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A reinforcement learning theory for homeostatic regulation

Keramati, M. & Gutkin, B. S. (2011). A reinforcement learning theory for homeostatic regulation. In: Shawe-Taylor, J., Zemel, R. S., Bartlett, P. L. , Pereira, F. & Weinberger, K. Q. (Eds.), Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Advances in Neural Information Processing Systems (24). (pp. 82-90). Neural Information Processing Systems ( NIPS ).


Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition. Negative-feedback models of homeostatic regulation, on the other hand, are concerned with behavioral adaptation in response to the “internal” state of the animal, and assume that animals’ behavioral objective is to minimize deviations of some key physiological variables from their hypothetical setpoints. Building upon the drive-reduction theory of reward, we propose a new analytical framework that integrates learning and regulatory systems, such that the two seemingly unrelated objectives of reward maximization and physiological-stability prove to be identi-
cal. The proposed theory shows behavioral adaptation to both internal and external states in a disciplined way. We further show that the proposed framework allows for a unified explanation of some behavioral pattern like motivational sensitivity of different associative learning mechanism, anticipatory responses, interaction among competing motivational systems, and risk aversion.

Publication Type: Book Section
Subjects: B Philosophy. Psychology. Religion > BF Psychology
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Health & Psychological Sciences > Psychology
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