Researchers have debated whether there is a relationship between a statement’s truth-value and whether it counts as a lie. One view is that a statement being objectively false is essential to whether it counts as a lie; the opposing view is that a statement’s objective truth-value is inessential to whether it counts as a lie. We report five behavioral experiments that use a novel range of behavioral measures to address this issue. In each case, we found evidence of a relationship. (...) A statement’s truth-value affects how quickly people judge whether it is a lie. When people consider the matter carefully and are told that they might need to justify their answer, they are more likely to categorize a statement as a lie when it is false than when it is true. When given options that inhibit perspective-taking, people tend to not categorize deceptively motivated statements as lies when they are true, even though they still categorize them as lies when they are false. Categorizing a speaker as “lying” leads people to strongly infer that the speaker’s statement is false. People are more likely to spontaneously categorize a statement as a lie when it is false than when it is true. We discuss four different interpretations of relevant findings to date. At present, the best supported interpretation might be that the ordinary lying concept is a prototype concept, with falsity being a centrally important element of the prototypical lie. (shrink)

This paper reports the first empirical investigation of the hypothesis that epistemic appraisals form part of the structure of concepts. To date, studies of concepts have focused on the way concepts encode properties of objects and the way those features are used in categorization and in other cognitive tasks. Philosophical considerations show the importance of also considering how a thinker assesses the epistemic value of beliefs and other cognitive resources and, in particular, concepts. We demonstrate that there are multiple, reliably (...) judged, dimensions of epistemic appraisal of concepts. Four of these dimensions are accounted for by a common underlying factor capturing how well people believe they understand a concept. Further studies show how dimensions of concept appraisal relate to other aspects of concepts. First, they relate directly to the hierarchical organization of concepts, reflecting the increase in specificity from superordinate to basic and subordinate levels. Second, they predict inductive choices in category-based induction. Our results suggest that epistemic appraisals of concepts form a psychologically important yet previously overlooked aspect of the structure of concepts. These findings will be important in understanding why individuals sometimes abandon and replace certain concepts; why social groups do so, for example, during a “scientific revolution”; and how we can facilitate such changes when we engage in deliberate “conceptual engineering” for epistemic, social, and political purposes. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

The likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another’s credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements and the conditional probabilities of (...) those conclusions given other related facts. For example, given that most lawyers make good sales people, what is the probability that most psychologists make good sales people? The results showed that when the fact and the conclusion had the same explanation the fact increased people’s willingness to believe the conclusion, but when they had different explanations the fact decreased the conclusion’s credibility. This decrease is attributed to explanation discounting; the explanation for the fact had the effect of reducing the plausibility of the explanation for the conclusion. (shrink)

Predicate nominals (e.g., “is a female”) seem to label or categorize their subjects, while their adjectival correlates (e.g., “is female”) merely attribute a property. Predicate nominals also elicit essentializing inferential judgments about inductive potential and stable explanatory membership. Data from psychology and semantics support that this distinction is robust and productive. I argue that while the difference between predicate nominals and predicate adjectives is elided by standard semantic theories, it ought not be. I then develop and defend a psychologically motivated (...) semantic account on which predicate nominals attribute kind membership and trigger a presupposition that underpins our essentialist judgments. (shrink)

The present studies tested the hypothesis that strong assumptions about within-category homogeneity impede children’s recognition of the inductive value of diverse samples of evidence. In Study 1a, children (7-year-olds) and adults were randomly assigned to receive a prime emphasizing within-category variability, a prime emphasizing within-category similarities, or to not receive a prime. Only following the variability prime, children demonstrated a reliable preference for evaluating diverse over nondiverse samples to determine whether there is support for a category-wide generalization. Adults demonstrated a (...) robust preference for diverse samples in all conditions. These effects extended beyond the specific categories included in the prime, as well as to multiple types of test questions. Study 1b demonstrated that priming variability leads children to select diverse samples only when doing so is informative for induction. Implications for conceptual development are discussed. (shrink)

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non-monotonicity, in which adding premises to a category-based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category-based induction taking premise sampling (...) assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case. (shrink)

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling (...) assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case. (shrink)

In this paper, I explore the implications of recent empirical research on concept representation for the philosophical enterprise of conceptual analysis. I argue that conceptual analysis, as it is commonly practiced, is committed to certain assumptions about the nature of our intuitive categorization judgments. I then try to show how these assumptions clash with contemporary accounts of concept representation in cognitive psychology. After entertaining an objection to my argument, I close by considering ways in which conceptual analysis might be altered (...) to accord better with the empirical work. (shrink)

Tenenbaum and Griffiths (2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (1987) and Tversky’s (1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen as a complement to, (...) instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

Tenenbaum and Griffiths (Behavioral and Brain Sciences 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard’s (Science 237(4820): 1317–1323, 1987) and Tversky’s (Psychological Review 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that (...) generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity. (shrink)

The feature-matching approach pioneered by Amos Tversky remains a groundwork for psychological models of similarity and categorization but is rarely explicitly justified considering recent advances in thinking about cognition. While psychologists often view similarity as an unproblematic foundational concept that explains generalization and conceptual thought, long-standing philosophical problems challenging this assumption suggest that similarity derives from processes of higher-level cognition, including inference and conceptual thought. This paper addresses three specific challenges to Tversky’s approach: (i) the feature-selection problem, (ii) the problem (...) of cognitive implausibility, and (iii) the problem of unprincipled tweaking. It subsequently supports key insights from Tversky’s account based on recent developments in Bayesian modeling of cognition. A novel computational view of similarity as inference is proposed that addresses each challenge by considering the contrast class as constitutive of similarity and selecting for highly informative features. In so doing, this view illustrates the ongoing promise of the feature-matching approach in explaining perception, generalization and conceptual thought by grounding them in principles of probabilistic inference. (shrink)

Each of our theories of mental representation provides some insight into how the mind works. However, these insights often seem incompatible, as the debates between symbolic, dynamical, emergentist, sub-symbolic, and grounded approaches to cognition attest. Mental representations—whatever they are—must share many features with each of our theories of representation, and yet there are few hypotheses about how a synthesis could be possible. Here, I develop a theory of the underpinnings of symbolic cognition that shows how sub-symbolic dynamics may give rise (...) to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This theory implements a version of conceptual role semantics by positing an internal universal representation language in which learners may create mental models to capture dynamics they observe in the world. The theory formalizes one account of how truly novel conceptual content may arise, allowing us to explain how even elementary logical and computational operations may be learned from a more primitive basis. I provide an implementation that learns to represent a variety of structures, including logic, number, kinship trees, regular languages, context-free languages, domains of theories like magnetism, dominance hierarchies, list structures, quantification, and computational primitives like repetition, reversal, and recursion. This account is based on simple discrete dynamical processes that could be implemented in a variety of different physical or biological systems. In particular, I describe how the required dynamics can be directly implemented in a connectionist framework. The resulting theory provides an “assembly language” for cognition, where high-level theories of symbolic computation can be implemented in simple dynamics that themselves could be encoded in biologically plausible systems. (shrink)

Three experiments, adopting an evolutionary biology perspective, investigated subjects’ inferences about living things. Subjects were told that different enzymes help regulate cell function in two taxa and asked which enzyme a third taxon most likely uses. Experiment 1 and its follow-up, with college students, used triads involving amphibians, reptiles, and mammals (reptiles and mammals are most closely related evolutionarily) and plants, fungi, and animals (fungi are more closely related to animals than to plants). Experiment 2, with 10th graders, also included (...) triads involving mammals, birds, and snakes/crocodilians (birds and snakes/crocodilians are most closely related). Some subjects received cladograms (hierarchical diagrams) depicting the evolutionary relationships among the taxa. The effect of providing cladograms depended on students’ background in biology. The results illuminate students’ misconceptions concerning common taxa and constraints on their willingness to override faulty knowledge when given appropriate evolutionary evidence. Implications for introducing tree thinking into biology curricula are discussed. (shrink)

Recent studies of semantic memory have investigated two theories of optimal search adopted from the animal foraging literature: Lévy flights and marginal value theorem. Each theory makes different simplifying assumptions and addresses different findings in search behaviors. In this study, an experiment is conducted to test whether clustering in semantic memory may play a role in evidence for both theories. Labeled magnets and a whiteboard were used to elicit spatial representations of semantic knowledge about animals. Category recall sequences from a (...) separate experiment were used to trace search paths over the spatial representations of animal knowledge. Results showed that spatial distances between animal names arranged on the whiteboard were correlated with inter-response intervals during category recall, and distributions of both dependent measures approximated inverse power laws associated with Lévy flights. In addition, IRIs were relatively shorter when paths first entered animal clusters, and longer when they exited clusters, which is consistent with marginal value theorem. In conclusion, area-restricted searches over clustered semantic spaces may account for two different patterns of results interpreted as supporting two different theories of optimal memory foraging. (shrink)

. This paper describes a cross-cultural and developmental research project on naïve or folk biology, that is, the study of how people conceptualize nature. The combination of domain specificity and cross-cultural comparison brings a new perspective to theories of categorization and reasoning and undermines the tendency to focus on “standard populations.” From the standpoint of mainstream cognitive psychology, we find that results gathered from standard populations in industrialized societies often fail to generalize to humanity at large. For example, similarity-driven typicality (...) and diversity effects and basic level phenomena either are not found or pattern differently when we move beyond undergraduates. From the perspective of domain-specificity, standard populations may yield misleading results, because such populations represent examples of especially impoverished experience with respect to nature. Conceptions of humans as biological kinds vary with cultural milieu and input conditions. We also show certain phenomena that are robust across populations, consistent with notions of domain-specificity. (shrink)

We propose that the pragmatic factors that mediate everyday deduction, such as alternative and disabling conditions (e.g. Cummins et al., 1991) and additional requirements (Byrne, 1989) exert their effects on specific inferences because of their perceived relevance to more general principles, which we term SuperPs. Support for this proposal was found first in two causal inference experiments, in which it was shown that specific inferences were mediated by factors that are relevant to a more general principle, while the same inferences (...) were unaffected by factors not relevant to the general principle. These results were extended to deontic inferences in two further experiments. Taken together, these findings show that unstated superordinate principles play a significant role in certain types of reasoning. Questions raised by the findings for the main theoretical approaches are discussed. (shrink)

Although cognitive scientists have learned a lot about concepts, their findings have yet to be organized in a coherent theoretical framework. In addition, after twenty years of controversy, there is little sign that philosophers and psychologists are converging toward an agreement about the very nature of concepts. Doing without Concepts (Machery 2009) attempts to remedy this state of affairs. In this article, I review the main points and arguments developed at greater length in Doing without Concepts.

A familiar adage in the philosophy of science is that general hypotheses are better supported by varied evidence than by uniform evidence. Several studies suggest that young children do not respect this principle, and thus suffer from a defect in their inductive methodology. We argue that the diversity principle does not have the normative status that psychologists attribute to it, and should be replaced by a simple rule of probability. We then report experiments designed to detect conformity to the latter (...) rule in children's inductive judgment. The results suggest that young children in both the United States and Taiwan are sensitive to the constraints imposed by the rule on judgments of probability and evidential strength. We conclude with a suggested reinterpretation of the thesis that children's inductive methodology qualifies them as “little scientists.”. (shrink)

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...) introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. (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)

Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I analyse a variety of traditional fallacies, deductive, inductive and causal, and find more merit in them than is generally acknowledged. Bayesian principles provide a framework for understanding ordinary arguments which is well worth developing.

Two main theories of concepts have emerged in the recent psychological literature: the Prototype Theory (which considers concepts to be self-contained lists of features) and the Theory Theory (which conceives of them as being embedded within larger theoretical networks). Experiments supporting the first theory usually differ substantially from those supporting the second, which suggests that these the· ories may be operating at different levels of explanation and dealing with different entities. A convergence is proposed between the Theory Theory and the (...) intentional stance in the philosophy of language and mind. From this stance, concepts should not be thought of as concrete physical entities. (shrink)

Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental evidence (e.g., in (...) sentential priming) suggesting that semantic similarity is more complex than simply a relation between isolated words. This article proposes a framework for representing the meaning of word combinations in vector space. Central to our approach is vector composition, which we operationalize in terms of additive and multiplicative functions. Under this framework, we introduce a wide range of composition models that we evaluate empirically on a phrase similarity task. (shrink)

Although argumentation plays an essential role in our lives, there is no integrated area of research on the psychology of argumentation. Instead research on argumentation is conducted in a number of separate research communities that are spread across disciplines and have only limited interaction. With a view to bridging these different strands, we first distinguish between three meanings of the word ?argument?: argument as a reason, argument as a structured sequence of reasons and claims, and argument as a social exchange. (...) All three meanings are integral to a complete understanding of human reasoning and cognition. Cognitive psychological research on argumentation has focused mostly on the first and second of these meanings, so we present perspectives on argumentation from outside of cognitive psychology, which focus on the second and third. Specifically, we give anoverview of the methods, goals, and disciplinary backgrounds of research on the production, the analysis, and the evaluation of arguments. Finally, inintroducing the experimental studies included in this special issue, which were conducted by researchers from a range of theoretical backgrounds, weunderline the breadth of argumentation research as well as stress opportunities for mutual awareness and integration. (shrink)

I argue for three principle claims. First, philosophers who seek to integrate the semantic and the intentional into a naturalistic metaphysical worldview need to address a task that they have thus far largely failed even to notice: explaining into- level connections between the physical and the intentional in a naturalistically acceptable way. Second, there are serious reasons to think that this task cannot be carried out in a way that would vindicate realism about intentionality. Third, there is much to be (...) said for an irrealist, non-eliminativist, naturalistic approach to intentionality that I call "preservative irrealism". (shrink)

(2013). Rational argument, rational inference. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 21-35. doi: 10.1080/19462166.2012.689327.

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)

Studies of categorical induction typically examine how belief in a premise (e.g., Falcons have an ulnar artery) projects on to a conclusion (e.g., Robins have an ulnar artery). We study induction in cases in which the premise is uncertain (e.g., There is an 80% chance that falcons have an ulnar artery). Jeffrey's rule is a normative model for updating beliefs in the face of uncertain evidence. In three studies we tested the descriptive validity of Jeffrey's rule and a related probability (...) theorem, the rule of total probability. Although these rules provided good approximations to mean judgments in some cases, the results from regression and correlation analyses suggest that participants focus on the parts of these rules that are associated with the highest overall probability. We relate our findings to rational models of judgment. (shrink)

Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases—assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses (...) produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this “iterated learning” procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners. (shrink)