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Poster B43, Wednesday, November 8, 3:00 – 4:15 pm, Harborview and Loch Raven Ballrooms

How using concepts changes them: A graph theory approach

Yoed N. Kenett1, Zareh Kaloustian1, Sharon L. Thompson-Schill1;1University of Pennsylvania

The generative capacity of language entails an ability to flexibly combine concepts with each other. Investigating how individuals combine concepts can shed unique light on different aspects of conceptual knowledge, including the cognitive mechanisms that enable the generative and flexible use of language. Conceptual combination can occur either by using an attribute of one concept to describe another (attributive combination) or by forming some relation between two concepts to create a new one (relational combination). For example, while some interpret the noun-noun combination whale boat as “a large boat”, applying the attribute “large” of the whale to the boat; others interpret this combination as “a boat that hunts whales”, applying a thematic, relational role between whales and boats. Prior research has addressed whether common or distinct processes support these two putatively different types of combinations. We turn the question around and ask whether the consequences of these combination types on our conceptual system might differ, by comparing semantic memory networks before and after participants perform either attributive or relational conceptual combinations. We characterized the semantic network of participants using their free association responses to 50 cue words taken from five semantic categories (such as animals or fruits and vegetables). These association responses were obtained twice, before and after completing a conceptual combination task that was biased to elicit either attributive or relational interpretations to half of these cue words. Semantic networks of these 50 cue words were computed based on association correlations – the overlap of association responses between any pair of cue words. With this procedure, we were able to assess the main effect, within subjects, of conceptual combination (by comparing the structure of the semantic networks at both time points) as well as the interaction, between subjects, of the type of conceptual combination on network change. We find a general effect on the semantic networks: The structure of network decreases after participants conceptually combine some of the concepts in the network. However, the relational combination manipulation has a greater effect. Furthermore, only the relational combination manipulation leads to an increase in the network’s connectivity. Overall, our results demonstrate that semantic networks can be applied to study group-level effects of different conceptual combination mechanisms and indicate that the relational combination manipulation has a greater effect on semantic memory structure than an attributive combination manipulation. Our findings are in line with current theories of semantic memory, which view it as a dynamic system. Such theories argue that both context (task demands) and individual differences (processing style) lead to short- and long-term changes in semantic memory structure. We show how manipulating concepts in the semantic network (through a conceptual combination manipulation) changes the structure of the network. Thus, the work reported here is a first step at harnessing computational network science to investigate the effects of different conceptual combination mechanisms on semantic memory structure. It demonstrates the efficacy of applying semantic network analysis in understanding high-level cognition.

Topic Area: Meaning: Lexical Semantics

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