Using Distributional Measures to Model Typicality in Categorization

Using Distributional Measures to Model Typicality in Categorization Louise Connell (louise.connell@ucd.ie) Department of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland Michael Ramscar (michael@cogsci.ed.ac.uk) School of Cognitive Science, University of Edinburgh, 2 Buccleuch Place, Edinburgh, EH8 9LW, Scotland. Abstract Typicality effects are ordinarily tied to concepts and conceptualization. The underlying assumption in much of categorization research is that effects such as typicality are reflective of stored conceptual structure. This paper questions this assumption by simulating typicality effects by the use of a co-occurrence model of language, Latent Semantic Analysis (LSA). Despite being a statistical tool based on simple word co- occurrence, LSA successfully simulates subject data relating to typicality effects and the effects of context on categories. Moreover, it does so without any explicit coding of categories or semantic features. The model is then used to successfully predict participants’ judgements of typicality in context. In the light of the findings reported here, we question the traditional interpretation of typicality data: are these data reflective of underlying structure in people’s concepts, or are they reflective of the distributional properties of the linguistic environments in which they find themselves. Introduction The world contains a myriad of objects and events that an intelligent observer could seemingly infinitely partition and generalise from. So how it is that humans can adopt a particular partitioning in the mass of data that confronts them? How do they pick out regularities in the stuff of experience and index them using words? What are these regularities? And how do humans recognise, communicate, learn and reason with them? These questions are central to cognitive science, and traditionally, their close linkage has tempted researchers to seek a unified answer to them: categorization – the act of grouping things in the world – has been commonly linked to the representation of concepts 1 , with many researchers assuming that a theory of one provides for the other (Armstrong, Gleitman & Gleitman, 1983; Keil, 1987; Lakoff, 1987). In the experiments reported, we follow the common assumption (Medin & Smith, 1984; Komatsu, 1992) that categories are classes, concepts are their mental representations and that an instance is a specific example of a category member. In much of this work, it is assumed that linguistic behavior (such as naming features associated with a concept, c.f. Rosch, 1973) is determined by, and reflective of, underlying concepts that are grounded in perceptual experience of objects and artifacts themselves. Here, we wish to consider the idea that language itself is part of the environment that determines conceptual behavior. A growing body of research indicates that distributional information may play a powerful role in many aspects of human cognition. In particular, it has been proposed that people can exploit statistical regularities in language to accomplish a range of conceptual and perceptual learning tasks. Saffran, Aslin & Newport (1996; see also Saffran, Newport, & Aslin; 1996) have demonstrated that infants and adults are sensitive to simple conditional probability statistics, suggesting one way in which the ability to segment the speech stream into words may be realized. Redington, Chater & Finch (1998) suggest that distributional information may contribute to the acquisition of syntactic knowledge by children. MacDonald & Ramscar (this volume) have shown how information derived from a 100 million word corpus can be used to manipulate subjects’ contextual experience with marginally familiar and nonce words, demonstrating that similarity judgements involving these words are affected by the distributional properties of the contexts in which they were read. The objective of this paper is to examine the extent to which co-occurrence techniques can model human categorization data: What is the relationship between typicality judgements and distributional information? Indeed, are the responses people provide in typicality experiments more reflective of the distributional properties of the linguistic environments in which they find themselves than they are of the underlying structure of people's concepts? Typicality Effects The first empirical evidence of typicality effects was provided by Rosch (1973), who found participants judged some category members as more (proto)typical than others. Rosch (1973) gave subjects a category name such as fruit with a list of members such as apple,