Cognitive Science, Volume 46, Issue 7, July 2022.

It is usually accepted that deductions are non-informative and monotonic, inductions are informative and nonmonotonic, abductions create hypotheses but are epistemically irrelevant, and both deductions and inductions can’t provide new insights. In this article, I attempt to provide a more cohesive view of the subject with the following hypotheses: (1) the paradigmatic examples of deductions, such as modus ponens and hypothetical syllogism, are not inferential forms, but coherence requirements for inferences; (2) since any reasoner aims to be coherent, any inference (...) must be deductive; (3) a coherent inference is an intuitive process where the premises should be taken as sufficient evidence for the conclusion, which on its turn should be viewed as necessary evidence for the premises in some modal range; (4) inductions, properly understood, are abductions, but there are no abductions beyond the fact that in any inference the conclusion should be regarded as necessary evidence for the premises; (5) monotonicity is not only compatible with the retraction of past inferences given new information, but it is a requirement for it; (6) this explanation of inferences holds true for discovery processes, predictions and trivial inferences. (shrink)

How are biases encoded in our representations of social categories? Philosophical and empirical discussions of implicit bias overwhelmingly focus on salient or statistical associations between target features and representations of social categories. These are the sorts of associations probed by the Implicit Association Test and various priming tasks. In this paper, we argue that these discussions systematically overlook an alternative way in which biases are encoded, that is, in the dependency networks that are part of our representations of social categories. (...) Dependency networks encode information about how features in a conceptual representation depend on each other. This information determines the degree of centrality of a feature for a conceptual representation. Importantly, centrally encoded biases systematically disassociate from those encoded in salient-statistical associations. Furthermore, the degree of centrality of a feature determines its cross-contextual stability: in general, the more central a feature is for a concept, the more likely it is to survive into a wide array of cognitive tasks involving that concept. Accordingly, implicit biases that are encoded in the central features of concepts are predicted to be more resilient across different tasks and contexts. As a result, the distinction between centrally encoded and salient-statistical biases has important theoretical and practical implications. (shrink)

The coherence of independent reports provides a strong reason to believe that the reports are true. This plausible claim has come under attack from recent work in Bayesian epistemology. This work shows that, under certain probabilistic conditions, coherence cannot increase the probability of the target claim. These theorems are taken to demonstrate that epistemic coherentism is untenable. To date no one has investigated how these results bear on different conceptions of coherence. I investigate this situation using Thagard’s ECHO model of (...) explanatory coherence. Thagard’s ECHO model provides a natural representation of the evidential significance of multiple independent reports. (shrink)

I argue that in addressing worries about the validity and reliability of implicit measures of social cognition, theorists should draw on research concerning “entitativity perception.” In brief, an aggregate of people is perceived as highly “entitative” when its members exhibit a certain sort of unity. For example, think of the difference between the aggregate of people waiting in line at a bank versus a tight-knit group of friends: The latter seems more “groupy” than the former. I start by arguing that (...) entitativity perception modulates the activation of implicit biases and stereotypes. I then argue that recognizing this modulatory role will help researchers to address concerns surrounding the validity and reliability of implicit measures. (shrink)

In some domains experts perform better than novices, and in other domains experts do not generally perform better than novices. According to empirical studies of expert performance, this is because the former but not the latter domains make available to training practitioners a direct form of learning feedback. Several philosophers resource this empirical literature to cast doubt on the quality of philosophical expertise. They claim that philosophy is like the dubious domains in that it does not make available the good, (...) direct kind of learning feedback, and thus there are empirical grounds for doubting the epistemic quality of philosophical expertise. I examine the empirical studies that are purportedly bad news for professional philosophers. On the basis of that examination, I provide three reasons why the empirical study of non-philosophical expertise does not undermine the status of philosophical expertise. First, the non-philosophical task-types from which the critics generalize are unrepresentative of relevant philosophical task-types. Second, empirical critiques of non-philosophical experts are often made relative to the performance of linear models—a comparison that is inapt in a philosophical context. Third, the critics fail to discuss findings from the empirical study of non-philosophical expertise that have more favorable implications for the epistemic status of philosophical expertise. In addition to discussing implications for philosophical expertise, this article makes progress in the philosophical analysis of the science of expertise and expert development. (shrink)

This paper considers how surprise (or its lack) can be cast as a metacognitive signal with an adaptive function in learning new knowledge and revising belief networks. It reviews the phenomena that may hinder this signal (e.g., hindsight bias) and argues for its extrinsic exploitation in instructional and educational contexts by educators, journalists and parents, who might train learners to internalize the use of surprise to drive explanation‐based learning.

This book examines the philosophical conception of abductive reasoning as developed by Charles S. Peirce, the founder of American pragmatism. It explores the historical and systematic connections of Peirce's original ideas and debates about their interpretations. Abduction is understood in a broad sense which covers the discovery and pursuit of hypotheses and inference to the best explanation. The analysis presents fresh insights into this notion of reasoning, which derives from effects to causes or from surprising observations to explanatory theories. The (...) author outlines some logical and AI approaches to abduction as well as studies various kinds of inverse problems in astronomy, physics, medicine, biology, and human sciences to provide examples of retroductions and abductions. The discussion covers also everyday examples with the implication of this notion in detective stories, one of Peirce’s own favorite themes. The author uses Bayesian probabilities to argue that explanatory abduction is a method of confirmation. He uses his own account of truth approximation to reformulate abduction as inference which leads to the truthlikeness of its conclusion. This allows a powerful abductive defense of scientific realism. This up-to-date survey and defense of the Peircean view of abduction may very well help researchers, students, and philosophers better understand the logic of truth-seeking. (shrink)

The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.

Bradford Hill (1965) highlighted nine aspects of the complex evidential situation a medical researcher faces when determining whether a causal relation exists between a disease and various conditions associated with it. These aspects are widely cited in the literature on epidemiological inference as justifying an inference to a causal claim, but the epistemological basis of the Hill aspects is not understood. We offer an explanatory coherentist interpretation, explicated by Thagard's ECHO model of explanatory coherence. The ECHO model captures the complexity (...) of epidemiological inference and provides a tractable model for inferring disease causation. We apply this model to three cases: the inference of a causal connection between the Zika virus and birth defects, the classic inference that smoking causes cancer, and John Snow’s inference about the cause of cholera. (shrink)

An influential suggestion about the relationship between Bayesianism and inference to the best explanation holds that IBE functions as a heuristic to approximate Bayesian reasoning. While this view promises to unify Bayesianism and IBE in a very attractive manner, important elements of the view have not yet been spelled out in detail. I present and argue for a heuristic conception of IBE on which IBE serves primarily to locate the most probable available explanatory hypothesis to serve as a working hypothesis (...) in an agent’s further investigations. Along the way, I criticize what I consider to be an overly ambitious conception of the heuristic role of IBE, according to which IBE serves as a guide to absolute probability values. My own conception, by contrast, requires only that IBE can function as a guide to the comparative probability values of available hypotheses. This is shown to be a much more realistic role for IBE given the nature and limitations of the explanatory considerations with which IBE operates. (shrink)

I develop a probabilistic account of coherence, and argue that at least in certain respects it is preferable to (at least some of) the main extant probabilistic accounts of coherence: (i) Igor Douven and Wouter Meijs’s account, (ii) Branden Fitelson’s account, (iii) Erik Olsson’s account, and (iv) Tomoji Shogenji’s account. Further, I relate the account to an important, but little discussed, problem for standard varieties of coherentism, viz., the “Problem of Justified Inconsistent Beliefs.”.

As it is standardly conceived, Inference to the Best Explanation (IBE) is a form of ampliative inference in which one infers a hypothesis because it provides a better potential explanation of one’s evidence than any other available, competing explanatory hypothesis. Bas van Fraassen famously objected to IBE thus formulated that we may have no reason to think that any of the available, competing explanatory hypotheses are true. While revisionary responses to the Bad Lot Objection concede that IBE needs to be (...) reformulated in light of this problem, reactionary responses argue that the Bad Lot Objection is fallacious, incoherent, or misguided. This paper shows that the most influential reactionary responses to the Bad Lot Objection do nothing to undermine the original objection. This strongly suggests that proponents of IBE should focus their efforts on revisionary responses, i.e. on finding a more sophisticated characterization of IBE for which the Bad Lot Objection loses its bite. (shrink)

Hearsay or indirect testimony receives little discussion even today in epistemology, and yet it represents one of the cardinal modes for the transmission of knowledge and for human cognitive development. It suffices to think of school education whereby a student listens to teachers reporting knowledge acquired, often indirectly, from the most varied sources such as text books, newspapers, personal memory, television, etc… Or let us consider the importance of oral tradition in the social and cultural development of civilisations. Or even (...) let us call to mind the learning process of infants who, only thanks to the knowledge learned from others, succeed in learning the language. Finally, with the emerging digital technology, the information gleaned from third parties is influencing the creation of knowledge in a full gamut of fields, in a quasi-comprehensive manner. This leads us to claim that hearsay, despite the reductionist conceptual scheme to which epistemology has confined it in that indirect testimony, is in any case quintessential in the dissemination of knowledge. This work fully expounds upon the basic mechanism of indirect testimonial transmission and provides an epistemologically founded explanation of the cognitive possibilities of hearsay by investigating, in the light of the anti-reductionist paradigm, three mutually connected epistemic properties, namely trust, reputation and coherence, that are key in the epistemic justification of the truth acquired via an indirect testimony. (shrink)

In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...) relevant to robustness analysis. I close by briefly outlining a promising alternative approach inspired by Horwich’s (1982) eliminative account of evidential diversity. (shrink)

Classical models of decision making deal fairly well with uncertainty, where settings are well-structured in terms of goals, alternatives, and consequences. Conversely, the typical ill-structured nature of strategy choices remains a challenge for extant models. Such cases can hardly build on the past, and their novelty makes the prediction of consequences a very difficult and poorly robust task. The weakness of the classical expected utility model in representing such problems has not been adequately solved by recent extensions. In this paper (...) we offer an explanatory coherence model for decision making in ill-structured problems. We model alternatives as sets of concurrent causal explanations of reality that act as justifications for action. According to these premises, choice is based on an evaluation of the internal coherence and the consistency of competing explanations of the available evidence. This model is psychologically grounded on causal inference and builds on the connectionist tradition of explanatory coherence. To illustrate the model, we consider the decision of investing in a new technology and we discuss how changes in the structure of alternatives may impact on the solution. We show how the final choice depends on collecting the relevant evidence, making the suitable hypotheses, and drawing the consistent causal explanations linking the two. (shrink)

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

Cognitive studies of science and technology have had a long history of largely independent research projects that have appeared in multiple outlets, but rarely together. The emergence of a new International Society for Psychology of Science and Technology suggests that this is a good time to put some of the latest work in this area into topiCS in a way that will both acquaint readers with the cutting edge in this domain and also give them a hint of its history. (...) One core theme includes how scientists, inventors, and engineers represent and solve problems; another, related theme is the extent to which they distribute and share cognition. Methodologies include fine‐grained studies of historical records, protocols of working scientists, observations and comparisons of engineering science laboratories, and computational simulations designed both to serve as research tools and also to improve scientific problem‐solving. The series of articles will conclude with the Associate Editor’s suggestions for future research. (shrink)

Work on analogy has been done from a number of disciplinary perspectives throughout the history of Western thought. This work is a multidisciplinary guide to theorizing about analogy. It contains 1,406 references, primarily to journal articles and monographs, and primarily to English language material. classical through to contemporary sources are included. The work is classified into eight different sections (with a number of subsections). A brief introduction to each section is provided. Keywords and key expressions of importance to research on (...) analogy are discussed in the introductory material. Electronic resources for conducting research on analogy are listed as well. (shrink)

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two (...) or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures. (shrink)

Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a (...) concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability. (shrink)

I argue that coherence is truth-conducive in that coherence implies an increase in the probability of truth. Central to my argument is a certain principle for transitivity in probabilistic support. I then address a question concerning the truth-conduciveness of coherence as it relates to (something else I argue for) the truth-conduciveness of consistency, and consider how the truth-conduciveness of coherence bears on coherentist theories of justification.

Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding--Grimm's, de Regt's, and de Regt and Dieks's--can be replaced by earlier accounts of scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.

This article generalizes the explanationist account of inference to the best explanation. It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence should (...) be equated with explanatory power and considered to be derivable from the other explanatory virtues: unification, explanatory depth and simplicity. (shrink)

Scientific and ‘intuitive’ or ‘folk’ theories are typically characterized as serving three critical functions: prediction, explanation, and control. While prediction and control have clear instrumental value, the value of explanation is less transparent. This paper reviews an emerging body of research from the cognitive sciences suggesting that the process of seeking, generating, and evaluating explanations in fact contributes to future prediction and control, albeit indirectly by facilitating the discovery and confirmation of instrumentally valuable theories. Theoretical and empirical considerations also suggest (...) why explanations may nonetheless feel intrinsically valuable. The paper concludes by considering some implications of the psychology of explanation for a naturalized philosophy of explanation. (shrink)

This paper evaluates four competing psychological explanations for why the jury in the O.J. Simpson murder trial reached the verdict they did: explanatory coherence, Bayesian probability theory, wishful thinking, and emotional coherence. It describes computational models that provide detailed simulations of juror reasoning for explanatory coherence, Bayesian networks, and emotional coherence, and argues that the latter account provides the most plausible explanation of the jury's decision.

This paper explores the ethical relevance of a precise new characterization of coherence as maximization of satisfaction of positive and negative constraints. A coherence problem can be stated by specifying a set of elements to be accepted or rejected along with sets of positive and negative constraints that incline pairs of elements to be accepted together or rejected together. Computationally tractable and psychologically plausible algorithms are available for determining the acceptance and rejection of elements in a way that reliably approximates (...) coherence maximization. This paper shows how justification of ethical principles and particular judgments can be accomplished by taking into account deductive, explanatory, analogical, and deliberative coherence. (shrink)

I take up three puzzles about our emotional and evaluative responses to fiction. First, how can we even have emotional responses to characters and events that we know not to exist, if emotions are as intimately connected to belief and action as they seem to be? One solution to this puzzle claims that we merely imagine having such emotional responses. But this raises the puzzle of why we would ever refuse to follow an author’s instructions to imagine such responses, since (...) we happily imagine many other implausible things. A natural response to this second puzzle is that our responses to fiction are real, and so can’t just be conjured up in response to an author’s demands. However, this simple response is inadequate, because we often respond differently to people and events in fiction than we would if we encountered them in real life. Solving these three puzzles in a consistent way requires the notion of a “perspective” on a fictional world. I sketch an account of this intuitive but frustratingly amorphous notion: perspectives are tools for organizing our thinking, which can in turn alter our emotional and evaluative responses. Cultivating a perspective can be illuminating, entertaining, or corrupting — or all three at once. (shrink)

Rejecting Klein's claims that normative epistemology and naturalism are mutually exclusive, I defend the normative naturalism of my "Epistemology in an Age of Cognitive Science". When insisting that epistemic standards simultaneously external to, superior to, and independent of those of science do not exist, I hold neither that science exhausts standards of rationality nor that relevant extra-scientific considerations do not exist. Cognitive science may transform how we pose some normative questions in epistemology. Concurring with Klein that the burden of evidence (...) resides with normative naturalism, I explore suggestive proposals that Churchland and Thagard have offered on just this front. (shrink)

The basic theory of scientific understanding presented in Sections 1–2 exploits three main ideas.First, that to understand a phenomenonP (for a given agent) is to be able to fitP into the cognitive background corpusC (of the agent).Second, that to fitP intoC is to connectP with parts ofC (via arguments in a very broad sense) such that the unification ofC increases.Third, that the cognitive changes involved in unification can be treated as sequences of shifts of phenomena inC. How the theory fits (...) typical examples of understanding and how it excludes spurious unifications is explained in detail. Section 3 gives a formal description of the structure of cognitive corpuses which contain descriptive as well as inferential components. The theory of unification is then refined in the light of so called puzzling phenomena, to enable important distinctions, such as that between consonant and dissonant understanding. In Section 4, the refined theory is applied to several examples, among them a case study of the development of the atomic model. The final part contains a classification of kinds of understanding and a discussion of the relation between understanding and explanation. (shrink)

In ‘A reductio of coherentism’ (Analysis 67, 2007) Tom Stoneham offers a novel argument against epistemological coherentism. ‘On the face of it’, he writes, ‘the argument gives a conclusive reductio ad absurdum of any coherence theory of justification. But that cannot be right, can it?’ (p. 254). It could be right, but it isn’t. I argue that coherentists need not accept the central premises of Stoneham’s argument and that, even if these premises were acceptable and true, Stoneham’s reductio would not (...) follow. (shrink)

I suggest a pluralistic account of folk psychology according to which not all predictions or explanations rely on the attribution of mental states, and not all intentional actions are explained by mental states. This view of folk psychology is supported by research in developmental and social psychology. It is well known that people use personality traits to predict behavior. I argue that trait attribution is not shorthand for mental state attributions, since traits are not identical to beliefs or desires, and (...) an understanding of belief or desire is not necessary for using trait attributions. In addition, we sometimes predict and explain behavior through appeal to personality traits that the target wouldn't endorse, and so could not serve as the target's reasons. I conclude by suggesting that our folk psychology includes the notion that some behavior is explained by personality traits—who the person is—rather than by beliefs and desires—what the person thinks. Consequences of this view for the debate between simulation theory and theory theory, as well as the debate on chimpanzee theory of mind are discussed. (shrink)

Scientists and laypeople alike use the sense of understanding that an explanation conveys as a cue to good or correct explanation. Although the occurrence of this sense or feeling of understanding is neither necessary nor sufficient for good explanation, it does drive judgments of the plausibility and, ultimately, the acceptability, of an explanation. This paper presents evidence that the sense of understanding is in part the routine consequence of two well-documented biases in cognitive psychology: overconfidence and hindsight. In light of (...) the prevalence of counterfeit understanding in the history of science, I argue that many forms of cognitive achievement do not involve a sense of understanding, and that only the truth or accuracy of an explanation make the sense of understanding a valid cue to genuine understanding. (shrink)

Philosophers agree that scientific explanations aim to produce understanding, and that good ones succeed in this aim. But few seriously consider what understanding is, or what the cues are when we have it. If it is a psychological state or process, describing its specific nature is the job of psychological theorizing. This article examines the role of understanding in scientific explanation. It warns that the seductive, phenomenological sense of understanding is often, but mistakenly, viewed as a cue of genuine understanding. (...) The article closes with a discussion of several new paths of research that tie the psychology of scientific explanation to cognate notions of learning, testimony, and understanding. (shrink)

This paper investigates the revolutionary conceptual changes that took place when the phlogiston theory of Stahl was replaced by the oxygen theory of Lavoisier. Using techniques drawn from artificial intelligence, it represents the crucial stages in Lavoisier's conceptual development from 1772 to 1789. It then sketches a computational theory of conceptual change to account for Lavoisier's discovery of the oxygen theory and for the replacement of the phlogiston theory.

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal (...) mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation. (shrink)

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in depth. We demonstrate that (...) systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)