The past 40 years have seen an enormous amount of research aimed at investigating human reasoning and decision-making abilities. This research has led to an extended debate about the extent to which humans meet the standards of normative theories of rationality. Recently, it has been proposed that dual-process theories, which posit that there are two distinct types of cognitive systems, offer a way to resolve this debate over human rationality. I will propose that the two systems of dual-process theories are (...) best understood as investigative kinds. I will then examine recent empirical research from the cognitive neuroscience of decision-making that lends empirical support to the theoretical claims of dual-process theorists. I will lastly argue that dual-process theories not only offer an explanation for much of the conflicting data seen in decision-making and reasoning research, but that they ultimately offer reason to be optimistic about the prospects of human rationality. (shrink)

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as “intuitive statisticians” while we argue for a more generative conception of humans as “probing organisms.” We first highlight how ecological rationality’s focus on cues and statistics is problematic for two reasons: the problem of cue salience, and the problem of cue uncertainty. We highlight these problems by revisiting the statistical and cue-based logic that underlies (...) ecological rationality, which originate from the misapplication of concepts in psychophysics. We then work through the most popular experimental task in the ecological rationality literature—the city size task—to illustrate how psychophysical assumptions have informally been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology—in contrast to ecological rationality’s focus on statistics. We argue that in uncertain environments cues are rarely given or available for statistical processing. Therefore we focus on the psychogenesis of awareness rather than psychophysics of cues. For any agent or organism, environments “teem” with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and the organism-directed probing that shapes awareness and perception. Cues in teeming environments are noticed when they serve as cues-for-something, requiring what might be called a “cue-to-clue” transformation. In this sense, awareness toward a cue or cues is actively “grown.” We thus argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. This generative approach not only applies to relatively mundane organism interactions with their environments—as well as organism-object relationships and their embodied nature—but also has significant implications for understanding the emergence of novelty in economic settings. We conclude with a discussion of how our arguments link with—but modify—Herbert Simon’s popular “scissors” metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments. (shrink)

This work examines the influence of reference dependence, including value selection bias and congruence effects, on diagnostic reasoning. Across two studies, we explored how dependence on the initial problem structure influences the ability to solve simplified precursors to the more traditional Bayesian reasoning problems. Analyses evaluated accuracy and types of response errors as a function of congruence between the problem presentation and question of interest, amount of information, need for computation, and individual differences in numerical abilities. Across all problem variations, (...) there was consistent and strong evidence of a value selection bias in that incorrect responses almost always conformed to values that were provided in the problem rather than other errors including those related to computation. The most consistent and unexpected error across all conditions in the first experiment was that people were often more likely to utilize the superordinate value as part of their solution rather than the anticipated reference class values. This resulted in a weakened effect of congruence, with relatively low accuracy even in congruent conditions, and a dominant response error of the superordinate value. Experiment 2 confirmed that the introduction of a new sample drew attention away from the provided reference class, increasing reliance on the overall sample size. This superordinate preference error, along with the benefit of repeating the PPV reference class within the question, demonstrated the importance of reference dependence based on the salience of information within the response prompt. Throughout, higher numerical skills were generally associated with higher accuracy, whether calculations were required or not. (shrink)

According to the Rationality Constraint, our concept of belief imposes limits on how much irrationality is compatible with having beliefs at all. We argue that empirical evidence of human irrationality from the psychology of reasoning and the psychopathology of delusion undermines only the most demanding versions of the Rationality Constraint, which require perfect rationality as a condition for having beliefs. The empirical evidence poses no threat to more relaxed versions of the Rationality Constraint, which only require only minimal rationality. Nevertheless, (...) we raise problems for all versions of the Rationality Constraint by appealing to more extreme forms of irrationality that are continuous with actual cases of human irrationality. In particular, we argue that there are conceivable cases of “mad belief” in which populations of Lewisian madmen have beliefs that are not even minimally rational. This undermines Lewis’s claim that our ordinary concept of belief is a theoretical concept that is implicitly defined by its role in folk psychology. We argue that introspection gives us a phenomenal concept of belief that cannot be analyzed by applying Lewis’s semantics for theoretical terms. (shrink)

Much of the time, human beings seem to rely on habits. Habits are learned behaviours directly elicited by context cues, and insensitive to short-term changes in goals: therefore they are sometimes irrational. But even where habitual responses are rational, it can seem as if they are nevertheless not done for reasons. For, on a common understanding of habitual behaviour, agents’ intentions do not play any role in the coming about of such responses. This paper discusses under what conditions we can (...) say that habitual responses are, after all, done for reasons. We show how the idea that habitual behaviour cannot be understood as ‘acting for reasons’ stems from a widely but often implicitly held theoretical framework: the causal theory of action. We then propose an alternative, Anscombean understanding of intentional action, which can account for habitual responses being done for reasons. (shrink)

Current psychology of human reasoning is divided into several different approaches. For instance, there is a major dispute over the question whether human beings are able to apply norms of the formal models of rationality such as rules of logic, or probability and decision theory, correctly. While researchers following the “heuristics and biases” approach argue that we deviate systematically from these norms, and so are perhaps deeply irrational, defenders of the “bounded rationality” approach think not only that the evidence for (...) this conclusion is problematic but also that we should not, at least not very often, use formal norms in reasoning. I argue that while the evidence for heuristics and biases is indeed questionable, the bounded rationality approach has its limits too. Most especially, we should not infer that formal norms play no role in a comprehensive theory of rationality. Instead, formal and bounded rules of reasoning might even be connected in a more comprehensive theory of rationality. (shrink)

Recent work in cognitive neuroscience on the child's Theory of Mind (ToM) has pursued the idea that the ability to metarepresent mental states depends on a domain-specific cognitive subystem implemented in specific neural circuitry: a Theory of Mind Module. We argue that the interaction of several domain-general mechanisms and lower-level domain-specific mechanisms accounts for the flexibility and sophistication of behavior, which has been taken to be evidence for a domain-specific ToM module. This finding is of more general interest since it (...) suggests a parsimonious cognitive architecture can account for apparent domain specificity. We argue for such an architecture in two stages. First, on conceptual grounds, contrasting the case of language with ToM, and second, by showing that recent evidence in the form of fMRI and lesion studies supports the more parsimonious hypothesis. 1. Theory of Mind, Metarepresentation, and Modularity2. Developmental Components of ToM3. The Analogy with Modularity of Language4. Dissociations without Modules5. The Evidence from Neuroscience6. Conclusion. (shrink)

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world—the actual “statistics of the environment.” But in fact the idea of a “true” prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional Bayesian (...) epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that “ground truth” probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be “tuned” to its environment. (shrink)

This book explores a question central to philosophy--namely, what does it take for a belief to be justified or rational? According to a widespread view, whether one has justification for believing a proposition is determined by how probable that proposition is, given one's evidence. In this book this view is rejected and replaced with another: in order for one to have justification for believing a proposition, one's evidence must normically support it--roughly, one's evidence must make the falsity of that proposition (...) abnormal in the sense of calling for special, independent explanation. This conception of justification bears upon a range of topics in epistemology and beyond. Ultimately, this way of looking at justification guides us to a new, unfamiliar picture of how we should respond to our evidence and manage our own fallibility. This picture is developed here. (shrink)

This paper discusses the ecological case for epistemic innocence: does biased cognition have evolutionary benefits, and if so, does that exculpate human reasoners from irrationality? Proponents of ‘ecological rationality’ have challenged the bleak view of human reasoning emerging from research on biases and fallacies. If we approach the human mind as an adaptive toolbox, tailored to the structure of the environment, many alleged biases and fallacies turn out to be artefacts of narrow norms and artificial set-ups. However, we argue that (...) putative demonstrations of ecological rationality involve subtle locus shifts in attributions of rationality, conflating the adaptive rationale of heuristics with our own epistemic credentials. By contrast, other cases also involve an ecological reframing of human reason, but do not involve such problematic locus shifts. We discuss the difference between these cases, bringing clarity to the rationality debate. (shrink)

I investigate whether degreed beliefs are able to play the predictive, explanatory, and modeling roles that they are frequently taken to play. The investigation focuses on evidence—both from sources familiar in epistemology as well as recent work in behavioral economics and cognitive psychology—of variability in agents' apparent degrees of belief. Although such variability has been noticed before, there has been little philosophical discussion of its breadth or of the psychological mechanisms underlying it. Once these are appreciated, the inadequacy of degrees (...) of belief becomes clear. I offer a theoretical alternative to degrees of belief, what I call the filter theory. (shrink)

Behavioral economics focuses mainly on how limitations of the human cognitive apparatus, risk attitudes, and human sociality affect decision making. The former two lead to deviations from rationality standards, the latter to deviations from rational self-interest. Some of these research interests are also shared by evolutionary psychology which, however, explains the observed deviations by features of the human genetic endowment conjectured to have evolved under fierce selection pressure in early human phylogeny. Important as the decision-making theoretical perspective of the two (...) disciplines is, it will be claimed to be too narrow to fully account for what an evolutionary approach has to say about economic behavior. This is particularly true regarding the questions of what choices are made and what causes them, i.e., the motivational aspects of behavior. In the language of economics this points to the agents’ preferences. This article discusses how preferences relate to the human genetic endowment, how they can change over time, and what conclusions have to be drawn from extending the analysis to the motivational side. (shrink)

Normative theories can be useful in developing descriptive theories, as when normative subjective expected utility theory is used to develop descriptive rational choice theory and behavioral game theory. “Ought” questions are also the essence of theories of moral reasoning, a domain of higher mental processing that could not survive without normative considerations.

In his early experimental work with Suppes, Davidson adopted rationality assumptions, not as necessary constraints on interpretation, but as practical conceits in addressing methodological problems faced by experimenters studying decision making under uncertainty. Although the content of their theory has since been undermined, their methodological approach—a Galilean form of methodological rationalism—lives on in contemporary psychological research. This article draws on Max Weber’s verstehen to articulate an account of Galilean methodological rationalism; explains how anomalies faced by Davidson’s early experimental work gave (...) rise to his later, canonical claims about rationality and interpretation; and reclaims this Galilean framework for use in contemporary psychological research. (shrink)

In his debates with Daniel Kahneman and Amos Tversky, Gerd Gigerenzer puts forward a stricter standard for the proper representation of judgment heuristics. I argue that Gigerenzer’s stricter standard contributes to naturalized epistemology in two ways. First, Gigerenzer’s standard can be used to winnow away cognitive processes that are inappropriately characterized and should not be used in the epistemic evaluation of belief. Second, Gigerenzer’s critique helps to recast the generality problem in naturalized epistemology and cognitive psychology as the methodological problem (...) of identifying criteria for the appropriate specification and characterization of cognitive processes in psychological explanations. I conclude that naturalized epistemologists seeking to address the generality problem should turn their focus to methodological questions about the proper characterization of cognitive processes for the purposes of psychological explanation. (shrink)

Severity of Test (SoT) is an alternative to Popper's logical falsification that solves a number of problems of the logical view. It was presented by Popper himself in 1963. SoT is a less sophisticated probabilistic model of hypothesis testing than Oaksford & Chater's (O&C's) information gain model, but it has a number of striking similarities. Moreover, it captures the intuition of everyday hypothesis testing.

Homo sapiens have evolved a dual-process cognitive architecture that is adaptive but prone to systematic errors. Fuzzy-trace theory predicts that nested or overlapping class-inclusion relations create processing interference, resulting in denominator neglect: behaving as if one ignores marginal denominators in a 2 × 2 table. Ignoring marginal denominators leads to fallacies in base-rate problems and conjunctive and disjunctive probability estimates.

Abstract:The effectiveness of heuristics has received contradicting interpretations in the behavioural sciences. We study the policy implications of two programmes that dispute the effectiveness of heuristics – the biases and heuristics and the fast and frugal heuristics programmes. While the first blames heuristics for most errors in judgement, the second posits heuristics as simple mental algorithms that work well in a range of environments. We argue that the fast and frugal programme is less paternalistic insofar as it models humans as (...) effective decision-makers in a range of environments. However, in the rapidly changing environments of the 21st century, both are needed to inform evidence-based policies. (shrink)

The results generated by experimentalists in psychology and economics haveled to numerous advances in the study of human decision making under risk.Camerer (1995) and Rabin (1998) provide excellent reviews of the relevantliterature. These results clearly display the gap between normative theoriesof ideal behavior and descriptive theories of observed behavior. The mostprominent result is loss aversion – the observation that a loss is given greatervalue than a gain of an equal size – and the resulting S-shaped utility function.Rabin puts the key (...) point as follows: ``Researchers have identified a pervasive feature of reference dependence: In a wide variety of domains, people are significantly more averse to losses than they are attracted to same size gains''(Rabin 1998, 13–14). In what follows, I will show that the ``wide variety of domains'' is wide indeed. In particular, I will review the effects of the S-shaped utility function on the resolution of the decision to commit a crime, to decision to violate professional ethics, and the decision to participate in a rebellion. (shrink)

In recent years evolutionary psychologists have developed and defended the Massive Modularity Hypothesis, which maintains that our cognitive architecture—including the part that subserves ‘central processing’ —is largely or perhaps even entirely composed of innate, domain-specific computational mechanisms or ‘modules’. In this paper I argue for two claims. First, I show that the two main arguments that evolutionary psychologists have offered for this general architectural thesis fail to provide us with any reason to prefer it to a competing picture of the (...) mind which I call the Library Model of Cognition. Second, I argue that this alternative model is compatible with the central theoretical and methodological commitments of evolutionary psychology. Thus I argue that, at present, the endorsement of the Massive Modularity Hypothesis by evolutionary psychologists is both unwarranted and unmotivated. (shrink)

Samuels and Stich explore the debate over the extent to which ordinary human reasoning and decision making is rational. One prominent cluster of views, often associated with the heuristics and biases tradition in psychology, maintains that human reasoning is, in important respects, normatively problematic or irrational. Samuels and Stich start by sketching some key experimental findings from this tradition and describe a range of pessimistic claims about the rationality of ordinary people that these and related findings are sometimes taken to (...) support. Such pessimistic interpretations of the experimental findings have not gone unchallenged however: Samuels and Stich outline some of the research on reasoning that has been done by evolutionary psychologists and sketch a cluster of more optimistic theses about ordinary reasoning that such psychologists defend. Although Samuels and Stich think that the most dire pronouncements made by writers in the heuristics and biases tradition are unwarranted, they also maintain that the situation is rather more pessimistic than sometimes suggested by evolutionary psychologists. They conclude by defending this “middle way” and sketch a family of “dual processing” theories of reasoning which, they argue, offer some support for the moderate interpretation they advocate. (shrink)

The framework of elementary probabilistic operations (EPO) explains the structure of elementary probabilistic reasoning tasks as well as people’s performance on these tasks. The framework comprises three components: (a) Three types of probabilities: joint, marginal, and conditional probabilities; (b) three elementary probabilistic operations: combination, marginalization, and conditioning, and (c) quantitative inference schemas implementing the EPO. The formal part of the EPO framework is a computational level theory that provides a problem space representation and a classification of elementary probabilistic problems based (...) on computational requirements for solving a problem. According to the EPO framework, current methods for improving probabilistic reasoning are of two kinds: First, reduction of Bayesian problems to a type of probabilistic problems requiring less conceptual and procedural competencies. Second, enhancing people’s utilization competence by fostering the application of quantitative inference schemas. The approach suggests new applications, including the teaching of probabilistic reasoning, using analogical problem solving in probabilistic reasoning, and new methods for analyzing errors in probabilistic problem solving. (shrink)

A diagnostic judgment of a teacher can be seen as an inference from manifest observable evidence on a student’s behavior to his or her latent traits. This can be described by a Bayesian model of in-ference: The teacher starts from a set of assumptions on the student (hypotheses), with subjective probabilities for each hypothesis (priors). Subsequently, he or she uses observed evidence (stu-dents’ responses to tasks) and knowledge on conditional probabilities of this evidence (likelihoods) to revise these assumptions. Many systematic (...) deviations from this model (biases, e.g. base-rate neglect, inverse fallacy) are reported in literature on Bayesian reasoning. In a teacher’s situation the information (hypotheses, priors, likelihoods) is usually not explicitly represented numerically (as in most research on Bayesian reasoning), but only by qualitative esti-mations in the mind of the teacher. In our study, we ask to which extent individuals (approximately) apply a rational Bayesian strategy or resort on other biased strategies of processing information for their diagnostic judgments. We explicitly pose this question with respect to non-numerical settings. To investigate this question, we developed a scenario that visually displays all relevant information (hypotheses, priors, likelihoods) in a graphically displayed hypothesis space (called “hypothegon”) – without recurring to numerical representations or mathematical procedures. 42 pre-service teach-ers were asked to judge the plausibility of different misconceptions of six students based on their responses to decimal comparison tasks (e.g. 3.39 > 3.4). Applying a Bayesian classification proce-dure, we identified three updating strategies: A Bayesian update strategy (BUS, processing all probabilities), a combined evidence strategy (CES, ignoring the prior probabilities but including all likelihoods), and a single evidence strategy (SES, only using the likelihood of the most probable hypothesis). In study 1, an instruction on the relevance of using all probabilities (priors and likelihoods) only weakly increased the processing of more information. In study 2, we found strong evidence that a visual explication of the prior-likelihood interaction led to an increase of processing the interaction of all relevant information. These results show that the phenomena found in general research on Bayesian reasoning in numerical settings extend to diagnostic judgments in non-numerical settings. (shrink)

In teaching statistics in secondary schools and at university, two visualizations are primarily used when situations with two dichotomous characteristics are represented: 2×2 tables and tree diagrams. Both visualizations can be depicted either with probabilities or with frequencies. Visualizations with frequencies have been shown to help students significantly more in Bayesian reasoning problems than probability visualizations do. Because tree diagrams or double-trees (which are largely unknown in school) are node-branch-structures, these two visualizations (compared to the 2×2 table) can even simultaneously (...) display probabilities on branches and frequencies inside the nodes. This is a teaching advantage as it allows the frequency concept to be used to better understand probabilities. However, 2×2 tables and (double-) trees have a decisive disadvantage: While joint probabilities (e.g., P(AB)) are represented in 2×2 tables but no conditional probabilities (e.g., P(A|B)), it is exactly the other way around with (double-) trees. Therefore, a visualization that is equally suitable for the representation of joint probabilities and conditional probabilities is desirable. In this article, we present a new visualization—the frequency net—in which all absolute frequencies and all types of probabilities can be depicted. In addition to a detailed theoretical analysis of the frequency net, we report the results of a study with 249 university students that shows that “net diagrams” can improve reasoning without previous instruction to a similar extent as 2×2 tables and double-trees. Regarding questions about conditional probabilities, frequency visualizations (2×2 table, double-tree, or net diagram with natural frequencies) are consistently superior to probability visualizations. The frequency net performs as well as the frequency double-tree. Only the 2×2 table with frequencies—the only visualization that participants were already familiar with —led to higher performance rates. If, on the other hand, a question about a joint probability had to be answered, all implemented visualizations clearly supported participants’ performance, and no uniform format effect becomes visible. Participants reached the highest performance in the versions with probability 2×2 tables and probability net diagrams. Furthermore, after conducting a detailed error analysis, we report interesting error shifts between the two information formats and the different visualizations and give recommendations for teaching probability. (shrink)

An experiment was conducted to test the effectiveness of brief instruction in information structuring (i.e., representing and integrating information) for improving the coherence of probability judgments and binary choices among intelligence analysts. Forty-three analysts were presented with comparable sets of Bayesian judgment problems before and immediately after instruction. After instruction, analysts’ probability judgments were more coherent (i.e., more additive and compliant with Bayes theorem). Instruction also improved the coherence of binary choices regarding category membership: after instruction, subjects were more likely (...) to invariably choose the category to which they assigned the higher probability of a target’s membership. The research provides a rare example of evidence-based validation of effectiveness in instruction to improve the statistical assessment skills of intelligence analysts. Such instruction could also be used to improve the assessment quality of other types of experts who are required to integrate statistical information or make probabilistic assessments. (shrink)

Current explanations for anchoring phenomena seem to be unable to account for the diversity of effects found by 40 years of research. Additionally, the theories do not have much to say about the processes that make anchors so resilient to modification. I argue that by focusing on the mechanisms involved in spatial representation, we can account for most anchoring effects which have spatial components.

The Monty Hall dilemma is a notorious probability problem with a counterintuitive solution. There is a strong tendency to stay with the initial choice, despite the fact that switching doubles the probability of winning. The current randomised experiment investigates whether feedback in a series of trials improves behavioural performance on the MHD and increases the level of understanding of the problem. Feedback was either conditional or non-conditional, and was given either in frequency format or in percentage format. Results show that (...) people learn to switch most when receiving conditional feedback in frequency format. However, problem understanding does not improve as a consequence of receiving feedback. Our study confirms the dissociation between behavioural performance on the MHD, on one hand, and actual understanding of the MHD, on the other. We discuss how this dissociation can be understood. (shrink)

Some evolutionary psychologists (EP) are strong nativists about Theory of Mind (TOM). They argue that the development of specialised cognitive competence required for TOM requires genetic specification of the developmental trajectory of a specialised cognitive system. EP arrives at this conclusion via conceptual arguments concerning the inadequacy of blank slate neuroconstructivism (strong neuroconstructivism) and empirical evidence from developmental and neuropsychology. I argue that the correct understanding of the conceptual argument applied to the case of TOM supports a moderate form of (...) neuroconstructivism which does not itself require genetic prespecification. In this respect there is no useful analogy with arguments which support stron nativism about linguistic competence. Understanding this debate has consequences for our understanding of modularity, autism, developmental disorders and nativism about high level cognition. (shrink)

Over the past few decades, reasoning and rationality have been the focus of enormous interdisciplinary attention, attracting interest from philosophers, psychologists, economists, statisticians and anthropologists, among others. The widespread interest in the topic reflects the central status of reasoning in human affairs. But it also suggests that there are many different though related projects and tasks which need to be addressed if we are to attain a comprehensive understanding of reasoning.

Cognitive modules are internal mental structures. Some theorists and empirical researchers hypothesize that the human mind is either partially or massively comprised of structures that are modular in nature. Modules are also invoked to explain cognitive capacities associated with the performance of specific functional tasks. Jerry Fodor (1983) considered that modules are useful only for explaining relatively low-level systems (input systems). These are the systems involved in capacities like perception and language. For Fodor, the central (high-level) systems of mind — (...) those involved in capacities like judgment (the “fixation of belief” in Fodor’s jargon), planning (practical reasoning), decision-making, and so forth — are not explainable in terms of modular mechanisms. However, some other philosophers as well as proponents and practitioners of evolutionary psychology consider that it is sensible — and even necessary — to invoke modules for explaining these systems, too. Indeed, the debate over modularity is mainly a debate over the modularity of central systems (high-level cognitive capacities). Admittedly, it is also possible to raise doubts about the modularity of peripheral systems (Prinz, 2006), but this kind of skepticism is not widespread in the literature. In contrast, both the empirical and the theoretical (a priori) cases in favor of central modularity are usually contested on multiple fronts and in tough terms. The case for the modularity of central systems is the core of the case for the massive modularity of mind hypothesis (viz. the hypothesis that consists in asserting that both the peripheral and the central systems of human mind are largely composed of modules). Granted, the massive modularity of mind hypothesis is an empirical statement and, as such, its truth should be ultimately decided in an empirical way, not a priori (Sperber, 1994, 2001). Nonetheless, much of the debate over massive modularity has taken place on theoretical grounds. This is due to an alleged underdetermination of the hypotheses regarding the modularity of central systems by data. In such conditions, the remaining open option has been to advance theoretical considerations in favor of the plausibility — and not directly in favor of the empirical truth or truthlikeness — of the massive modularity of mind generally, and of the modularity of some central systems in particular. These theoretical considerations are mostly based on an adaptationist view of evolution cum a classical computationalist approach to mind. They include arguments about the nature and evolution of hierarchically ordered complex systems (the evolvability of complexity argument), a presumption of optimality expressed in the apparent design of phenotypic traits shaped by natural selection (the task-specificity argument) and dismissing nonmodular mental architectures as computationally intractable (the tractability argument). Is the massive modularity of mind hypothesis a cogent view about the ontological nature of human mind or is it, rather, an effective/ineffective adaptationist discovery heuristic for generating predictively successful hypothesis about both heretofore unknown psychological traits and unknown properties of already identified psychological traits? Considering the inadequacies of the case in favor of massive modularity as an ontological hypothesis, I suggest approaching and valuing massive modularity as an adaptationist discovery heuristic. (shrink)

In the present paper we empirically investigate the psychometric properties of some of the most famous statistical and logical cognitive illusions from the “heuristics and biases” research program by Daniel Kahneman and Amos Tversky, who nearly 50 years ago introduced fascinating brain teasers such as the famous Linda problem, the Wason card selection task, and so-called Bayesian reasoning problems (e.g., the mammography task). In the meantime, a great number of articles has been published that empirically examine single cognitive illusions, theoretically (...) explaining people’s faulty thinking, or proposing and experimentally implementing measures to foster insight and to make these problems accessible to the human mind. Yet these problems have thus far usually been empirically analyzed on an individual-item level only (e.g., by experimentally comparing participants’ performance on various versions of one of these problems). In this paper, by contrast, we examine these illusions as a group and look at the ability to solve them as a psychological construct. Based on an sample ofN= 2,643 Luxembourgian school students of age 16–18 we investigate the internal psychometric structure of these illusions (i.e., Are they substantially correlated? Do they form a reflexive or a formative construct?), their connection to related constructs (e.g., Are they distinguishable from intelligence or mathematical competence in a confirmatory factor analysis?), and the question of which of a person’s abilities can predict the correct solution of these brain teasers (by means of a regression analysis). (shrink)

People often struggle with Bayesian reasoning. However, research showed that people’s performance (and rationality) can be supported by the way of representing the statistical information. First, research showed that using natural frequencies instead of probabilities as format of statistical information increases people’s performance in Bayesian situations thoroughly. Second, research also yielded that people’s performance increases through using visualization. We build our paper on existing research in this field. The main aim is to analyse people’s strategies in Bayesian situations that are (...) still erroneous although statistical information is represented by natural frequencies and visualizations. In particular, we compare two pairs of visualization with similar numerical information (tree diagram vs. unit square and double-tree diagram vs. 2x2-table) concerning their impact on people’s erroneous strategies in Bayesian situations. For this aim, we conducted an experiment with 540 university students. The students were randomly assigned to four conditions that are defined by the four different visualizations of statistical information. The students were asked to indicate a fraction as response in four Bayesian situations. We documented the numerator and the denominator of the students’ responses representing a basic set and a subset in a Bayesian situation. Our results show that people’s erroneous strategies are highly dependent on the visualization. A central finding is that the visualization’s characteristic of making the nested-sets structure of a Bayesian situation transparent has a facilitating effect on people’s Bayesian reasoning. For example, compared to the unit square, a tree diagram does not explicitly visualize the set-subset relations that are relevant in a Bayesian situation. Accordingly, compared to a unit square, a tree diagram partly hinders people to find the correct denominator in a Bayesian situation, and, in particular, triggers to select a wrong numerator. By analyzing people’s erroneous strategies in Bayesian situations, we contribute to investigating approaches to facilitate Bayesian reasoning and to further develop the teaching of Bayesian reasoning. (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)

How do human children come up to carve up and think of the world around them in its most general and abstract structure? And to which degree are these general forms of viewing the world shared by other animals, notably by non-human primates? In response to these questions of what could be called comparative metaphysics, this paper discusses new evidence from developmental and comparative research to argue for the following picture: human children and non-human primates share a basic framework of (...) natural ontology: they think about their natural surroundings in essentialist ways in terms of natural kind objects constituted by their essential properties, and in generic terms as governed by general descriptive regularities. In contrast, there is a great divide when it comes to how human children and non-human primates carve up their social environment: only human children then go on to use their essentialist and generic thinking for developing a distinctively social ontology, to conceive of their surrounding in terms of socially constituted objects governed by general prescriptive norms. (shrink)

This article presents three experiments that examine the relation between order effects and frequency learning, with the following results. First, when frequencies of occurrence are presented as sequences of real events, base rates can be learned and used with a high degree of accuracy. However, conditional probabilities for multiple sequentially presented evidence items cannot be completely learned, due to the distortion of a recency order effect for actual decisions. Second, there is also a recency order effect for belief evaluations, which (...) cannot be eliminated even if base rates are used correctly. Third, base rates learned in one environment can be transferred to another environment, but the transfer soon diminishes due to learning in the new environment. However, belief evaluations are not transferred from one environment to another The existing models of frequency learning cannot explain the order effect for actual decisions because they do not consider sequential information. The existing models of belief updating can explain both types of order effects, but they do not have any mechanisms for frequency learning. To account for the complete spectrum of frequency learning and order effects, we outline our initial effort in developing a unified model that integrates frequency learning and order effects. (shrink)

(2013). Evolutionary modules and Bayesian facilitation: The role of general cognitive resources. Thinking & Reasoning: Vol. 19, No. 1, pp. 27-53. doi: 10.1080/13546783.2012.713177.