A Linguistic-Sensorimotor Model of the Basic-Level Advantage in Category Verification

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.