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Abstract

Near-scale environments, like work desks, restaurant place settings or lab benches, are the interface of our hand-based interactions with the world. How are our conceptual representations of these environments organized? What properties distinguish among reachspaces, and why? We obtained 1.25 million similarity judgments on 990 reachspace images, and generated a 30-dimensional embedding which accurately predicts these judgments. Examination of the embedding dimensions revealed key properties underlying these judgments, such as reachspace layout, affordance, and visual appearance. Clustering performed over the embedding revealed four distinct interpretable classes of reachspaces, distinguishing among spaces related to food, electronics, analog activities, and storage or display. Finally, we found that reachspace similarity ratings were better predicted by the function of the spaces than their locations, suggesting that reachspaces are largely conceptualized in terms of the actions they support. Altogether, these results reveal the behaviorally-relevant principles that structure our internal representations of reach-relevant environments.