Koehler's target article attempts a balanced view of the relevance of knowledge of base rates to judgments of subjective or credal probability, but he is not sensitive enough to the difference between requiring and permitting the equation of probability judgments with base rates, the interaction between precision of base rate and reference class information, and the possibility of indeterminate probability judgment.

(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.

Dans cet article, je décris une partie de l’histoire des travaux sur le raisonnement humain mené par les philosophes et les psychologues en psychologie expérimentale. Ce travail interdisciplinaire particulier est intéressant, car il montre de quelles façons la recherche interdisciplinaire peut solidifier certaines idées préconçues omniprésentes dans un domaine particulier. Les travaux en psychologie expérimentale ont démontré que certains systèmes normatifs ne parviennent pas à modéliser le raisonnement dans des contextes de langage naturel. J’affirmerai, par conséquent, que les philosophes devraient (...) se servir de cette recherche psychologique comme motivation pour modifier ces modèles normatifs ou changer radicalement nos idées sur la manière dont ces modèles sont appliqués au raisonnement en langage naturel. (shrink)

Psychologists' work on conversational pragmatics and judgment suggests a refreshing approach to charitable interpretation and theorizing. This charitable approach—what I call Gricean charity —recognizes the role of conversational assumptions and norms in subject-experimenter communication. In this paper, I outline the methodological lessons Gricean charity gleans from psychologists' work in conversational pragmatics. In particular, Gricean charity imposes specific evidential standards requiring that researchers collect empirical information about (1) the conditions of successful and unsuccessful communication for specific experimental contexts, and (2) the (...) conversational norms governing communication in experimental contexts. More generally, the Gricean turn in psychological research shifts focus from attributional to reflexive, situational explanations. Gricean charity does not primarily seek to rationalize subject responses. Rather, it imposes evidential requirements on psychological studies for the purpose of gaining a more accurate picture of the surprising and muddled ways in which we weigh evidence and draw. Key Words: Gricean charity • methodological rationalism • interpretation • principle of charity • cognitive psychology • conversational pragmatics • heuristics and biases • reflexive analysis. (shrink)

is normative in the sense that it aims to make recommendations for improving human judgment; it aims to have a practical impact on morally and politically significant human decisions and actions; and it studies normative, rational judgment qua rational judgment. These nonstandard ways of understanding ACP as normative collectively suggest a new interpretation of the strong replacement thesis that does not call for replacing normative epistemic concepts, relations, and inquiries with descriptive, causal ones. Rather, it calls for recognizing that the (...) aims and normative inquiries of epistemology and normative psychology have become intermutual in nature. Key Words: Heuristics and biases • applied cognitive psychology • normative psychology • rationality • naturalized epistemology • Epistemics • Applied Naturalized Epistemology • strong replacement • strategic reliabilism • ameliorative psychology. (shrink)

Recent work in social epistemology has shown that, in certain situations, less communication leads to better outcomes for epistemic groups. In this paper, we show that, ceteris paribus, a Bayesian agent may believe less strongly that a single agent is biased than that an entire group of independent agents is biased. We explain this initially surprising result and show that it is in fact a consequence one may conceive on the basis of commonsense reasoning.

Visual imagination (or visualization) is peculiar in being both free, in that what we imagine is up to us, and useful to a wide variety of practical reasoning tasks. How can we rely upon our visualizations in practical reasoning if what we imagine is subject to our whims? The key to answering this puzzle, I argue, is to provide an account of what constrains the sequence in which the representations featured in visualization unfold—an account that is consistent with its freedom. (...) Three different proposals are outlined, building on theories that link visualization to sensorimotor predictive mechanisms (e.g., efference copies, forward models ). Each sees visualization as a kind of reasoning, where its freedom consists in our ability to choose the topic of the reasoning. Of the three options, I argue that the approach many will find most attractive—that visualization is a kind of off-line perception, and is therefore in some sense misrepresentational—should be rejected. The two remaining proposals both conceive of visualization as a form of sensorimotor reasoning that is constitutive of one’s commitments concerning the way certain kinds of visuomotor scenarios unfold. According to the first, these commitments impinge on one’s web of belief from without, in the manner of normal perceptual experience; according to the second, these commitments just are one’s (occurrent) beliefs about such generalizations. I conclude that, despite being initially counterintuitive, the view of visualization as a kind of occurrent belief is the most promising. (shrink)

What is the proper attitude toward what is expressed by a vague sentence in the face of borderline evidence? Some call this attitude “ambivalence” and distinguish it from uncertainty. It has been argued that Classical Epistemicism conjoined with classical probability theory fails to characterize this attitude, and that we must therefore abandon classical logic or classical probabilities in the presence of vagueness. In this paper, I give a characterization of ambivalence assuming a supervaluationist semantics for vague terms that does not (...) revise either. The theory, which I call the theory of Superprobabilities, identifies the proper attitude toward a vague sentence, in the presence of exact borderline evidence, as the set of classical probabilities of the evidence on each member of the set of all precisifications of a vague sentence. I defend the use of sets of probabilities against objections by generalizing the theory of Superprobabilities to a decision theory called Superrationality. I then compare the merits of the theory of Superprobabilities to Classical Epistemicism and nonclassical probabilities theories with respect to the problem of ambivalence. (shrink)

Two distinct issues are sometimes confused in the base rate literature: Why do people make logical mistakes in the assessment of probabilities? and why do subjects not use base rates the way experimenters do? The latter problem may often reflect differences in an implicit reference class rather than a disinclination to update a base rate by Bayes' theorem. Also important are considerations concerning the interaction of several potentially relevant base rates.

The underutilization of base rates is a consistent finding. The strong claim that base rates are ignored has been rejected and this needs no further emphasis. Following the path of “normal science,” research examines the conditions predicting changes in the degree of underutilization. A scientific revolution that might dethrone the heuristics and biases paradigm is not in sight.

Common sense suggests that visual consciousness is essential to skilled motor action, but Andy Clark—inspired by Milner and Goodale's dual visual systems theory—has appealed to a wide range of experimental dissociations to argue that such an assumption is false. Critics of Clark's argument contend that the content driving motor action is actually within subjects' experience, just not easily discovered. In this article, I argue that even if such content exists, it cannot be guiding motor action, since a review of current (...) visual neuroscience indicates that the visual brain areas producing conscious representations are distinct from those driving motor action. (shrink)

Virtue epistemology is among the dominant influences in mainstream epistemology today. An important commitment of one strand of virtue epistemology – responsibilist virtue epistemology (e.g., Montmarquet 1993; Zagzebski 1996; Battaly 2006; Baehr 2011) – is that it must provide regulative normative guidance for good thinking. Recently, a number of virtue epistemologists (most notably Baehr, 2013) have held that virtue epistemology not only can provide regulative normative guidance, but moreover that we should reconceive the primary epistemic aim of all education as (...) the inculcation of the intellectual virtues. Baehr’s picture contrasts with another well-known position – that the primary aim of education is the promotion of critical thinking (Scheffler 1989; Siegel 1988; 1997; 2017). In this paper – that we hold makes a contribution to both philosophy of education and epistemology and, a fortiori, epistemology of education – we challenge this picture. We outline three criteria that any putative aim of education must meet and hold that it is the aim of critical thinking, rather than the aim of instilling intellectual virtue, that best meets these criteria. On this basis, we propose a new challenge for intellectual virtue epistemology, next to the well-known empirically-driven ‘situationist challenge’. What we call the ‘pedagogical challenge’ maintains that the intellectual virtues approach does not have available a suitably effective pedagogy to qualify the acquisition of intellectual virtue as the primary aim of education. This is because the pedagogic model of the intellectual virtues approach (borrowed largely from exemplarist thinking) is not properly action-guiding. Instead, we hold that, without much further development in virtue-based theory, logic and critical thinking must still play the primary role in the epistemology of education. (shrink)

Virtue epistemology is among the dominant influences in mainstream epistemology today. An important commitment of one strand of virtue epistemology – responsibilist virtue epistemology – is that it must provide regulative normative guidance for good thinking. Recently, a number of virtue epistemologists have held that virtue epistemology not only can provide regulative normative guidance, but moreover that we should reconceive the primary epistemic aim of all education as the inculcation of the intellectual virtues. Baehr’s picture contrasts with another well-known position (...) – that the primary aim of education is the promotion of critical thinking. In this paper – that we hold makes a contribution to both philosophy of education and epistemology and, a fortiori, epistemology of education – we challenge this picture. We outline three criteria that any putative aim of education must meet and hold that it is the aim of critical thinking, rather than the aim of instilling intellectual virtue, that best meets these criteria. On this basis, we propose a new challenge for intellectual virtue epistemology, next to the well-known empirically-driven ‘situationist challenge’. What we call the ‘pedagogical challenge’ maintains that the intellectual virtues approach does not have available a suitably effective pedagogy to qualify the acquisition of intellectual virtue as the primary aim of education. This is because the pedagogic model of the intellectual virtues approach is not properly action-guiding. Instead, we hold that, without much further development in virtue-based theory, logic and critical thinking must still play the primary role in the epistemology of education. (shrink)

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural (...) network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena. (shrink)

Koehler's call for a reanalysis of the base rate fallacy is particularly important in the applied domain of accounting, since base rate data appear to be an important input for many accounting tasks. In this commentary I discuss the use of base rates in accounting and explain why more flexible standards of performance are important when judging the use of base rates.

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role (...) in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)

Commentators agree that simple conclusions about a general base rate fallacy are not appropriate. It is more constructive to identify conditions under which base rates are differentially weighted. Commentators also agree that improving the ecological validity of the research is desirable, although this is less important to those interested exclusively in psychological processes. The philosophers and ecologists among the commentators offer a kinder perspective on base rate reasoning than the psychologists. My own perspective is that the interesting questions (both psychological (...) and normative/prescriptive) are best answered through empirical studies that use real-world performance standards. (shrink)

During the last 25 years, researchers studying human reasoning and judgment in what has become known as the “heuristics and biases” tradition have produced an impressive body of experimental work which many have seen as having “bleak implications” for the rationality of ordinary people (Nisbett and Borgida 1975). According to one proponent of this view, when we reason about probability we fall victim to “inevitable illusions” (Piattelli-Palmarini 1994). Other proponents maintain that the human mind is prone to “systematic deviations from (...) rationality” (Bazerman & Neale 1986) and is “not built to work by the rules of probability” (Gould 1992). It has even been suggested that human beings are “a species that is uniformly probability-blind” (Piattelli-Palmarini 1994). This provocative and pessimistic interpretation of the experimental findings has been challenged from many different directions over the years. One of the most recent and energetic of these challenges has come from the newly emerging field of evolutionary psychology, where it has been argued that it’s singularly implausible to claim that our species would have evolved with no “instinct for probability” and, hence, be “blind to chance” (Pinker 1997, 351). Though evolutionary psychologists concede that it is possible to design experiments that “trick our probability.. (shrink)

This commentary discusses three points: (1) The implications of the fact that it is rational to ignore base rates if probabilities are estimated by frequencies from samples without missing data (natural sampling); (2) second order probabilities distributions are a plausible way to model imprecise probabilities; and (3) Bayesian networks represent a normative reference for multi-cue models of probabilistic inference.

Failure to consider base rate is regarded as potentially hazardous, mainly because its consideration is assumed to be determined solely by P(H/D), the probability of the individuating data if the hypothesis is true, and not at all by P(D/˜H), the probability if the hypothesis is false. However, when P(D/˜H) is unconfounded from P(D/H), it turns out to be the stronger determinant of base rate consideration.

We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President. It postulates that uncertainty is a guide to improbability. In its computer implementation, an intuitive system 1 simulates evidence in mental models and forms analog non-numerical representations of the magnitude of degrees of belief. This system has minimal computational power and combines evidence using a small repertoire of primitive operations. It resolves the uncertainty of divergent evidence for single events, (...) for conjunctions of events, and for inclusive disjunctions of events, by taking a primitive average of non-numerical probabilities. It computes conditional probabilities in a tractable way, treating the given event as evidence that may be relevant to the probability of the dependent event. A deliberative system 2 maps the resulting representations into numerical probabilities. With access to working memory, it carries out arithmetical operations in combining numerical estimates. Experiments corroborated the theory's predictions. Participants concurred in estimates of real possibilities. They violated the complete joint probability distribution in the predicted ways, when they made estimates about conjunctions: P, P, P, disjunctions: P, P, P, and conditional probabilities P, P, P. They were faster to estimate the probabilities of compound propositions when they had already estimated the probabilities of each of their components. We discuss the implications of these results for theories of probabilistic reasoning. (shrink)

Abstract There are two kinds of beliefs. If the ultimate objective is wellbeing (util- ity), the generated beliefs are “practical.” If the ultimate objective is truth, the generated beliefs are “scientific.” This article defends the practical/scientific belief distinction. The proposed distinction has been ignored by standard rational choice theory—as well as by its two major critics, viz., the Tversky/Kahneman program and the Simon/ Gigerenzer program. One ramification of the proposed distinction is clear: agents who make errors with regard to scientific (...) beliefs (e.g., the conjunction fallacy) should not be taken as committing irrationality—because they are most probably engaging the other kind of maximization, the pursuit of wellbeing. (shrink)

Setting the two hypotheses of complete neglect and full use of base rates against each other is inappropriate. The proper question concerns the degree to which base rates are used (or neglected), and under what conditions. We outline alternative approaches and recommend regression analysis. Koehler's conclusion that we have been oversold on the base rate fallacy seems to be premature.

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high‐dimensional space using a distributional semantics model. High‐dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high‐level cognition, and (...) it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high‐level cognition and provide human‐readable, explainable models, but scale poorly to naturalistic, non‐symbolic, or big data. Vector‐symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector‐space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector‐space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition. (shrink)

Illusionists claim that phenomenal consciousness does not exist, but merely seems to exist. Most debates concerning illusionism focus on whether or not it is true—whether phenomenal consciousness really is an illusion. Here I want to tackle a different question: assuming illusionism is true, what kind of illusion is the illusion of phenomenality? Is it a “rich” illusion—the cognitively impenetrable activation of an incorrect representation—or a “sparse” illusion—the cognitively impenetrable activation of an incomplete representation, which leads to drawing incorrect judgments? I (...) present this distinction and I classify the most influential illusionist theories along this line of divide. I then offer an argument against the accounts of the illusion of phenomenality in terms of sparse illusion. (shrink)

Many philosophers have tried to defend physicalism concerning phenomenal consciousness, by explaining dualist intuitions within a purely physicalist framework. One of the most common strategies to do so consists in interpreting the alleged “explanatory gap” between phenomenal states and physical states as resulting from a fallacy, or a cognitive illusion. In this paper, I argue that the explanatory gap does not rest on a fallacy or a cognitive illusion. This does not imply the falsity of physicalism, but it has consequences (...) on the kind of physicalism we should embrace. (shrink)

In recent years, the philosophy and psychology of reasoning have made a ‘social turn’: in both disciplines it is now common to reject the traditional picture of reasoning as a solitary intellectual exercise in favour of the idea that reasoning is a social activity driven by social aims. According to the most prominent social account, Mercier and Sperber’s interactionist theory, this implies that reasoning is not a normative activity. As they argue, in producing reasons we are not trying to ‘get (...) things right’; instead our aims are to justify ourselves and persuade others to accept our views. I will argue that even if interactionism has played a crucial role in bringing about the ‘social turn’ in our thinking about reasoning, it does not convince in its claim that reasoning is not a normative activity. Moreover, I argue that it is in fact perfectly possible to understand reasoning as a social tool that is also aimed at getting things right. I will propose that Gilbert Ryle’s conceptualization of reasoning as ‘didactic discourse’ offers one possible way to understand reasoning as both social and normative activity, and that as such his ideas could be of great value for the social turn in our thinking about reasoning. (shrink)

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point (...) estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way. (shrink)

Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages), both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on “transparent” Bayesian problems varies widely, and generally remains rather unimpressive. (...) We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures) at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point. (shrink)