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)

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)

Jean Mandler proposes an original and richly detailed theory of how concepts relate to sensory and motor capacities. I focus on her claims about conceptual representations and the processes that produce them. On her view, concepts are declarative representations of object kind information. First, I argue that since sensorimotor representations may be declarative, there is no bar to percepts being constituents of concepts. Second, I suggest that concepts track kinds and other categories not by representing kind information per se, but (...) rather by being subject to the appropriate sort of inferential dispositions. These dispositions themselves may apply equally to perceptual and non-perceptual representations. Third, I argue that Mandler's proposed redescriptive mechanism for producing conceptual primitives can be viewed as a kind of Fodorian triggering device. Hence there may be less distance between her view and Fodor's than either one has supposed. I suggest that redescription needs to be supplemented with several other kinds of more flexible and open-ended concept learning mechanisms. Finally, I briefly sketch the view of conceptual development that results from adopting these proposals and contrast it with Mandler's. (shrink)

Recently, concerns have been raised about the phenomenon of ‘overdiagnosis’, the diagnosis of a condition that is not causing harm, and will not come to cause harm. Along with practical, ethical, and scientific questions, overdiagnosis raises questions about our concept of disease. In this paper, we analyse overdiagnosis as an epistemic problem and show how it challenges many existing accounts of disease. In particular, it raises ques- tions about conceptual links drawn between disease and dysfunction, harm, and risk. We argue (...) that ‘disease’ should be considered a vague concept with a non-classical structure. On this view, overdiagnosed cases are ‘borderline’ cases of disease, falling in the zone between cases that are clearly disease, and cases that are clearly not disease. We then develop a pre ́cising definition of disease designed to provide practical help in preventing and limiting overdiagnosis. We argue that for this purpose, we can define disease as dysfunction that has a significant risk of causing severe harm to the patient. (shrink)

In this paper, we examine recent critiques of the debate about defining disease, which claim that its use of conceptual analysis embeds the problematic assumption that the concept is classically structured. These critiques suggest, instead, developing plural stipulative definitions. Although we substantially agree with these critiques, we resist their implication that no general definition of “disease” is possible. We offer an alternative, inductive argument that disease cannot be classically defined and that the best explanation for this is that the concept (...) is structured as a cluster. We further argue that we do not need to reject the possibility of defining the general concept “disease” to legitimate developing stipulative definitions of disease that are relative to particular purposes. A cluster definition of disease is compatible with contextually motivated definitions, which may be considered précisifications of the more general cluster concept. (shrink)

Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences (...) between features. These representations are used as input for the second module, the Context Module, which categorizes contexts based on object co-occurrences. Feedback connections from the Context Module to the Object Module send activation from the active context to those objects that frequently occur within this context. We demonstrate that context feedback contributes to the successful categorization of objects, especially when bottom-up feature information is degraded or ambiguous. (shrink)

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)

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)

Understanding novel phrases (e.g. upside‐down daisy) and classifying objects in categories named by phrases ought to have common properties, but you'd never know it from current theories. The best candidate for both jobs is the Theory Theory, but it faces difficulties when theories are impoverished. A potential solution is a dual approach that couples theories (representations‐about categories) with fixed mentalese expressions (representations‐of categories). Both representations combine information in parallel when understanding phrases. Although there are objections to the notion that theories (...) can combine, some of them lose their force when applied to a suitable dual model. (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)

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic‐level concepts (carrot). A feature‐based attractor network with a single layer of semantic features developed representations of both basic‐level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat (...) attractor network. Experiment and Simulation 2 show that, as with basic‐level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. (shrink)