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)

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)

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)

Intelligent systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identification of specific instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus in a (...) low-dimensional proximal shape space. That space turns out to support representation of distal shape similarities which is veridical in the sense of Shepard's (1968) notion of second-order isomorphism (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous, symmetric, and hierarchical, as in the multidimensional scaling models (Shepard, 1980), to discrete and non-hierarchical, as in the general contrast models (Tversky, 1977; Shepard and Arabie, 1979). (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)

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 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)

Perceptual similarity is one of the most fiercely debated topics in the philosophy and psychology of perception. The documented history of the issue spans all the way from Plato – who regarded similarity as a key factor for human perceptual experience and cognition – through to contemporary psychologists – who have tried to determine whether, and if so, how similarity relationships can be established between stimuli both within and across the senses. Recent research on cross-sensory associations, otherwise known as crossmodal (...) correspondences – that is, the existence of observable consensual associations, or mappings, between stimuli across different senses – represents an especially interesting field in which to study perceptual similarity. In fact, most accounts of crossmodal association that have been put forward in the literature to date evoke perceptual similarity as a key explanatory factor mediating the underlying association. At the same time, however, these various accounts raise several important theoretical questions concerning the very nature of similarity, with, for example, the sensory, affective, or cognitive underpinnings of similarity judgements remaining unclear. We attempt to shed light on these questions by examining the various accounts of crossmodal associations that have been put forward in the literature. Our suggestion is that perceptual similarity varies from being phenomenologically-based to conceptually-based. In particular, we propose that the nature of the associations underlying similarity judgements – whether these associations are phenomenologically-, structurally-, emotionally-, or conceptually-based – may be represented in a two-dimensional space with associative strength on one axis, and cognitive penetrability on the other. (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)

This paper investigates the ways English speakers employ the predicates like, similar, and resemble to express similarity in natural speech. A corpus of 450 instances was created and manually coded, and an acceptability rating experiment was conducted. Converging evidence from the corpus analysis and the experiment shows that the three predicates occur with the same range of uses, but differ in their propensities to occur with particular dimensions of similarity. Specifically, like is associated with metaphorical comparisons, and resemble is associated (...) with visual comparisons. A synchronic account of these findings is developed, based on a distinction between conventionally encoded meaning and prototypical usage. A diachronic account is also proposed, highlighting the commonality in the predicates’ conceptual origins and the differences in their historical usage. This work has theoretical and methodological implications for the study of similarity. In particular, it raises the possibility that previous findings may be distorted due to a reliance on certain similarity predicates in the phrasing of experimental instructions and stimuli. It also ties into debates on synonymy, suggesting a shift in attention from a distinction between denotational and associative meaning, to the aforementioned distinction between conventional and prototypical meaning. (shrink)

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...) there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged,diversity.Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into:modality-specificrepresentations, instantiated on perceptual similarity;multimodalrepresentations, instantiated primarily on the discovery of spatiotemporal contiguity; andcategoricalrepresentations, instantiated primarily on social interaction. These elements serve as the building blocks ofcomplex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e.,predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology. (shrink)

People judge positive information to be more alike than negative information. This good-bad asymmetry in similarity was argued to constitute a true property of the information ecology (Alves, H., Koch, A., & Unkelbach, C. (2017). Why good is more alike than bad: Processing implications. Trends in Cognitive Sciences, 21, 69–79). Alternatively, the asymmetry may constitute a processing outcome itself, namely an influence of phasic affect on information processing. Because no research has yet tested whether phasic affect influences perceived similarity among (...) stimuli, we conducted 5 Experiments that also tested whether phasic affect can account for the higher judged similarity among positive compared to negative stimuli. In three experiments, we affectively charged pictures of different Pokemon by pairing them with monetary gains and losses (Exp. 1a, 1b) as well as positive and negative trait words (Exp. 2); yet, the evaluative charge did not differentially influence perceived similarity among the Pokemon. Experiment 3 replicated the basic similarity asymmetry among positive and negative words, and found that it was unaffected by externally induced phasic affect. Experiment 4 showed that phasic affect had no influence on perceived similarity of non-evaluative words either. We conclude that albeit a weak influence of phasic affect on perceived similarity of stimuli cannot be ruled out entirely, it can most likely not account for the typically medium to large sized asymmetry in similarity among positive and negative stimuli. (shrink)

In the present dissertation, I propose a general, robust, and objective characteristic of the information environment, according to which negative information is more diverse than positive information. I present an explanatory framework for this phenomenon based on the non-extremity of positive qualities. Specifically, most attribute dimensions host one “positive” range which is surrounded by two distinct “negative” ranges, resulting in a greater diversity of negative compared to positive attributes, stimuli, and information in general. Chapter 1 of my dissertation reviews evidence (...) for the proposed greater diversity of negative information, showing that positive stimuli are consistently perceived as more similar than negative stimuli. Chapter 2 presents a line of research that demonstrates the same phenomenon in the domain of person perception. People perceive other people they like as more similar to one another than other people they dislike. I further suggest that the greater diversity of negative information as an “extrapsychic” phenomenon can account for a number of valence asymmetries in information processing that have previously been explained based on “intrapsychic” affective reactions. In line with this idea, Chapter 3 presents research showing that negative words enjoy a recognition advantage over positive words because they are more diverse. Chapter 4 discusses limitations, open questions and further implications of the current empirical findings and of the explanatory framework provided. (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)

Human cognition is striking in its brilliance and its adaptability. How do we get that way? How do we move from the nearly helpless state of infants to the cognitive proficiency that characterizes adults? In this paper I argue, first, that analogical ability is the key factor in our prodigious capacity, and, second, that possession of a symbol system is crucial to the full expression of analogical ability.

“In reality, all arguments from experience are founded on the similarity which we discover among natural objects, and by which we are induced to expect effects similar to those which we have found to follow from such objects. ... From causes which appear similar we expect similar effects.”.

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)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Similarity is central for the definition of concepts in several theories in cognitive psychology. However, similarity encounters several problems which were emphasized by Goodman in 1972. At the end of his article, Goodman banishes similarity from any serious philosophical or scientific investigations. If Goodman is right, theories of concepts based on similarity encounter a huge problem and should be revised entirely. In this paper, we would like to analyze the notion of similarity with some insight from psychological works on analogical (...) cognition. Analogical cognition compares two situations or objects in order to find similarities between them. In doing so, the analogical process sorts the different features of the two situations or objects and determines the most important ones. The analogical process is also highly sensitive to context. Context-sensitivity is desirable at some level, but it is also problematic as it leads to a computational explosion. To answer this problem, we would like to consider salience as a possible heuristic in the analogical process. We will distinguish three forms of salience: Sensory, categorical, and operational. By taking salience into account, we can introduce a shortcut into the computation of similarity and circumvent computational explosion. (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)

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)

We present a novel way of accounting for similarity judgments. Our approach posits that similarity stems from three main sources—familiarity, priming, and inherent perceptual likeness. Here, we explore each of these constructs and demonstrate their individual and combined effectiveness in explaining similarity judgments. Using these three measures, our account of similarity explains ratings of simple, color-based perceptual stimuli that display asymmetry effects, as well as more complicated perceptual stimuli with structural properties; more traditional approaches to similarity solve one or the (...) other and have difficulty accounting for both. Overall, our work demonstrates the importance of each of these components of similarity in explaining similarity judgments, both individually and together, and suggests important implications for other similarity approaches. (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)

The ability to reason by analogy is particularly important because it permits the extension of knowledge of a target domain by virtue of its similarity to a base domain via a process of analogical inference. The general procedure for analogical inference involves copying structure from the base to the target in which missing information is generated, and substitutions are made for items for which analogical correspondences have already been found. A pure copying with substitution and generation process is too profligate (...) to be useful, and so constraints must be placed on what information is to be carried over. In this paper, the importance of systematicity as a constraint on inference is explored in four studies in which subjects find correspondences between domains and also make inferences. This work suggests that people prefer to make inferences of information connected to systematic correspondences between domains. A second important theme of this paper is that violations of one‐to‐one mapping can lead to inconsistent object substitutions in inference. The data reveal no such inconsistent substitutions in people's inferences, suggesting that they do respect one‐to‐one mapping in analogical inference. These findings are discussed relative to four prominent computational models of analogical mapping and inference. (shrink)

Putnam’s internal realism is aimed at reconciling realist and antirealist intuitions about truth and the nature of reality. A common complaint about internal realism is that it has never been stated with due precision. This paper attempts to render the position precise by drawing on the literature on conceptual spaces as well as on earlier work of the authors on the notion of identity.

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)

Similarity‐based theories of concepts have a broad intuitive appeal and have been successful in accounting for various phenomena related to the formation and application of concepts. Their adequacy as theories of concepts has been questioned, however, as similarity is often taken as too flexible, too unconstrained, to be explanatory of categorization. In this article, I propose an account of similarity that takes the “foil” against which the target items are measured as integral to the process of comparison, making the similarity (...) relation a fundamentally triadic one. I argue that this account delivers more internal constraints on the process of comparison than the traditional, dyadic account of similarity does. Finally, I propose that this account is advantageous for a similarity‐based theory of concepts, as it easily integrates with the notion of conceptual taxonomy and uses it to deliver additional constraints on categorization. On such a theory of concepts, the foil, whose variability is in large part responsible for the flexible nature of similarity judgements, is turned within the framework of conceptual taxonomy to the very element that constrains similarity judgements in categorization. Therefore, this notion of similarity mitigates the worry that similarity is too flexible to be explanatory of categorization. (shrink)

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision‐making process: instance‐based knowledge, recognition‐based retrieval, adaptive strategies, necessity‐based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refinement of instances, containing the decision‐making situation, action, and utility of decisions. As decision makers interact with a dynamic task, they recognize a situation according to its similarity to past instances, (...) adapt their judgment strategies from heuristic‐based to instance‐based, and refine the accumulated knowledge according to feedback on the result of their actions. The IBLT's learning mechanisms have been implemented in an ACT‐R cognitive model. Through a series of experiments, this paper shows how the IBLT's learning mechanisms closely approximate the relative trend magnitude and performance of human data. Although the cognitive model is bounded within the context of a dynamic task, the IBLT is a general theory of decision making applicable to other dynamic environments. (shrink)

In this paper we present the results of two experiments. The first is on the categorization of texture words in the English language. The goal was to determine whether there is a common basis for subjects' groupings of words related to visual texture, and if so, to identify the underlying dimensions used to categorize those words.Eleven major clusters were identified through hierarchical cluster analysis, ranging from “random” to “repetitive”. These clusters remained intact in a multidimensional scaling solution. The stress for (...) a three‐dimensional solution obtained through multidimensional scaling was 0.18, meaning that 82% of the variance in the data is explained through the use of three dimensions. It appears that the major dimensions of texture descriptors are repetitive versus nonrepetitive; linearly oriented versus circularly oriented; and simple versus complex.In the second experiment we measured the strength of association between texture words and texture images. The goal was to determine whether there is any systematic correspondence between the domains of texture words and texture images. Pearson's coefficient of contingency, a measure of the strength of association, was found to be 0.63 for words corresponding to given images and 0.56 for images corresponding to given words. Thus the texture categories in the verbal space and those in the visual space are strongly tied.In sum, our two experiments show (a) that despite the tremendous variety in the words we have to describe textures, there is an underlying structure to the lexical space which can be derived from the experimental data; and (b) that the association between a category of words and a category of images was strongest when both categories represent the same underlying property. This suggests that subjects' organizations of texture terms are systematically tied to their organization of texture images. (shrink)

Although our concepts of “Mama,” “milk,” and “mice” have much in common, the suggestion that they are identical in structure in the mind of the prelinguistic child is mistaken. Even infants think about objects as different from substances and appreciate the distinction between kinds (e.g., mice) and individuals (e.g., Mama). Such cognitive capacities exist in other animals as well, and have important adaptive consequences.

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)