. 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)

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...) exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

According to grounded cognition, words whose semantics contain sensory-motor features activate sensory-motor simulations, which, in turn, interact with spatial responses to produce grounded congruency effects. Growing evidence shows these congruency effects do not always occur, suggesting instead that the grounded features in a word's meaning do not become active automatically across contexts. Researchers sometimes use this as evidence that concepts are not grounded, further concluding that grounded information is peripheral to the amodal cores of concepts. We first review broad evidence (...) that words do not have conceptual cores, and that even the most salient features in a word's meaning are not activated automatically. Then, in three experiments, we provide further evidence that grounded congruency effects rely dynamically on context, with the central grounded features in a concept becoming active only when the current context makes them salient. Even when grounded features are central to a word's meaning, their activation depends on task conditions. (shrink)

Concept Pluralism and Concept Eliminativism are two positions recently proposed in the philosophy and the psychology of concepts. Both of these theories are motivated by the view that all current theories of concepts are empirically and methodologically inadequate and hold in common the assumption that for any category that can be represented in thought, a person can possess multiple, distinct concepts of it. In this paper, I will challenge these in light of a third theory, Conceptual Atomism, which addresses and (...) dispels the contentious issues. In particular, I contend that Conceptual Atomism, when properly understood, is empirically adequate and can overcome difficulties that plague Pluralism and Eliminativism. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.

What might a theory of mental imagery look like, and how might one begin formulating such a theory? These are the central questions addressed in the present paper. The first section outlines the general research direction taken here and provides an overview of the empirical foundations of our theory of image representation and processing. Four issues are considered in succession, and the relevant results of experiments are presented and discussed. The second section begins with a discussion of the proper form (...) for a cognitive theory, and the distinction between a theory and a model is developed. Following this, the present theory and computer simulation model are introduced. This theory specifies the nature of the internal representations (data structures) and the processes that operate on them when one generates, inspects, or transforms mental images. In the third, concluding, section we consider three very different kinds of objections to the present research program, one hinging on the possibility of experimental artifacts in the data, and the others turning on metatheoretical commitments about the form of a cognitive theory. Finally, we discuss how one ought best to evaluate theories and models of the sort developed here. (shrink)

This paper describes an operational computer simulation of visual mental imagery in humans. The structure of the simulation was motivated by results of experiments on how people represent information in, and access information from, visual images. The simulation includes a “surface representation,” which is spatial and quasi‐pictorial, and an underlying “deep representation,” which contains “perceptual” information encoding appearance plus “propositional” information describing facts about an object. The simulation embodies a theory of how surface images are generated from deep representations, and (...) how surface images are processed when one accesses information embedded in them. The simulation also offers an account of various sorts of imagery transformations. (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)

Conceptual engineering means to provide a method to assess and improve our concepts working as cognitive devices. But conceptual engineering still lacks an account of what concepts are (as cognitive devices) and of what engineering is (in the case of cognition). And without such prior understanding of its subject matter, or so it is claimed here, conceptual engineering is bound to remain useless, merely operating as a piecemeal approach, with no overall grip on its target domain. The purpose of this (...) programmatic paper is to overcome this knowledge gap by providing some guidelines for developing the theories of concepts and of cognition that will ground the systematic unified framework needed to effectively implement conceptual engineering as a widely applicable method for the cognitive optimization of our conceptual devices. (shrink)

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically (...) identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties—such as clusters, hubs, and the small world—that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK’s principles, using text documents as an illustrative semantic domain. (shrink)