The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...) suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure‐mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished (...) cleanly from literal similarity statements, applications of abstractions, and other kinds of comparisons.Two mapping principles are described: (a) Relations between objects, rather than attributes of objects, are mapped from base to target; and (b) The particular relations mapped are determined by systematicity, as defined by the existence of higher‐order relations. (shrink)

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats (...) both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent. (shrink)

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory (...) items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

A comprehensive introduction to current research on the psychology of concept formation and use.

The idea that the language we speak influences the way we think has evoked perennial fascination and intense controversy. According to the strong version of this hypothesis, called the Sapir-Whorf hypothesis after the American linguists who propounded it, languages vary in their semantic partitioning of the world, and the structure of one’s language influences how one understands the world. Thus speakers of different languages perceive the world differently. Although the last two decades have been marked by extreme skepticism concerning the (...) possible effects of language on thought, recent theoretical and methodological advances in cognitive science have given the question new life. Research in linguistics and linguistic anthropology has revealed striking differences in cross-linguistic semantic patterns, and cognitive psychology has developed subtle techniques for studying how people represent and remember experience. It is now possible to test predictions about how a given language influences the thinking of its speakers. Language in Mind includes contributions from both skeptics and believers and from a range of fields. It contains work in cognitive psychology, cognitive development, linguistics, anthropology, and animal cognition. The topics discussed include space, number, motion, gender, theory of mind, thematic roles, and the ontological distinction between objects and substances. I Introduction 1 Whither Whorf 3 Dedre Gentner and Susan Goldin-Meadow II Position Statements 15 1 Languages and Representations 17 Eve V. Clark 2 Language and Mind: Let’s Get the Issues Straight! 25 Stephen C. Levinson 3 The Key Is Social Cognition 47 Michael Tomasello III Language as Lens: Does the Language We Acquire Influence How We See the World? 59 4 Sex, Syntax, and Semantics 61 Lera Boroditsky, Lauren A. Schmidt, and Webb Phillips 5 Speaking versus Thinking about Objects and Actions 81 Barbara C. Malt, Steven A. Sloman, and Silvia P. Gennari 6 The Effects of Spatial Language on Spatial Representation: Setting Some Boundaries 113 Edward Munnich and Barbara Landau 7 Language and Thought Online: Cognitive Consequences of Linguistic Relativity 157 Dan I. Slobin IV Language as Tool Kit: Does the Language We Acquire Augment Our Capacity for Higher-Order Representation and Reasoning? 193 8 Why We’re So Smart 195 Dedre Gentner 9 Does Language Help Animals Think? 237 Stan A. Kuczaj, II, and Jennifer L. Hendry 10 What Makes Us Smart? Core Knowledge and Natural Language 277 Elizabeth S. Spelke 11 Conceptual and Linguistic Factors in Inductive Projection: How Do Young Children Recognize Commonalities between Animals and Plants? 313 Kayoko Inagaki and Giyoo Hatano 12 Language for Thought: Coming to Understand False Beliefs 335 Jill G. de Villiers and Peter A. de Villiers V Language as Category Maker: Does the Language We Acquire Influence Where We Make Our Category Distinctions? 385 13 Space under Construction: Language-Specific Spatial Categorization in First Language Acquisition 387 Melissa Bowerman and Soonja Choi 14 Reevaluating Linguistic Relativity: Language-Specific Categories and the Role of Universal Ontological Knowledge in the Construal of Individuation 429 Mutsumi Imai and Reiko Mazuka 15 Interaction of Language Type and Referent Type in the Development of Nonverbal Classification Preferences 465 John A. Lucy and Suzanne Gaskins 16 Thought before Language: Do We Think Ergative? 493 Susan Goldin-Meadow. (shrink)

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its performance (...) can be related to human performance (e.g., solution trajectory, error patterns, time‐on‐task). Furthermore, CAB's bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB's strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. (shrink)