nature of causality and causal inference. (shrink)

To explain agent-identification behaviours, universalist theories in the biological and cognitive sciences have posited mental mechanisms thought to be universal to all humans, such as agent detection and face recognition mechanisms. These universalist theories have paid little attention to how particular sociocultural or historical contexts interact with the psychobiological processes of agent-identification. In contrast to universalist theories, contextualist theories appeal to particular historical and sociocultural contexts for explaining agent-identification. Contextualist theories tend to adopt idiographic methods aimed at recording the heterogeneity (...) of human behaviours across history, space, and cultures. Defenders of the universalist approach tend to criticise idiographic methods because such methods can lead to relativism or may lack generality. To overcome explanatory limitations of proposals that adopt either universalist or contextualist approaches in isolation, I propose a philosophical model that integrates contributions from both traditions: the psycho-historical theory of agent-identification. This theory investigates how the tracking processes that humans use for identifying agents interact with the unique socio-historical contexts that support agent-identification practices. In integrating hypotheses about the history of agents with psychological and epistemological principles regarding agent-identification, the theory can generate novel hypotheses regarding the distinction between recognition-based, heuristic-based, and explanation-based agent-identification. (shrink)

Neuromoral theorists are those who claim that a scientific understanding of moral judgment through the methods of psychology, neuroscience and related disciplines can have normative implications and can be used to improve the human ability to make moral judgments. We consider three neuromoral theories: one suggested by Gazzaniga, one put forward by Gigerenzer, and one developed by Greene. By contrasting these theories we reveal some of the fundamental issues that neuromoral theories in general have to address. One important issue concerns (...) whether the normative claims that neuromoral theorists would like to make are to be understood in moral terms or in non-moral terms. We argue that, on either a moral or a non-moral interpretation of these claims, neuromoral theories face serious problems. Therefore, neither the moral nor the non-moral reading of the normative claims makes them philosophically viable. (shrink)

Our programmatic article on Homo heuristicus (Gigerenzer & Brighton, 2009) included a methodological section specifying three minimum criteria for testing heuristics: competitive tests, individual-level tests, and tests of adaptive selection of heuristics. Using Richter and Späth’s (2006) study on the recognition heuristic, we illustrated how violations of these criteria can lead to unsupported conclusions. In their comment, Hilbig and Richter conduct a reanalysis, but again without competitive testing. They neither test nor specify the compensatory model of inference they argue for. (...) Instead, they test whether participants use the recognition heuristic in an unrealistic 100% (or 96%) of cases, report that only some people exhibit this level of consistency, and conclude that most people would follow a compensatory strategy. We know of no model of judgment that predicts 96% correctly. The curious methodological practice of adopting an unrealistic measure of success to argue against a competing model, and to interpret such a finding as a triumph for a preferred but unspecified model, can only hinder progress. Marewski, Gaissmaier, Schooler, Goldstein, and Gigerenzer (2010), in contrast, specified five compensatory models, compared them with the recognition heuristic, and found that the recognition heuristic predicted inferences most accurately. (shrink)

Bayesian reasoning, defined here as the updating of a posterior probability following new information, has historically been problematic for humans. Classic psychology experiments have tested human Bayesian reasoning through the use of word problems and have evaluated each participant’s performance against the normatively correct answer provided by Bayes’ theorem. The standard finding is of generally poor performance. Over the past two decades, though, progress has been made on how to improve Bayesian reasoning. Most notably, research has demonstrated that the use (...) of frequencies in a natural sampling framework —as opposed to single-event probabilities—can improve participants’ Bayesian estimates. Furthermore, pictorial aids and certain individual difference factors also can play significant roles in Bayesian reasoning success. The mechanics of how to build tasks which show these improvements is not under much debate. The explanations for why naturally-sampled frequencies and pictures help Bayesian reasoning remain hotly contested, however, with many researchers falling into ingrained “camps” organized around two dominant theoretical perspectives. The present paper evaluates the merits of these theoretical perspectives, including the weight of empirical evidence, theoretical coherence, and predictive power. By these criteria, the ecological rationality approach is clearly better than the heuristics and biases view. Progress in the study of Bayesian reasoning will depend on continued research that honestly, vigorously, and consistently engages across these different theoretical accounts rather than staying “siloed” within one particular perspective. The process of science requires an understanding of competing points of view, with the ultimate goal being integration. (shrink)

The domain of “folk-economics” consists in explicit beliefs about the economy held by laypeople, untrained in economics, about such topics as, for example, the causes of the wealth of nations, the benefits or drawbacks of markets and international trade, the effects of regulation, the origins of inequality, the connection between work and wages, the economic consequences of immigration, or the possible causes of unemployment. These beliefs are crucial in forming people's political beliefs and in shaping their reception of different policies. (...) Yet, they often conflict with elementary principles of economic theory and are often described as the consequences of ignorance, irrationality, or specific biases. As we will argue, these past perspectives fail to predict the particular contents of popular folk-economic beliefs and, as a result, there is no systematic study of the cognitive factors involved in their emergence and cultural success. Here we propose that the cultural success of particular beliefs about the economy is predictable if we consider the influence of specialized, largely automatic inference systems that evolved as adaptations to ancestral human small-scale sociality. These systems, for which there is independent evidence, include free-rider detection, fairness-based partner choice, ownership intuitions, coalitional psychology, and more. Information about modern mass-market conditions activates these specific inference systems, resulting in particular intuitions, for example, that impersonal transactions are dangerous or that international trade is a zero-sum game. These intuitions in turn make specific policy proposals more likely than others to become intuitively compelling, and, as a consequence, exert a crucial influence on political choices. (shrink)

What makes beliefs thrive? In this paper, we model the dissemination of bona fide science versus pseudoscience, making use of Dan Sperber's epidemiological model of representations. Drawing on cognitive research on the roots of irrational beliefs and the institutional arrangement of science, we explain the dissemination of beliefs in terms of their salience to human cognition and their ability to adapt to specific cultural ecologies. By contrasting the cultural development of science and pseudoscience along a number of dimensions, we gain (...) a better understanding of their underlying epistemic differences. Pseudoscience can achieve widespread acceptance by tapping into evolved cognitive mechanisms, thus sacrificing intellectual integrity for intuitive appeal. Science, by contrast, defies those deeply held intuitions precisely because it is institutionally arranged to track objective patterns in the world, and the world does not care much about our intuitions. In light of these differences, we discuss the degree of openness or resilience to conceptual change (evidence and reason), and the divergent ways in which science and pseudoscience can achieve cultural “success”. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

Does conscious reflection lead to good decision-making? Whereas engaging in reflection is traditionally thought to be the best way to make wise choices, recent psychological evidence undermines the role of reflection in lay and expert judgement. The literature suggests that thinking about reasons does not improve the choices people make, and that experts do not engage in reflection, but base their judgements on intuition, often shaped by extensive previous experience. Can we square the traditional accounts of wisdom with the results (...) of these empirical studies? Should we even attempt to? I shall defend the view that philosophy and cognitive sciences genuinely interact in tackling questions such as whether reflection leads to making wise choices. (shrink)

In binary choice between discrete outcome lotteries, an individual may prefer lottery L1 to lottery L2 when the probability that L1 delivers a better outcome than L2 is higher than the probability that L2 delivers a better outcome than L1. Such a preference can be rationalized by three standard axioms (solvability, convexity and symmetry) and one less standard axiom (a fanning-in). A preference for the most probable winner can be represented by a skew-symmetric bilinear utility function. Such a utility function (...) has the structure of a regret theory when lottery outcomes are perceived as ordinal and the assumption of regret aversion is replaced with a preference for a win. The empirical evidence supporting the proposed system of axioms is discussed. (shrink)

A heuristic is a rule of thumb. In psychology, heuristics are relatively simple rules for making judgments. A fast heuristic is easy to use and allows one to make judgments quickly. A frugal heuristic relies on a small fraction of the available evidence in making judgments. Typically, fast and frugal heuristics (FFHs) have, or are claimed to have, a further property: They are very reliable, yielding judgments that are about as accurate in the long run as ideal non-fast, non-frugal rules. (...) This paper introduces some well-known examples of FFHs, raises some objections to the FFH program, and looks at the implications of those parts of the FFH program about which we can have some reasonable degree of confidence. (shrink)

Empirical adequacy matters directly - as it does for antirealists - if we aim to get all or most of the observable facts right, or indirectly - as it does for realists - as a symptom that the claims we make about the theoretical facts are right. But why should getting the facts - either theoretical or empirical - right be required of an acceptable theory? Here we endorse two other jobs that good theories are expected to do: helping us (...) with a) understanding and b) managing the world. Both are of equal, often greater, importance than getting a swathe of facts right, and empirical adequacy fares badly in both. It is not needed for doing these jobs and in many cases it gets in the way of doing them efficiently. (shrink)

Rationality in decision making is commonly assessed by comparing choice performance against normative standards. We argue that such a performance-centered approach blurs the distinction between rational choice and adaptive behavior. Instead, rational choice should be assessed with regard to the way individuals make analytic decisions. We suggest that analytic decisions can be made in two different modes in which control processes are directed at different levels. In a RUN mode, thought is directed at controlling the operation of a decision strategy. (...) In the JUMP mode, the individual analyses the interpretation of the decision situation as well as the appropriateness of alternative strategies. We suggest that a decision should be considered “rational” when an individual is able to switch between these modes and balance their interaction. (shrink)

This paper focuses on tacit versus explicit uses of plural performance metrics as a primary methodological characteristic. This characteristic usefully distinguishes two schools of normative analysis and their approaches to normative interpretations of bounded rationality. Both schools of thought make normative claims about bounded rationality by comparing the performance of decision procedures using more than one performance metric. The consistency school makes tacit reference to performance metrics outside its primary axiomatic framework, but lexicographically promotes internal axiomatic consistency as the primary, (...) and in most cases sufficient, normative outcome with which to undertake welfare comparisons. The consistency school's axiomatization program, in both neoclassical and behavioral forms, pre-commits to welfare interpretations that follow a hierarchy of rationalities based on the stringency of restrictions that different axiomatizations impose on choice data. In contrast, the ecological rational.. (shrink)

We investigate 1 factor that influences the success of ideas or cultural representations by proposing that they have a habitat, that is, a set of environmental cues that encourages people to recall and transmit them. We test 2 hypotheses: (a) fluctuation: the success of an idea will vary over time with fluctuations in its habitat, and (b) competition: ideas with more prevalent habitats will be more successful. Four studies use subject ratings and data from newspapers to provide correlational support for (...) our 2 hypotheses, with a negative factoid, positive rumor, catchphrases, and variants of a proverb. Three additional experimental studies manipulate the topic of actual conversations and find empirical support for our theory, with catchphrases, proverbs, and slang. The discussion examines how habitat prevalence applies to a more extensive class of ideas and suggests how habitats may influence the process by which ideas evolve. (shrink)

Given free information and unlimited processing power, should decision algorithms use as much information as possible? A formal model of the decision-making environment is developed to address this question and provide conditions under which informationally frugal algorithms, without any information or processing costs whatsoever, are optimal. One cause of compression that allows optimal algorithms to rationally ignore information is inverse movement of payoffs and probabilities (e.g., high payoffs occur with low probably and low payoffs occur with high probability). If inversely (...) related payoffs and probabilities cancel out, then predictors that correlate with payoffs and consequently condition the probabilities associated with different payoffs will drop out of the expected-payoff objective function, severing the link between information and optimal action rules. Stochastic payoff processes in which rational ignoring occurs are referred to as compressed environments, because optimal action depends on a reduced-dimension subset of the environmental parameters. This paper considers benefits and limitations of economic models versus other methods for studying links between environmental structure and the real-world success of simple decision procedures. Different methods converge on the normative proposition of ecological rationality, as opposed to axiomatic rationality based on informational efficiency and internal consistency axioms, as a superior framework for comparing the effectiveness of decision strategies and prescribing decision algorithms in application. (shrink)

The existence of superstition and religious beliefs in most, if not all, human societies is puzzling for behavioral ecology. These phenomena bring about various fitness costs ranging from burial objects to celibacy, and these costs are not outweighed by any obvious benefits. In an attempt to resolve this problem, we present a verbal model describing how humans and other organisms learn from the observation of coincidence (associative learning). As in statistical analysis, learning organisms need rules to distinguish between real patterns (...) and randomness. These rules, which we argue are equivalent to setting the level of α for rejection of the null hypothesis in statistics, are governed by risk management as well as by comparison to previous experiences. Risk management means that the cost of a possible type I error (superstition) has to be traded off against the cost of a possible type II error (ignorance). This trade-off implies that the occurrence of superstitious beliefs is an inevitable consequence of an organism’s ability to learn from observation of coincidence. Comparison with previous experiences (as in Bayesian statistics) improves the chances of making the right decision. While this Bayesian approach is found in most learning organisms, humans have evolved a unique ability to judge from experiences whether a candidate subject has the power to mechanistically cause the observed effect. Such “strong” causal thinking evolved because it allowed humans to understand and manipulate their environment. Strong causal thinking, however, involves the generation of hypotheses about underlying mechanisms (i.e., beliefs). Assuming that natural selection has favored individuals that learn quicker and more successfully than others owing to (1) active search to detect patterns and (2) the desire to explain these patterns mechanistically, we suggest that superstition has evolved as a by-product of the first, and that belief has evolved as a by-product of the second. (shrink)

People’s beliefs about normality play an important role in many aspects of cognition and life (e.g., causal cognition, linguistic semantics, cooperative behavior). But how do people determine what sorts of things are normal in the first place? Past research has studied both people’s representations of statistical norms (e.g., the average) and their representations of prescriptive norms (e.g., the ideal). Four studies suggest that people’s notion of normality incorporates both of these types of norms. In particular, people’s representations of what is (...) normal were found to be influenced both by what they believed to be descriptively average and by what they believed to be prescriptively ideal. This is shown across three domains: people’s use of the word ‘‘normal” (Study 1), their use of gradable adjectives (Study 2), and their judgments of concept prototypicality (Study 3). A final study investigated the learning of normality for a novel category, showing that people actively combine statistical and prescriptive information they have learned into an undifferentiated notion of what is normal (Study 4). Taken together, these findings may help to explain how moral norms impact the acquisition of normality and, conversely, how normality impacts the acquisition of moral norms. (shrink)