AAAI Publications, 2017 AAAI Fall Symposium Series

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An Integrated Computational Framework for Attention, Reinforcement Learning, and Working Memory
Andrea Stocco

Last modified: 2017-10-09


This paper proposes a reinterpretation of selective attention as a form of control of working memory based on self-generated reward signals and model-free reinforcement learning. In addition to being simple and parsimonious, this approach systematizes a number of classic psychological constructs without calling for additional, specific mechanisms. Finally, the papers presents the results of an empirical test of this framework, and elaborates on the implications of our findings for general models of control and intelligent behavior, as well as neurobiological models of the basal ganglia.


Reinforcemente Learning; Cognitive Architectures; Cognitive Modeling; Dopamine; Basal Ganglia

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