Abstract
People are generally more accurate at categorizing objects at the basic level (e.g., dog) than at more general, superordinate categories (e.g., animal). Recent research has suggested that this basic-level advantage emerges from the linguistic-distributional and sensorimotor relationship between a category concept and object concept, but the proposed mechanisms have not been subject to a formal computational test. In this paper, we present a computational model of category verification that allows linguistic distributional information and sensorimotor experience to interact in a grounded implementation of a full-size adult conceptual system. In simulations across multiple datasets, we demonstrate that the model performs the task of category verification at a level comparable to human participants, and—critically—that its operation naturally gives rise to the basic-level-advantage phenomenon. That is, concepts are easier to categorize when there is a high degree of overlap in sensorimotor experience and/or linguistic distributional knowledge between category and member concepts, and the basic-level advantage emerges as an overall behavioral artifact of this linguistic and sensorimotor overlap. Findings support the linguistic–sensorimotor preparation account of the basic-level advantage and, more broadly, linguistic–sensorimotor theories of the conceptual system.

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