Proceedings of the Annual Meeting of the Cognitive Science Society

How do we learn non-physical properties of physical objects? We explored how the statistical structure of eventscan be a source of object property learning. Twenty-five participants saw sequences of visual events surrounding two distinctobjects. Object identity determined 1) the direction of a high transition probability between two events, and 2) the frequency oftwo other events. Learning was unsupervised and unguided. However, participants spontaneously noticed these regularities. Inan explicit, verbal judgment task, they discriminated between frequent vs. rare events (t(24) = 10.7, p< 0.00001) and betweenpredictive vs. non-predictive event pairs (t(24) = 3.04, p<0.01), as appropriate to the object. These statistics gave rise to distinctconceptual interpretations: participants ascribed a causal interpretation to the predictive statistics (t(24) = 1.91, p<0.05) morethan to events frequently co-occurring with the objects (t(24) = 3.00, p<0.01). Such learning may underlie concept acquisition,particularly of functional kinds like artifacts.