Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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A significant component of human observational learning is the ability to focus attention toward important or relevant input features. Amechanism with this capability can serve as an inductive bias to facilitate learning in both humans and machines. Past attempts to model attentional focus for human learning have postulated a single salience value for each feature, such that features with greater salience command more attention. These models, however, assume that the feature's salience is not dependent on context, whereas studies of human attention show sensitivity to context. This paper presents a mechanism for contextually focused attention in observational learning.