This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization.

This paper presents a computational model of how category specific semantic deficits resulting from two types of brain injury can arise in a single semantic system without explicit category representations. Category specific impairments resulting from focal brain damage have been explained by predominant damage to a specific type of feature: perceptual in the case of natural kinds, functional in the case of artifacts. Perceptual features are assumed to be stored in temporolimbic areas while functional features are stored in frontoparietal regions. Damage localized to either region preferentially disrupts the semantic information in that region resulting in a category specific impairment. Recent reports of category specific impairments in Alzheimer's disease, a pathology of widespread, patchy damage affecting both temporolimbic and frontoparietal regions, raises the question: Can a single theory explain category specific impairments arising from these highly different pathologies? Mod...

It has been claimedthat concepts in differentsemantic domains vary in the extent to which their meaning is comprised of different kinds of semantic information. Discussion has mainly focused around two kinds of concepts—living things and man-made objects—arguing that functional information is central to the meaning of artefacts whereas perceptual information is more important for the meaning of living things. This distinction has been important in accounting for patterns of semantic impairments following brain injury (Warrington & Shallice, 1984). We suggest that functional information may be especially salient in the semantic representations of both living and nonliving things. Our evidence for this claim comes from priming studies with normal subjects, and data from brain-damaged patients that supports the claim that functional information is relatively spared following brain damage. We explore further implications of the role of functional properties in semantic representations,considering distinctions between different types of functional information in the representationof living things. We focus on the developmental claim that biological functional information,

Quantities are ubiquitous and an important part of our understanding about the world -- we talk of engine horsepower, size, mileage, price of cars; GDP, population, area of countries; wingspan, weight, surface area of birds, and so on. In this paper, we present cognitively plausible symbolic representations of quantity and principles for generating those representations. Bringing together evidence in linguistics and psychology, we argue that our representations must make two kinds of distinctions -- dimensional, those that denote changes of quantity, e.g., large and small; and structural, those that denote changes of quality, e.g. boiling point and poverty line.

A fundamental question in research on conceptual structure concerns how information is represented in memory and used in tasks such as recognizing words. The present research focused on the role of correlations among semantic properties in conceptual memory. Norms were collected for 190 entities from 10 categories. Property intercorrelations influenced people's performance in both a property verification task and a short interval semantic priming experiment. Furthermore, correlated properties were more important for biological kinds than for artifacts. A connectionist model of the computation of word meaning was implemented in which property intercorrelations developed in the course of learning. The model was used to simulate the results of the two experiments. We then tested a novel prediction derived from the model: that the intercorrelational density of a concept's properties should influence the speed with which a concept is computed. This prediction was confirmed in a final experi...

Quantities are ubiquitous and an important part of our understanding about the world – we talk of engine horsepower, size, mileage, price of cars; GDP, population, area of countries; wingspan, weight, surface area of birds, and so on. In this paper, we present a sketch of a theory of quantity – cognitively sound representations and principles for generating those representations. We present evidence from psychology, natural language, and ecological constraints to argue for a cognitively plausible representation of quantities. We then propose a general principle of how to make the necessary and relevant distinctions. Structured models of retrieval, similarity, and generalization, and in general models involving symbolic representations, do not handle quantities adequately. That is an artifact of poor representations of quantity, and we believe that the representations proposed here will make these models more quantity-aware. This investigation is at the intersection of qualitative reasoning, cognitive psychology, and linguistics, and builds on existing evidence in these fields to potentially contribute to the understanding of quantities in all the three. 1

The ability to categorize and use concepts e#ectively is a basic requirementofany intelligent actor. The utility-based approach to categorization is founded on the thesis that categorization is fundamentally in service of action, i.e., the choice of concepts made by an actor is critical to its choice of appropriate actions. This is in contrast to classical and similarity-based approaches which seek logical completeness in concept description with respect to sensory data rather than action-oriented e#ectiveness. Utility-based categorization is normative and not descriptive. It prescribes howanintelligent agent ought to conceptualize to act e#ectively. It provides ideals for categorization, speci#es criteria for the design of e#ective computational agents, and provides a model of ideal competence. A decision-theoretic framework for utilitybased categorization whichinvolves reasoning about alternative categorization models of varying levels of abstraction is proposed. Categorization mode...

We specify a model for the conceptual interpretation of relative adjectives (like “big”), which covers a crucial aspect of the underlying comprehension process – the comparison to a norm that is associated with a comparison class. Building on an elaborate domain ontology and knowledge about intercorrelations, comparison classes are dynamically created depending on the context in which adjectival utterances occur. To appear in: Cognitive Science ' 98 — Proc. of the 20th Annual Meeting of the Cognitive

The work described in this chapter is motivated by the conviction that a cognitive theory of semantic memory is best-suited to investigate the functional and neural bases of the semantic memory system. The advantage of this approach is that detailed hypotheses about the structure and function of the semantic system can be formulated and then tested in behavioral experiments with healthy individuals and neurologically impaired patients. The challenge is then to identify the neural correlates of these experimentally validated cognitive structures and processes, i.e., their neural substrates and mechanisms. The cognitive model provides a detailed framework for this investigation which, when combined with the appropriate functional-neuroanatomical technique, provides the potential to meet this challenge.