Learned Attention

Unlike many approaches to machine learning, human learning involves selective attention. When confronted by new things to learn, people can rapidly shift attention, thereby increasing speed of acquisition and decreasing interference with previous knowledge. The shift of attention is itself learned, so that attention is allocated to particular cues in particular contexts. While selective attention benefits acquisition, it can also lead to distortions of knowledge that are evident when the knowledge is transferred to novel situations. Several mathematical models have been designed to implement selective attention in learning; the models quantitatively fit human performance in many experiments. This presentation reviews various research projects of the author.