Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to (...) all other words in the network and a word's local feature distinctiveness is measured relative to words in sub‐networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan. (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 study investigated the effect of unconscious processing on creative problem solving (CPS) by combining a revised priming paradigm that manipulated the content of unconscious processing together with remote association tests (RAT). In the real world, most CPS is facilitated by unconscious information processing after a problem is represented. However, most previous studies have focused on priming subthreshold stimuli before the problem presentation, with few exploring unconscious priming after the problem is presented, even though different prime timings might involve distinct (...) processing mechanisms. Therefore, Experiment 1 compared the unconscious priming effects of presenting the prime prior to the target (Before-T) versus during target solving (During-T). The RAT difficulty and type of priming material were also manipulated. The results of Experiment 1 revealed a significant difference in the priming effects of prime timing with the During-T group benefiting more from subliminal stimuli than the Before-T group. Experiment 2 further strengthened the justifications for the hypotheses regarding the influence of word versus picture priming in the during-T condition. Overall, this study found a clear dissociable contribution of unconscious processing to CPS, and the facilitation effect was optimised by prime timing, the prime material, and RAT difficulty. (shrink)

This thesis is a contribution toward an integral cognitive theory of memory and technology. It, furthermore, develops a theory and prototype for technologically extending mind (memory and thinking).

. 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)

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)

The adequacy of similarity to prototype as an account of categorization in natural concepts was assessed by analyzing the monotonicity of the relation between typicality of an item in a category and the probability of a positive categorization response using data from McCloskey and Glucksberg (1978). The analysis revealed a strong underlying similarity-based threshold curve, with systematic deviations. Further data collection showed that deviations from the curve could be attributed to the effects of unfamiliarity and non-categorial associations on typicality judgments, (...) as well as differences between the perceptual appearance of an item (which tended to boost typicality) and its underlying nature (which tended to boost categorization). The results are discussed in terms of the different presuppositions and task constraints involved in rating typicality as opposed to performing a categorization. (shrink)

A taxonomy of part‐whole or meronymic relations is developed to explain the ordinary English‐speaker's use of the term “part of” and its cognates. The resulting classification yields six types of meronymic relations: 1. component‐integral object (pedal‐bike), 2. member‐collection (ship‐fleet), 3. portion‐mass (slice‐pie), 4. stuff‐object (steel‐car), 5. feature‐activity (paying‐shopping), and 6. place‐area (Everglades‐Florida). Meronymic relations ore further distinguished from other inclusion relations, such as spatial inclusion, and class inclusion, and from several other semantic relations: attribution, attachment, and ownership. This taxonomy is (...) then used to explain cases of apparent intransitivity in merological syllogisms, and standard form syllogisms whose premises express different inclusion relations. The data suggest that intransitivities arise due to equivocations between different types of semantic relations. These results are then explained by means of the relation element theory which accounts for the character and behavior of semantic relations in terms of more primitive relational elements. The inferential phenomena observed are then explained by means of a single principle of element matching. (shrink)

This paper proposes the ‘AGENCY model’ of conscious state attribution, according to which an entity's displaying certain relatively simple features (e.g. eyes, distinctive motions, interactive behavior) automatically triggers a disposition to attribute conscious states to that entity. To test the model's predictions, participants completed a speeded object/attribution task, in which they responded positively or negatively to attributions of mental properties (including conscious and non-conscious states) to different sorts of entities (insects, plants, artifacts, etc.). As predicted, participants responded positively to conscious (...) state attributions only for those entities that typically display the simple features identified in the AGENCY model (eyes, distinctive motion, interaction) and took longer to deny conscious states to those entities. (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)

Traditionally, theories of concepts in psychology assume that concepts are a single, uniform kind of mental representation. But no single kind of representation can explain all of the empirical data for which concepts are responsible. I argue that the assumption that concepts are uniformly the same kind of mental structure is responsible for these theories’ shortcomings, and outline a pluralist theory of concepts that rejects this assumption. On pluralism, concepts should be thought of as being constituted by multiple representational kinds, (...) with the particular kind of concept used on an occasion being determined by the context. I argue that endorsing pluralism does not lead to eliminativism about concepts as an object of scientific interest. (shrink)

The concepts to be considered in this chapter are those that occur in everyday common human thought and language – the “natural history” of concepts in use. While many may appear to be constituted by similarity relations, which make them suitable for modelling in conceptual spaces for example, other concepts in everyday use may be differently constituted. These concepts include abstract concepts, essentialist kinds, natural kinds, and logical or mathematical concepts. I discuss the different sources, uses and epistemological bases of (...) these different forms of concept, and propose a three-level hierarchy of concepts based on their sources of validation. (shrink)

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the (...) referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human‐range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory. (shrink)

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

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)

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)

Two experiments are reported which demonstrated intransitivity in category judgments, thus challenging a widely held assumption that the relation between categorized sets is one of class inclusion. Subjects consistently accepted the truth of certain category statements, in spite of being aware of the existence of counterexamples. Implications for semantic memory theory are discussed.

Research has demonstrated that feeling-of-knowing judgments are affected by the amount of accessible information related to an inaccessible target. Further, studies have demonstrated that, in some situations, FOK judgment magnitude is not only related to the amount of accessed features, but also the correctness of those features . The present study examined the conditions under which the correctness of features would influence FOK judgment magnitude. We hypothesized that accuracy of retrieved features would influence FOK judgments, but only in situations where (...) semantically meaningful information was accessible. In three experiments, we manipulated accessibility of semantic information. In all experiments, the quantity, or amount of retrieved partial information had a greater impact on FOK judgments than the accuracy of that information. However, in situations where semantic information was accessible, accuracy of retrieved semantic features also influenced FOK judgment magnitude, and later recognition. (shrink)

We investigate the family of concepts that an agent comes to know through a set of defining features, and examine the role played by these features in the process of categorization. In a qualitative framework, categorial membership is evaluated through an order relation among the objects at hand, which translates the fact that an object may fall more than another under a given concept. For concepts defined by their features, this global membership order depends on the degree with which each (...) feature applies to the objects of the universe. The passage from these individual membership degrees to a global membership order poses a problem analogous to vote aggregation in social choice theory. This similarity leads to an original solution that is particularly well-adapted to the framework of cognitive psychology. The resulting membership order extends to compound concepts, and provides a good description of the guppy paradox and the conjunction effect. (shrink)

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...) the representation of complex concepts. (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)

Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...) of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources. (shrink)

This paper reviews the uneven history of the relationship between Anthropology and Cognitive Science over the past 30 years, from its promising beginnings, followed by a period of disaffection, on up to the current context, which may lay the groundwork for reconsidering what Anthropology and (the rest of) Cognitive Science have to offer each other. We think that this history has important lessons to teach and has implications for contemporary efforts to restore Anthropology to its proper place within Cognitive Science. (...) The recent upsurge of interest in the ways that thought may shape and be shaped by action, gesture, cultural experience, and language sets the stage for, but so far has not fully accomplished, the inclusion of Anthropology as an equal partner. (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)