Abductive reasoning assigns special status to the explanatory power of a hypothesis. But how do people make explanatory judgments? Our study clarifies this issue by asking: How does the explanatory power of a hypothesis cohere with other cognitive factors? How does probabilistic information affect explanatory judgments? In order to answer these questions, we conducted an experiment with 671 participants. Their task was to make judgments about a potentially explanatory hypothesis and its cognitive virtues. In the responses, we isolated three constructs: (...) Explanatory Value, Rational Acceptability, and Entailment. Explanatory judgments strongly cohered with judgments of causal relevance and with a sense of understanding. Furthermore, we found that Explanatory Value was sensitive to manipulations of statistical relevance relations between hypothesis and evidence, but not to explicit information about the prior probability of the hypothesis. These results indicate that probabilistic information about statistical relevance is a strong determinant of Explanatory Value. More generally, our study suggests that abductive and probabilistic reasoning are two distinct modes of inference. (shrink)

Claims pertaining to understanding are made in a variety of contexts and ways. As a result, few in the philosophical literature have made an attempt to precisely characterize the state that is y understanding x. This paper builds an account that does just that. The account is motivated by two main observations. First, understanding x is somehow related to being able to manipulate x. Second, understanding is a mental phenomenon, and so what manipulations are required to be an understander must (...) only be mental manipulations. Combining these two insights, the paper builds an account (URM) of understanding as a certain representational capacity—specifically, understanding x involves possessing a representation of x that could be manipulated in useful ways. By tying understanding to representation, the account correctly identifies that understanding is a fundamentally cognitive achievement. However, by also demanding that which representations count as understanding-conferring be determined by their practical effects, URM captures the insight that understanding is vitally connected to practice. URM is fully general, and can apply equally well to understanding states of affairs, understanding events, and even understanding people and works of art. The ultimate test of URM is its applicability in actual scientific and philosophical discourse. To that end the paper discusses the importance of understanding in the philosophy of science, psychology, and computer science. (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)

In his book Doing Without Concepts, Edouard Machery argues that cognitive scientists should reject the concept of “concept” as a natural, psychological kind. I review and critique several of Machery’s arguments, focusing on his definition of “concept” and on claims against the possibility and utility of a unified account of concepts. In particular, I suggest ways in which prototype, exemplar, and theory-theory approaches to concepts might be integrated.

Machery emphasizes the centrality of explanation for theory-based approaches to concepts. I endorse Machery's emphasis on explanation and consider recent advances in psychology that point to the of explanation, with consequences for Machery's heterogeneity hypothesis about concepts.

The ability to generalize from the known to the unknown is central to learning and inference. Two experiments explore the relationship between how a property is explained and how that property is generalized to novel species and artifacts. The experiments contrast the consequences of explaining a property mechanistically, by appeal to parts and processes, with the consequences of explaining the property functionally, by appeal to functions and goals. The findings suggest that properties that are explained functionally are more likely to (...) be generalized on the basis of shared functions, with a weaker relationship between mechanistic explanations and generalization on the basis of shared parts and processes. The influence of explanation type on generalization holds even though all participants are provided with the same mechanistic and functional information, and whether an explanation type is freely generated (Experiment 1), experimentally provided (Experiment 2), or experimentally induced (Experiment 2). The experiments also demonstrate that explanations and generalizations of a particular type (mechanistic or functional) can be experimentally induced by providing sample explanations of that type, with a comparable effect when the sample explanations come from the same domain or from a different domains. These results suggest that explanations serve as a guide to generalization, and contribute to a growing body of work supporting the value of distinguishing mechanistic and functional explanations. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Carey argues that the aspects of categorization that are diagnostic of deep conceptual structure and, by extension, narrow conceptual content, must be distinguished from those aspects that are incidental to categorization tasks. For natural kind concepts, discriminating between these two types of processes is complicated by the role of explanatory stance and the causal history of features in determining category structure.

Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations—even to themselves—they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments provide (...) evidence for the subsumptive constraints account: prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership, relative to describing items (Exp. 1), thinking aloud (Exp. 2), or free study (Exp. 3). Although explaining facilitates discovery, Experiment 1 finds that description is more beneficial for learning item details. Experiment 2 additionally suggests that explaining anomalous observations may play a special role in belief revision. The findings provide insight into explanation’s role in discovery and generalization. (shrink)

This paper considers three package deals combining views in philosophy of mind, meta-philosophy, and experimental philosophy. The most familiar of these packages gives center-stage to pumping intuitions about fanciful cases, but that package involves problematic commitments both to a controversial descriptivist theory of reference and to intuitions that “negative” experimental philosophers have shown to be suspiciously variable and context-sensitive. In light of these difficulties, it would be good for future-minded experimental philosophers to align themselves with a different package deal. This (...) paper suggests two alternatives. Experimentalists might help fans of “naturalized” approaches discover what natural kinds have been playing an appropriate role in causing us to use concepts as we do. Or, better still, experimentalists might instead help pragmatists and teleo-semanticists discover how our concept usage regularly yields beneficial outcomes, so that we can then craft philosophical analyses that will.. (shrink)

Explanation is a central concept in human psychology. Drawing upon philosophical theories of explanation, psychologists have recently begun to examine the relationship between explanation, probability and causality. Our study advances this growing literature in the intersection of psychology and philosophy of science by systematically investigating how judgments of explanatory power are affected by the prior credibility of a potential explanation, the causal framing used to describe the explanation, the generalizability of the explanation, and its statistical relevance for the evidence. Collectively, (...) the results of our five experiments support the hypothesis that the prior credibility of a causal explanation plays a central role in explanatory reasoning: first, because of the presence of strong main effects on judgments of explanatory power, and second, because of the gate-keeping role it has for other factors. Highly credible explanations were not susceptible to causal framing effects. Instead, highly credible hypotheses were sensitive to the effects of factors which are usually considered relevant from a normative point of view: the generalizability of an explanation, and its statistical relevance for the evidence. These results advance current literature in the philosophy and psychology of explanation in three ways. First, they yield a more nuanced understanding of the determinants of judgments of explanatory power, and the interaction between these factors. Second, they illuminate the close relationship between prior beliefs and explanatory power. Third, they clarify the relationship between abductive and probabilistic reasoning. (shrink)

This paper provides evidence for a contrastive account of explanation that is motivated by pragmatic theories that recognize the contribution that context makes to the interpretation of a prompt for explanation. This study replicates the primary findings of previous work in explanation-based category learning, extending that work by illustrating the critical role of the context in this type of learning. Participants interacted with items from two categories either by describing the items or explaining their category membership. We manipulated the feature (...) overlap between the categories and examined both the explanations generated and acquired knowledge of the categories. Explanations for membership in a given category were influenced by the unprompted contrast category, indicating an important role for contrastive processing in the generation of explanations. The influence of the contrast category was similarly seen in the transfer performance of the participants. (shrink)

A theory of conceptual development must provide an account of the innate representational repertoire, must characterize how these initial representations differ from the adult state, and must provide an account of the processes that transform the initial into mature representations. In Carey, 2009 (The Origin of Concepts), I defend three theses: 1) the initial state includes rich conceptual representations, 2) nonetheless, there are radical discontinuities between early and later developing conceptual systems, 3) Quinean bootstrapping is one learning mechanism that underlies (...) the creation of new representational resources, enabling such discontinuity. I also claim that the theory of conceptual development developed in The Origin of Concepts addresses two of Fodor's challenges to cognitive science; namely, to show how learning could possibly lead to an increase in expressive power and to defeat Mad Dog Nativism, the thesis that all concepts lexicalized as mono-morphemic words are innate. A recent article by Georges Rey (Mind & Language, 29.2, 2014) argues that my responses to Fodor's challenges fail, because, he says, I fail to distinguish concept possession from manifestation and I do not confront Goodman's new riddle of induction. My response is to show that, and how, new primitives in a language of thought can be learned, that there are easy routes and hard ones to doing so, and that characterizing the learning mechanisms involved is the key to understanding both concept possession and constraints on induction. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)