Event category learning

TL;DR: This paper investigated the learning of event categories, in particular, categories of simple animated events, each involving a causal interaction between two characters, and found that correlations among attributes of events are easier to learn when they form part of a rich correlational structure than when they are independent of other correlations.

Abstract: This research investigated the learning of event categories, in particular, categories of simple animated events, each involving a causal interaction between 2 characters. Four experiments examined whether correlations among attributes of events are easier to learn when they form part of a rich correlational structure than when they are independent of other correlations. Event attributes (e.g., state change, path of motion) were chosen to reflect distinctions made by verbs. Participants were presented with an unsupervised learning task and were then tested on whether the organization of correlations affected learning. Correlations forming part of a system of correlations were found to be better learned than isolated correlations. This finding of facilitation from correlational structure is explained in terms of a model that generates internal feedback to adjust the salience of attributes. These experiments also provide evidence regarding the role of object information in events, suggesting that this role is mediated by object category representations.