This paper presents a new computational framework where both ‘the extrinsic reward from the external goal or cost’ and ‘the in- trinsic reward from multiple emotion circuits and drives’ play an integral role in learn- ing and decision making. We show that the integration of the intrinsic reward from affect systems can be used for enhancing the efficacy of learning and decision mak- ing. In particular, we suggest a model of the affective anticipatory reward that is as- sumed to arise from the emotional seeking system. Our simulation results for a single- step choice and sequential multi-step choices show that affective biases from affective an- ticipatory rewards can be applied for im- proving the speed of learning, regulating the trade-off between exploration and exploita- tion in learning more efficiently, and adjust- ing the weight given to the immediate re- wards over the future rewards in obtaining a decision making policy.
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