Semantic Packing As a Core Mechanism of Category Coherence, Fast Mapping and Basic Level Categories - eScholarship

Semantic Packing As a Core Mechanism of Category Coherence, Fast Mapping and Basic Level Categories Shohei Hidaka (hidaka@cog.ist.i.kyoto-u.ac.jp) Graduate School of Informatics, Kyoto University and JSPS Research Fellow Jun Saiki (saiki@cv.jinkan.kyoto-u.ac.jp) Graduate School of Human and Environmental Studies, Kyoto University Linda B. Smith (smith4@indiana.edu) Department of Psychological and Brain Sciences, Indiana University to solve the coherence problem. Insead, they suggested that categorization is based on folk theories and causal induction, or what some call “theory theory”. However, their major criticism of similarity-based accounts, circu- larity, is also a problem for theory theories. To what theory to apply to any thing – a theory about food and the relevance of color or a theory about artifacts and the nonerelevance of color – one has to already know what kind of thing is at hand. Abstract In the present study, category coherence, a question why intuitive groupings for natural categories exist, is con- sidered by applying a computational theory that models discrimination and generalization. The computational process is what we called “semantic packing”. In the model, category learners’ two conﬂicting constraints on discrimination and generalization are optimized simul- taneously as if it packed knowledge into memory. The model also revealed a computational structure of eﬃ- cient categories, called basic categories, by mathemati- cal proof and exempliﬁcation of the relationship between the past proposed theory and our theory. Furthermore, the empirical evidence of the theory from human seman- tic rating was shown and used for testing predictability of novel natural categories. The results suggest that semantic packing could reproduce the conﬁguration of natural categories from only their generalization with- out any knowledge as fast mapping in which children can generalize a novel instance without trial and error. In summary, the semantic packing could be a core mecha- nism of the three essential categorization processes, cat- egory coherence, fast mapping, and basic level category. The Outline Introduction Why Does “Category Coherence” Emerge? Murphy and Medin (1985) deﬁne “category coherence” as the intuitive and useful groupings that characterize natural categories, and claim that this coherence is one of most important aspects of semantic cognition. For ex- ample, color is more important for discrimination when the item is a pea rather than when it is a ball. How do we learn this? And how do we use the knowledge that we have learned? How, when we see an object-a potential pea or ball-do we know to attend to color or not? This problem of feature selection has played a key role in theoretical discussions of the mechanisms that under- lie category learning. In past studies, some measures of a learner’s category representation were proposed to ex- plain the basic level category advantage. The basic cat- egory is known as the most eﬃcient level for various cog- nitive processes, such as picture naming, category or fea- ture listing, and speech frequency (Rosch, Mervis, Gray, Johnson & Boyes-Braem, 1976). This concerns the rela- tive frequency of features or categories, thus, these mea- sures depend on what features or categories are selected (Murphy and Lassaline, 1997). For these and other rea- sons, Murphy and Medin (1985) claimed that categoriza- tion based only similarity and correlations is not enough The main goal of this study is to propose a computa- tional theory that connects the emergence of category coherence, basic level categories, and fast mapping (see also the next paragraph). To this end, we propose two metrics. The ﬁrst is what we call smoothness. This con- cerns the relationship between features and generaliza- tion of categories found in developmental studies and is a measure how cohesive categories are. Categorization with the smooth feature space makes a novel instance generalized precisely, which is considered as fast map- ping. The second metric concerns discrimination, which yields category coherence as the result. There is an es- sential trade-oﬀ between generalization and discrimina- tion. Both cannot be maximized simultaneously. The joint optimization is obtained by what we call “semantic packing”. This process is analogous to a task in which various sized and shaped containers (categories) are eﬃ- ciently packed into a larger but ﬁnite container (memory and the attentional and retrieval processes that apply to memory). The packing process is related to processes such as cue validity (Rosch et al. 1976) and category util- ity (Corter & Gluck, 1992), which are measures for basic level categories. We ﬁrst present the theory metaphor- ically and then the formal mathematical speciﬁcation later. Working Deﬁnition: “Semantic Smoothness” in Natural Categories Many developmental studies using novel word general- ization tasks have shown that children systematically attend to diﬀerent properties when generalizing diﬀer- ent types of entities, a process known as “fast map- ping”. For example, children generalized solid artifacts and non-solid substances based on the similarity of shape and material, respectively (Soja, Carey & Spelke, 1991). Therefore, children seem to solve the circularity problem,