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

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...) psychology. Despite the ubiquity of causal reasoning, textbooks of cognitive psychology have neglected this growing field. The goal of The Oxford Handbook of Causal Reasoning is to fill this gap. The handbook brings together the leading researchers in the field of causal reasoning and offers state-of-the-art presentations of theories and research. It provides introductions of competing theories of causal reasoning, and discusses its role in various cognitive functions and domains. The final section presents research from neighboring fields. 1. Causal Reasoning: An Introduction Michael R. Waldmann Part I: Theories of Causal Cognition 2. Associative Accounts of Causal Cognition Mike E. Le Pelley, Oren Griffiths, and Tom Beesley 3. Rules of Causal Judgment: Mapping Statistical Information Onto Causal Beliefs José C. Perales, Andrés Catena, Antonio Cándido, and Antonio Maldonado 4. The Inferential Reasoning Theory of Causal Learning: Toward a Multi- Process Propositional Account Yannick Boddez, Jan De Houwer, and Tom Beckers 5. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World: Generalizing the Invariance of Causal Power Patricia W. Cheng and Hongjing Lu 6. The Acquisition and Use of Causal Structure Knowledge Benjamin Margolin Rottman 7. Formalizing Prior Knowledge in Causal Induction Thomas L. Griffiths 8. Causal Mechanisms Samuel G. B. Johnson and Woo-kyoung Ahn 9. Force Dynamics and Causation Phillip Wolff and Robert Thorstad 10. Mental Models and Causation P. N. Johnson- Laird and Sangeet S. Khemlani 11. Pseudocontingencies Klaus Fiedler and Florian Kutzner 12. Singular Causation David Danks 13. Cognitive Neuroscience of Causal Reasoning Joachim T. Operskalski and Aron K. Barbey Part II: Basic Cognitive Functions 14. Visual Impressions of Causality Peter White 15. Goal-Directed Actions Bernhard Hommel 16. Planning and Control Magda Osman 17. Reinforcement Learning and Causal Models Samuel J. Gershman 18. Causation and the Probability of Causal Conditionals David E. Over 19. Causal Models and Conditional Reasoning Mike Oaksford and Nick Chater 20. Concepts as Causal Models: Categorization Bob Rehder 21. Concepts as Causal Models: Induction Bob Rehder 22. Causal Explanation Tania Lombrozo and Nadya Vasilyeva 23. Diagnostic Reasoning Björn Meder and Ralf Mayrhofer 24. Inferring Causal Relations by Analogy Keith J. Holyoak and Hee-Seung Lee 25. Causal Argument Ulrike Hahn, Roland Bluhm, and Frank Zenker 26. Causality in Decision- Making York Hagmayer and Philip M. Fernbach Part III: Domains of Causal Reasoning 27. Intuitive Theories Tobias Gerstenberg and Joshua B. Tenenbaum 28. Space, Time, and Causality Marc J. Buehner 29. Causation in Legal and Moral Reasoning David A. Lagnado and Tobias Gerstenberg 30. The Role of Causal Knowledge in Reasoning About Mental Disorders Woo-kyoung Ahn, Nancy S. Kim, and Matthew S. Lebowitz 31. Causality and Causal Reasoning in Natural Language Torgrim Solstad and Oliver Bott 32. Social Attribution and Explanation Denis Hilton Part IV: Development, Phylogeny, and Culture 33. The Development of Causal Reasoning Paul Muentener and Elizabeth Bonawitz 34. Causal Reasoning in Non-Human Animals Christian Schloegl and Julia Fischer 35. Causal Cognition and Culture Andrea Bender, Sieghard Beller, and Douglas L. Medin. (shrink)

There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general (...) idea via recent work on explanationist models of updating, which are fundamentally probabilistic but assign a substantial, non-Bayesian role to explanatory considerations. (shrink)

Uncertainty, insufficient information or information of poor quality, limited cognitive capacity and time, along with value conflicts and ethical considerations, are all aspects thatmake risk managementand riskcommunication difficult. This paper provides a review of different risk concepts and describes how these influence risk management, communication and planning in relation to forest ecosystem services. Based on the review and results of empirical studies, we suggest that personal assessment of risk is decisive in the management of forest ecosystem services. The results are (...) used together with a reviewof different principles of the distribution of risk to propose an approach to risk communication that is effective aswell as ethically sound. Knowledge of heuristics and mutual information on both beliefs and desires are important in the proposed risk communication approach. Such knowledge provides an opportunity for relevant information exchange, so that gaps in personal knowledge maps can be filled in and effective risk communication can be promoted. (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)

Recent work on conditional reasoning argues that denying the antecedent [DA] and affirming the consequent [AC] are defeasible but cogent patterns of argument, either because they are effective, rational, albeit heuristic applications of Bayesian probability, or because they are licensed by the principle of total evidence. Against this, we show that on any prevailing interpretation of indicative conditionals the premises of DA and AC arguments do not license their conclusions without additional assumptions. The cogency of DA and AC inferences rather (...) depends on contingent factors extrinsic to, and independent of, what is asserted by DA and AC arguments. arguments do not license their conclusions without additional assumptions. The cogency of DA and AC inferences rather depends on contingent factors extrinsic to, and independent of, what is asserted by DA and AC arguments. (shrink)

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, (...) that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory led to transitive causal judgments. On the other hand, chains schematized as multiple chunks led to intransitive judgments despite strong intermediate links. Normative accounts of causal intransitivity could not explain these intransitive judgments. (shrink)

The fallacy beneath the base rate fallacy is that we know what a base rate is. We talk as if base rates and individuating information were two different kinds of information. From a Bayesian perspective, however, the only difference between base rate and individuating information is – which comes first.

Any instance (i.e., event, behavior, trait) belongs to infinitely many reference classes, hence there are infinitely many base rates from which to choose. People clearly do not entertain all possible reference classes, however, so something must be limiting the search space. We suggest some possible mechanisms that determine which reference class is evoked for the purpose of judgment and decision.

The important question is how people process probabilistic information, not whether they process it in accordance with a normative model that we never should have expected them to be capable of following. Experience is not the cure, as widely thought, to problems with utilizing base rate information.

Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In (...) this paper we exploit such structures to investigate how people make predictions based on uncertain hierarchical knowledge. We distinguish between aligned and non-aligned environments, and conjecture that people assume alignment. Participants were exposed to a non-aligned training set in which the most probable superordinate category predicted one outcome, whereas the most probable subordinate category predicted a different outcome. In the test phase participants allowed their initial probability judgments about category membership to shift their final ratings of the probability of the outcome, even though all judgments were made on the basis of the same statistical data. In effect people were primed to focus on the most likely path in an inference tree, and neglect alternative paths. These results highlight the importance of the level at which statistical data are represented, and suggest that when faced with hierarchical inference problems people adopt a simplifying heuristic that assumes alignment. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (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.

Realizing the ideal of democracy requires political inclusion for citizens. A legitimate democracy must give citizens the opportunity to express their attitudes about the relative attractions of different policies, and access to political mechanisms through which they can be counted and heard. Actual governance often aims not at accurate belief, but at nonepistemic factors like achieving and maintaining institutional stability, creating the feeling of government legitimacy among citizens, or managing access to influence on policy decision-making. I examine the traditional relationship (...) between inclusiveness and accuracy, and illustrate this connection by discussing empirical work on how group decision-making can improve accuracy. I also advance a Generic Epistemic Principle that any evidence-based decision-making procedures must embrace. Focusing on policy-making, I then measure the distance between these standards and the ones actually implemented in U.S. political settings. Psychological research on individual and group decision-making is a source of normative assessment for existing policy judgment, but it neither rationalizes nor legitimates the actual and typical processes used in U.S. institutions of political decision making. To establish this point, I focus on one characteristic government institution—the U.S. House of Representatives Committee on Science, Space, and Technology—that displays deliberative processes at odds with the sciences they advocate, and with the Generic Epistemic Principle. I explain this discouraging condition in terms of several inveterate factors in U.S. politics: a limitlessly money-driven and endless campaigning process that effectively forces elected representatives to align themselves with money and vote strategically, the use of procedural arrangements known to make people feel politically included when they are not, and the unresponsiveness of a majoritarian (vs. consensus) democracy. (shrink)

In this paper we discuss the new Tweety puzzle. The original Tweety puzzle was addressed by approaches in non-monotonic logic, which aim to adequately represent the Tweety case, namely that Tweety is a penguin and, thus, an exceptional bird, which cannot fly, although in general birds can fly. The new Tweety puzzle is intended as a challenge for probabilistic theories of epistemic states. In the first part of the paper we argue against monistic Bayesians, who assume that epistemic states can (...) at any given time be adequately described by a single subjective probability function. We show that monistic Bayesians cannot provide an adequate solution to the new Tweety puzzle, because this requires one to refer to a frequency-based probability function. We conclude that monistic Bayesianism cannot be a fully adequate theory of epistemic states. In the second part we describe an empirical study, which provides support for the thesis that monistic Bayesianism is also inadequate as a descriptive theory of cognitive states. In the final part of the paper we criticize Bayesian approaches in cognitive science, insofar as their monistic tendency cannot adequately address the new Tweety puzzle. We, further, argue against monistic Bayesianism in cognitive science by means of a case study. In this case study we show that Oaksford and Chater’s (2007, 2008) model of conditional inference—contrary to the authors’ theoretical position—has to refer also to a frequency-based probability function. (shrink)

There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular (...) type of causal cognition; the extant data do not warrant any wide-ranging conclusions about the pervasiveness of normative considerations in causal cognition. Of course, almost all empirical investigations involve some manner of ampliative inference, and so we provide novel empirical results demonstrating that there are types of causal cognition that do not seem to be influenced by moral considerations. (shrink)

Most investigations into how people make risky choices have employed a simple drosophila: monetary gambles involving stated outcomes and probabilities. People are asked to make decisions from description . When people decide whether to back up their computer hard drive, cross a busy street, or go out on a date, however, they do not enjoy the convenience of stated outcomes and probabilities. People make such decisions either in the void of ignorance or in the twilight of their own often limited (...) experience of such real-world options. In the latter case, they make decisions from experience . Recent research has consistently documented that decisions from description and decisions from experience can lead to substantially different choices. Key in this description–experience gap is people’s treatment of rare events. In this paper, I briefly review studies that have documented the description–experience gap, offer several explanations for this gap, and discuss to what extent people’s decisions from experience are in conflict with benchmarks of rationality. (shrink)

Standard Analytic Epistemology (SAE) names a contingently clustered class of methods and theses that have dominated English-speaking epistemology for about the past half-century. The major contemporary theories of SAE include versions of foundationalism, coherentism, reliabilism, and contextualism. While proponents of SAE don’t agree about how to define naturalized epistemology, most agree that a thoroughgoing naturalism in epistemology can’t work. For the purposes of this paper, we will suppose that a naturalistic theory of epistemology takes as its core, as its starting-point, (...) an empirical theory. The standard argument against naturalistic approaches to epistemology is that empirical theories are essentially descriptive, while epistemology is essentially prescriptive, and a descriptive theory cannot yield normative, evaluative prescriptions. In short, naturalistic theories cannot overcome the is-ought divide. Our main goal in this paper is to show that the standard argument against naturalized epistemology has it almost exactly backwards. (shrink)

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.

Forty years of experimentation on class inclusion and its probabilistic relatives have led to inconsistent results and conclusions about human reasoning. Recent research on the conjunction "fallacy" recapitulates this history. In contrast to previous results, we found that a majority of participants adhere to class inclusion in the classic Linda problem. We outline a theoretical framework that attributes the contradictory results to differences in statistical sophistication and to differences in response mode-whether participants are asked for probability estimates or ranks-and propose (...) two precise cognitive algorithms for ranking probabilities. Our framework allows us to make novel predictions about when and why people adhere to class inclusion. Evidence obtained in several studies supports these predictions and demonstrates that the proposed ranking algorithms can account for about three-quarters of participants' inferences in the Linda problem. (shrink)

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.

We believe that when assessing the likelihood of uncertain events, statistically unsophisticated people utilize a coarse internal scale that only has a limited number of categories. The success of the nested sets hypothesis may lie in its ability to provide an appropriate set structure of the problem by reducing the computational demands.

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)

We investigate the articulation between domain-general reasoning and interpretive processes in failures of deductive reasoning. We focus on illusory inferences from disjunction-like elements, a broad class of deductive fallacies studied in some detail over the past 15 years. These fallacies have received accounts grounded in reasoning processes, holding that human reasoning diverges from normative standards. A subset of these fallacies, however, can be analyzed differently: human reasoning is not to blame, instead the premises were interpreted in a nonobvious, yet perfectly (...) predictable and reasonable way. Once we consider these interpretations, the apparent fallacious conclusion is no mistake at all. We give a two-factor account of these fallacies that incorporates both reasoning-based elements and interpretive elements, showing that they are not in real competition. We present novel experimental evidence in favor of our theory. Cognitive load such as induced by a dual-task design is known to hinder the interpretive mechanisms at the core of interpretation-based accounts of the fallacies of interest. In the first experiment of its kind using this paradigm with an inferential task instead of a simpler truth-value-judgment task, we found that the manipulation affected more strongly those illusions where our theory predicts that interpretive processes are at play. We conclude that the best way forward for the field to investigate the elusive line between reasoning and interpretation requires combining theories and methodologies from linguistic semantics and the psychology of reasoning. (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)

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent‐based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the (...) accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one‐third of participants each. In the first, “Categorical” response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, “No change” response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a “certainty threshold” basis, whereby, if they are certain enough about the single event, they will make the “Categorical” response, and if they are below this threshold, they will make the “No change” response. Ramifications are considered for the “categorical” response in particular, as this approach produces a positive‐feedback dynamic similar to that seen in the belief polarization/confirmation bias literature. (shrink)

Periodically, by a rough estimate twice per decade, a new popular book aspires to shake our common understanding of rationality. Since this concept is not only the backbone of normative analysis in...

The literatures on bounded and ecological rationality are built on adaptationism—and its associated modular, cognitivist and computational paradigm—that does not address or explain the evolutionary origins of rationality. We argue that the adaptive mechanisms of evolution are not sufficient for explaining human rationality, and we posit that human rationality presents exaptive origins, where exaptations are traits evolved for other functions or no function at all, and later co-opted for new uses. We propose an embodied reconceptualization of rationality—embodied rationality—based on the (...) reuse of the perception-action system, where many neural processes involved in the control of the sensory-motor system, salient in ancestral environments have been later co-opted to create—by tinkering—high-level reasoning processes, employed in civilized niches. (shrink)

What does it mean to replicate an experiment? A distinction is often drawn between ‘exact’ (or ‘direct’) and ‘conceptual’ replication. However, in recent work, Uljana Feest argues that the notion of replication in itself, whether exact or conceptual, is flawed due to the problem of systematic error, and Edouard Machery argues that, although the notion of replication is not flawed, we should nevertheless dispense with the distinction between exact and conceptual replication. My plan in this paper is to defend the (...) value of replication, along with the distinction between exact and conceptual replication, from the critiques of Feest and Machery. To that end, I provide an explication of conceptual replication, and distinguish it from what I call ‘experimental’ replication. On the basis, then, of a tripartite distinction between exact, experimental and conceptual replication, I argue in response to Feest that replication is still informative despite the prospect of systematic error. I also rebut Machery’s claim that conceptual replication is fundamentally confused and wrongly conflates replication and extension, and in turn raise some objections to his own Resampling Account of replication. (shrink)

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2 × 2 matrix supports a set of generic tasks (...) and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2 × 2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge (...) concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (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)

DNA evidence is one of the most significant modern advances in the search for truth since the cross examination, but its format as a random-match-probability makes it difficult for people to assign an appropriate probative value (Koehler, 2001). While Frequentist theories propose that the presentation of the match as a frequency rather than a probability facilitates more accurate assessment (e.g., Slovic et al., 2000), Exemplar-Cueing Theory predicts that the subjective weight assigned may be affected by the frequency or probability format, (...) and how easily examples of the event, i.e., ‘exemplars’, are generated from linguistic cues that frame the match in light of further evidence (Koehler & Macchi, 2004). This paper presents two juror research studies to examine the difficulties that jurors have in assigning appropriate probative value to DNA evidence when contradictory evidence is presented. Study 1 showed that refuting evidence significantly reduced guilt judgments when exemplars were linguistically cued, even when the probability match and the refuting evidence had the same objective probative value. Moreover, qualitative reason for judgment responses revealed that interpreting refuting evidence was found to be complex and not necessarily reductive; refutation was found indicative of innocence or guilt depending on whether exemplars have been cued or not. Study 2 showed that the introduction of judges’ directions to linguistically cue exemplars, did not increase the impact of refuting evidence beyond its objective probative value, but less guilty verdicts were returned when jurors were instructed to consider all possible explanations of the evidence. The results are discussed in light of contradictory frequentist and exemplar-cueing theoretical positions, and their real-world consequences. (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)

The libertarian attribute of Thaler and Sunstein’s nudge theory (Nudge: improving decisions about health, wealth, and happiness. Yale University Press, New Haven, 2008) is one of the most important features for its candidature as a new model for public policy-making. It relies on the reversibility of choices made under the influence of nudging. Since the mind is articulated into two systems, the choice taken by System 1 is always reversible because it can be overridden by the deliberative and corrective role (...) of System 2. This article does not aim to criticise the whole theory of nudge and neither to assess its practical efficacy as a policy-making tool. Rather it intends to show that there are doubts that the specific claim of reversibility is correct for a subset of nudges called “System 1 nudges” (Sunstein in The ethics of influence, Cambridge University Press, Cambridge, 2016). And that therefore, in this case, the libertarian claim seems little justified. The thrust of my argument contests the truth of the main pillar of the libertarian claim, namely the dual-process theory of mind. I will show that even the minimal version of dual reasoning, namely the Type 1 and Type 2 processes proposed by Evans and Stanovich (Perspect Psychol Sci 8(3):223–241, 2013), is weakened by psychological and neural data. (shrink)

Axiomatic rationality is defined in terms of conformity to abstract axioms. Savage limited axiomatic rationality to small worlds, that is, situations in which the exhaustive and mutually exclusive set of future states S and their consequences C are known. Others have interpreted axiomatic rationality as a categorical norm for how human beings should reason, arguing in addition that violations would lead to real costs such as money pumps. Yet a review of the literature shows little evidence that violations are actually (...) associated with any measurable costs. Limiting axiomatic rationality to small worlds, I propose a naturalized version of rationality for situations of intractability and uncertainty, all of which are not in. In these situations, humans can achieve their goals by relying on heuristics that may violate axiomatic rationality. The study of ecological rationality requires formal models of heuristics and an analysis of the structures of environments these can exploit. It lays the foundation of a moderate naturalism in epistemology, providing statements about heuristics we should use in a given situation. Unlike axiomatic rationality, ecological rationality can explain less-is-more effects, formalize when one should move from ‘is’ to ‘ought,’ and be evaluated by goals beyond coherence, such as predictive accuracy, frugality, and efficiency. Ecological rationality can be seen as a formalization of means–end instrumentalist rationality, based on Herbert Simon’s insight that rational behavior is a function of the mind and its environment. (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)

Cliff Hooker’s effort at developing a naturalistic philosophy for scientific and quotidian reason is formidable. With Barry Hoffmaster, Hooker has recently expanded his naturalism to encompass moral reason and moral epistemology by considering a real life example of moral decision-making. Hoffmaster and Hooker’s work thus presents a unique opportunity to examine a thoroughgoing naturalism applied to a concrete, complex case. This paper offers a critical assessment of the Hoffmaster and Hooker piece through the lens of an externalist approach to naturalism (...) developed by Adam Morton. General lessons for naturalism about reason are revealed before a rough sketch of a novel account of problems and problem-solving is given to illustrate the importance of externalism. (shrink)

A theory of rationality is a theory that evaluates instances of reasoning as rational, irrational, or (ir)rational to some degree. Theories can be categorized as rule-based or consequentialist. Rule-based theories say that rational reasoning accords with certain rules (e.g., of logic or probability). Consequentialist theories say that rational reasoning tends to produce good consequences. For instance, the reliabilist takes rationality to be reasoning that tends to produce mostly true beliefs. The pragmatist takes it to be reasoning that tends to produce (...) mostly useful beliefs. This article reviews some of the features and the challenges of rule-based, reliabilist, and pragmatist theories of rationality. (shrink)

We report a series of experiments examining whether people ascribe knowledge for true beliefs based on probabilistic evidence. Participants were less likely to ascribe knowledge for beliefs based on probabilistic evidence than for beliefs based on perceptual evidence or testimony providing causal information. Denial of knowledge for beliefs based on probabilistic evidence did not arise because participants viewed such beliefs as unjustified, nor because such beliefs leave open the possibility of error. These findings rule out traditional philosophical accounts for why (...) probabilistic evidence does not produce knowledge. The experiments instead suggest that people deny knowledge because they distrust drawing conclusions about an individual based on reasoning about the population to which it belongs, a tendency previously identified by “judgment and decision making” researchers. Consistent with this, participants were more willing to ascribe knowledge for beliefs based on probabilistic evidence that is specific to a particular case. 2016 APA, all rights reserved). (shrink)