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)

In this paper we develop test cases to adjudicate between dual-process and the causal mapping explanations of order effects. Using dilemmas with minimized emotional force, we explore new conditions for order effects to occur. Overall, the results support causal model theory. We produced novel evidence that order effects extend not only to cases with low emotional engagement, but also to specialized judgments about whether an action violates a rule. However, when objects are sacrificed instead of persons the order effect either (...) disappears or becomes symmetrical, contrary to previous theorizing that it is an asymmetrical transfer effect. Causal model theory needs to be developed to include interplays between the moral status of sacrificed entities and computational models of causal mapping. Symmetric order effects remain a puzzle, motivating future research. Though we do not know how to explain them yet, we discuss how symmetric order effects can influence policy decision making. (shrink)

In philosophy of science, abstraction tends to be subsumed under representation, often being described as the omission of a target’s features when it is represented. This approach to abstraction sidesteps cognitive aspects of abstraction processes. However, cognitive aspects of abstraction are important in understanding the role of historically grounded epistemic criteria supporting modeling in science. Drawing on recent work on the relation between metaphor and abstraction, we introduce the concept of paths of abstraction, and use historical and contemporary examples to (...) point to their role in guiding the development of relevance criteria which support modeling strategies in science. (shrink)

According to Satosi Watanabe's "theorem of the ugly duckling", the number of predicates satisfied by any two different particulars is a constant, which does not depend on the choice of the two particulars. If the number of predicates satisfied by two particulars is their number of properties in common, and the degree of resemblance between two particulars is a function of their number of properties in common, then it follows that the degree of resemblance between any two different particulars is (...) also constant, which is absurd. Avoiding this absurd conclusion requires questioning assumptions about infinity in the proof or interpretation of the theorem, adopting a sparse conception of properties, or denying degree of resemblance is a function of number of properties in common. After arguing against both the first two options, this paper argues for a version of the third which analyses degree of resemblance as a function of properties in common, but weighted by their degree of naturalness or importance. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

The history of gender challenges faced by women in elite sports is fraught with controversy and injustice. These athletes' unique physical beauty creates what appears to be a paradox yet is, in fact, scientifically predictable. Intense training for the highest levels of competition leads to unique bodily strength and rare beauty associated with specific anatomic changes, leading top athletes to be singled out as exceptions from their gender and even excluded from competing. Authorities like the IOC and IAF, as well (...) as coaches and fellow athletes, use traditional and sometimes racialized aesthetic norms as the basis for ungrounded judgments of "gender mis-identity." Misjudging the gender identity of elite athletes exemplifies a biased cognitive framework, a form of erroneous and damaging categorical perception that we call "perceptual sexism." We argue that perceptual sexism has a long history within aesthetic and competitive realms and is still perpetuated by popular culture. Correcting this will reduce injustices created by gender identity controversies. (shrink)

We argue that analogical reasoning, particularly Gentner's structure-mapping theory, provides an integrative theoretical framework through which we can better understand the development of symbol use. Analogical reasoning can contribute both to the understanding of others’ intentions and the establishment of correspondences between symbols and their referents, two crucial components of symbolic understanding. We review relevant research on the development of symbolic representations, intentionality, comparison, and similarity, and demonstrate how structure-mapping theory can shed light on several ostensibly disparate findings in the (...) literature. Focusing on visual symbols, we argue that analogy underlies and supports the understanding of both intention and correspondence, which may enter into a reciprocal bootstrapping process that leads children to gain the prodigious human capacity of symbol use. (shrink)

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

For more than fifty years, taxonomists have proposed numerous alternative definitions of species while they searched for a unique, comprehensive, and persuasive definition. This monograph shows that these efforts have been unnecessary, and indeed have provably been a pursuit of a will o’ the wisp because they have failed to recognize the theoretical impossibility of what they seek to accomplish. A clear and rigorous understanding of the logic underlying species definition leads both to a recognition of the inescapable ambiguity that (...) affects the definition of species, and to a framework-relative approach to species definition that is logically compelling, i.e., cannot not be accepted without inconsistency. An appendix reflects upon the conclusions reached, applying them in an intellectually whimsical taxonomic thought experiment that conjectures the possibility of an emerging new human species. (shrink)

Pictures are 2D surfaces designed to elicit 3D-scene-representing experiences from their viewers. In this essay, I argue that philosophers have tended to underestimate the relevance of research in vision science to understanding the nature of pictorial experience. Both the deeply entrenched methodology of virtual psychophysics as well as empirical studies of pictorial space perception provide compelling support for the view that pictorial experience and seeing face-to-face are experiences of the same psychological, explanatory kind. I also show that an empirically informed (...) account of pictorial experience provides resources to develop a novel, resemblance-based account of depiction. According to what I call the deep resemblance theory, pictures work by presenting virtual models of objects and scenes in phenomenally 3D, pictorial space. (shrink)

We advance a theory of inductive reasoning based on similarity, and test it on arguments involving mammal categories. To measure similarity, we quantified the overlap of neural activation in left Brodmann area 19 and the left ventral temporal cortex in response to pictures of different categories; the choice of of these regions is motivated by previous literature. The theory was tested against probability judgments for 40 arguments generated from 9 mammal categories and a common predicate. The results are interpreted in (...) the context of Hume’s thesis relating similarity to inductive inference. (shrink)

The effect of state anxiety on analogical reasoning was investigated by examining qualitative differences in mapping performance between anxious and non-anxious individuals reasoning about pictorial analogies. The working-memory restriction theory of anxiety, coupled with theories of analogy that link complexity of mapping with working-memory capacity, predicts that high anxiety will impair the ability to find correspondences based on relations between multiple objects relative to correspondences based on overlap of attributes between individual objects. Anxiety was induced in one condition by a (...) stressful speeded subtraction task administered prior to the analogy task. Anxious participants produced fewer relational responses and more attribute responses than did non-anxious participants, both in the absence of explicit instructions to find relational mappings (Experiment 1) and after receiving such instructions (Experiment 2). The findings support the postulated links among anxiety, working memory, and the ability to perform complex analogical mapping. (shrink)

This paper presents a new response to the colour similarity argument, an argument that many people take to pose the greatest threat to colour physicalism. The colour similarity argument assumes that if colour physicalism is true, then colour similarities should be scrutable under standard physical descriptions of surface reflectance properties such as their spectral reflectance curves. Given this assumption, our evident failure to find such similarities at the reducing level seemingly proves fatal to colour physicalism. I argue that we should (...) dispense with this assumption, and thus endorse the inscrutability of colour similarity. This strategy is inspired by parallels between the colour similarity argument and the explanatory gap between mind and body made vivid by Jackson’s (1986) knowledge argument, and in particular by type-B physicalist responses to that argument. This inscrutability response is further motivated by cases in chemistry and biochemistry in which analogous scrutability theses fail to hold. Along the way, I present a challenge to standard formulations of the colour similarity argument based on the extreme context sensitivity of the similarity relation. Most presentations of the argument fail to control for such contextual variation, which raises the distinct possibility that the argument equivocates on the similarity relation across its premises. Although ultimately inconclusive, this context challenge forces a significant reformulation of the colour similarity argument, and highlights the need for much greater care in handling claims about colour similarity. (shrink)

Political discourse is saturated with metaphor, but evidence for the persuasive power of this language has been hard to come by. We addressed this issue by investigating whether voting intentions were affected by implicit mappings suggested by a metaphorically framed message, drawing on a real-world example of political rhetoric about the federal budget. In the first experiment, the federal budget was framed as similar to or different from a household budget, though the information participants received was identical in both conditions. (...) When the federal budget was described as similar to a household's, people considered the personal finances of a presidential candidate more relevant—a finding we replicated in a larger, pre-registered study. In a follow-up experiment, we presented participants with a more explicit rhetorical argument and found a similar effect, moderated by political affiliation. These studies illuminate how metaphorical comparison affects cognition for important real-world issues, sometimes in unintended ways. (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)

Quantum probability theory (QP) is the best formal representation available of the most common form of judgment involving attribute comparison (inside judgment). People are capable, however, of judgments that involve proportions over sets of instances (outside judgment). Here, the theory does not do so well. I discuss the theory both in terms of descriptive adequacy and normative appropriateness.

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)

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)

: According to a consensus of psycho-physiological and philosophical theories, color sensations (or qualia) are generated in a cerebral "space" fed from photon-photoreceptor interaction (producing "metamers") in the retina of the eye. The resulting "space" has three dimensions: hue (or chroma), saturation (or "purity"), and brightness (lightness, value or intensity) and (in some versions) is further structured by primitive or landmark "colors"—usually four, or six (when white and black are added to red, yellow, green and blue). It has also been (...) proposed that there are eleven semantic universals—labeling the previous six plus the "intermediaries" of orange, pink, brown, purple, and gray. There are many versions of this consensus, but they all aim to provide ontological, epistemological and semantic blueprints for the brute fact of the reality of color ordained by Nature (evolution). In contrast to this consensus, we have argued that "seeing color" is not a matter of light waves impacting on our eyes, producing sensations to be categorized and labeled in the "color space" in the brain. While electrochemical events may unproblematically be regarded as the causal precondition for seeing color, the reception of sensations in "the color space" as semantically labeled natural categories, kinds, or information, is a "just so" story: it is Wittgenstein's beetle in a box. In contrast we consider that the authority of this consensus might better be regarded not as the result of the truth-tracking of nature, but as the sociohistorical outcome of philosophical presuppositions, scientific theories, experimental practices, technological apparatus, and their feed forward into the lifeworld. The question we shall therefore explore is whether, or to what extent, we ourselves are changed, as the conditions of production of color science change. Thus we are doing a kind of anthropology at two levels: of color science itself (and its effect on our own lifeworld), and of those studied by the "anthropology of color". As befits this stance we are agnostic about the theoretical entities of color science (cf. van Fraassen 2001), and within this new context, we propose to cross-cut object-and-subject, organism-and-environment (the bedrock of color science) in socio-historical ways. Our approach is in part inspired by, but not the same as, that of Gibson, in that we wish to pursue the notion of "social affordances" (Burmudez 1995). We suggest that color has become a naturalization through science-based technologies, which, through praxes and materializations, have become the perceptual and cultural entities that structure experience and understanding in the lifeworld. It is this naturalization that we shall refer to and characterize as "the historically inflected exosomatic organ". Consequently we shall explore the historical ontology of "color" without assuming an underlying biological constant (Dupré 2001). In part 1 we show the flimsiness of the evidence for the three dimensions of color, borrowed from physics, and fine-tuned to a "standard observer" (a "spectral creature" with a phenomenal "color space"). In part 2 we address the structuring of hue through the development of color circles and color spaces. This is followed by a review of the evidence for unique hues. Again the evidence is shown to be flimsy. We then show that an isolated domain of color is a particular kind of model, not a "natural given". In part 3, after reviewing what is referred to as "the isomorphy thesis," we discuss the exemplary case study of Berlin and Kay (1969). This illustrates the pull of stadial models presupposed by their evolutionary theory of color language. The Berlin and Kay paradigm proposes that American English color terms are incorrigible and can provide the universal metalanguage. We conclude by presenting an alternative account, namely that we ourselves are changed as the conditions of production of color science change. We argue that it is better to regard "seeing-color" as a historically inflected exosomatic organ that provides social affordances for those trained to grasp them. (shrink)

Similarity is one of the most important relations humans perceive, arguably subserving category learning and categorization, generalization and discrimination, judgment and decision making, and other cognitive functions. Researchers have proposed a wide range of representations and metrics that could be at play in similarity judgment, yet have not comprehensively compared the power of these representations and metrics for predicting similarity within and across different semantic categories. We performed such a comparison by pairing nine prominent vector semantic representations with seven established (...) similarity metrics that could operate on these representations, as well as supervised methods for dimensional weighting in the similarity function. This approach yields a factorial model structure with 126 distinct representation‐metric pairs, which we tested on a novel dataset of similarity judgments between pairs of cohyponymic words in eight categories. We found that cosine similarity and Pearson correlation were the overall best performing unweighted similarity functions, and that word vectors derived from free association norms often outperformed word vectors derived from text (including those specialized for similarity). Importantly, models that used human similarity judgments to learn category‐specific weights on dimensions yielded substantially better predictions than all unweighted approaches across all types of similarity functions and representations, although dimension weights did not generalize well across semantic categories, suggesting strong category context effects in similarity judgment. We discuss implications of these results for cognitive modeling and natural language processing, as well as for theories of the representations and metrics involved in similarity. (shrink)

Empirical studies indicate that analogy consists of two main processes: retrieval and mapping. While current theories and models of analogy have revealed much about the mainly structural constraints that govern the mapping process, the similarities that underpin the correspondences between individual representational elements and drive retrieval are understood in less detail. In existing models symbol similarities are externally defined but neither empirically grounded nor theoretically justified. This paper introduces a new model (EMMA: the environmental model of analogy) which relies on (...) co‐occurrence information provided by LSA (Latent Semantic Analysis; Landauer & Dumais, 1997) to ground the relations between the symbolic elements aligned in analogy. LSA calculates a contextual distribution for each word encountered in a corpus by counting the frequency with which it co‐occurs with other words. This information is used to define a model that locates each word encountered in a high‐dimensional space, with relations between elements in this space representing contextual similarities between words. A series of simulation experiments demonstrate that the environmental approach to semantics embodied in LSA can produce appropriate patterns of analogical retrieval, but that this semantic measure is not sufficient to model analogical mapping. The implications of these findings, both for theories of representation in analogy research and more general theories of semantics in cognition, are explored. (shrink)

Two experiments investigated whether infants represent goal‐directed actions of others in a way that allows them to draw inferences to unobserved states of affairs (such as unseen goal states or occluded obstacles). We measured looking times to assess violation of infants' expectations upon perceiving either a change in the actions of computer‐animated figures or in the context of such actions. The first experiment tested whether infants would attribute a goal to an action that they had not seen completed. The second (...) experiment tested whether infants would infer from an observed action the presence of an occluded object that functions as an obstacle. The looking time patterns of 12‐month‐olds indicated that they were able to make both types of inferences, while 9‐month‐olds failed in both tasks. These results demonstrate that, by the end of the first year of life, infants use the principle of rational action not only for the interpretation and prediction of goal‐directed actions, but also for making productive inferences about unseen aspects of their context. We discuss the underlying mechanisms that may be involved in the developmental change from 9 to 12 months of age in the ability to infer hypothetical (unseen) states of affairs in teleological action representations. (shrink)

The distinction between rules and similarity is central to our understanding of much of cognitive psychology. Two aspects of existing research have motivated the present work. First, in different cognitive psychology areas we typically see different conceptions of rules and similarity; for example, rules in language appear to be of a different kind compared to rules in categorization. Second, rules processes are typically modeled as separate from similarity ones; for example, in a learning experiment, rules and similarity influences would be (...) described on the basis of separate models. In the present article, I assume that the rules versus similarity distinction can be understood in the same way in learning, reasoning, categorization, and language, and that a unified model for rules and similarity is appropriate. A rules process is considered to be a similarity one where only a single or a small subset of an object's properties are involved. Hence, rules and overall similarity operations are extremes in a single continuum of similarity operations. It is argued that this viewpoint allows adequate coverage of theory and empirical findings in learning, reasoning, categorization, and language, and also a reassessment of the objectives in research on rules versus similarity. Key Words: categorization; cognitive explanation; language; learning; reasoning; rules; similarity. (shrink)

We re-evaluate existing data that demonstrate a large amount of variability in the content of categories considering the fact that these data have been obtained in a specific task: the production of features of single isolated categories. We present new data that reveal a large consensus when participants have to judge whether or not a given feature is characteristic of a category and we show that classification tasks produce an intermediate level of consensus. We argue that the differences observed between (...) tasks are due to the extent of implied context and we propose a reinterpretation of typicality effects, demonstrating that they are compatible with the existence of a stable conceptual core. In order to explain how the existence of a conceptual core is consistent with variability due to context, we present a theory of categorisation based on a property tree organisation. Within a domain of description, we distinguish between semantic implications (flying → moving) and empirical implications (flying → having wings) as well as between properties used to describe objects. Semantic implications serve to build property lines and Galois lattices are used to reveal category structures according to empirical implications. We show that variability in the category content may be explained by the fact that some properties are emphasised while others are masked according to the context. (shrink)

Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, (...) and to date they have all been equally plausible and equally successful. And, just to be explicit, I do not mean “IQ” by “intelligence.” I am using “intelligence” in the way AI uses it: as a semi-techical term referring to a general property of all intelligent systems, animal (including humans), or machine, alike.). (shrink)

Given the fundamental role that concepts play in theories of cognition, philosophers and cognitive scientists have a common interest in concepts. Nonetheless, there is a great deal of controversy regarding what kinds of things concepts are, how they are structured, and how they are acquired. This chapter offers a detailed high-level overview and critical evaluation of the main theories of concepts and their motivations. Taking into account the various challenges that each theory faces, the chapter also presents a novel approach (...) to concepts that is organized around two ideas. The first is a pluralistic view of differing types of conceptual structure. The second is a model that treats concepts as atomic representations that are linked to various types of conceptual structures. (shrink)