Optimal decision-making theories: linking neurobiology with behaviour.

Scientific Abstract

This article reviews recently proposed theories postulating that, during simple choices, the brain performs statistically optimal decision making. These theories are ecologically motivated by evolutionary pressures to optimize the speed and accuracy of decisions and to maximize the rate of receiving rewards for correct choices. This article suggests that the models of decision making that are proposed on different levels of abstraction can be linked by virtue of the same optimal computation. Also reviewed here are recent observations that many aspects of the circuit that involves the cortex and basal ganglia are the same as those that are required to perform statistically optimal choice. This review illustrates how optimal-decision theories elucidate current data and provide experimental predictions that concern both neurobiology and behaviour.

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Optimal decision-making theories: linking neurobiology with behaviour.

Scientific Abstract

This article reviews recently proposed theories postulating that, during simple choices, the brain performs statistically optimal decision making. These theories are ecologically motivated by evolutionary pressures to optimize the speed and accuracy of decisions and to maximize the rate of receiving rewards for correct choices. This article suggests that the models of decision making that are proposed on different levels of abstraction can be linked by virtue of the same optimal computation. Also reviewed here are recent observations that many aspects of the circuit that involves the cortex and basal ganglia are the same as those that are required to perform statistically optimal choice. This review illustrates how optimal-decision theories elucidate current data and provide experimental predictions that concern both neurobiology and behaviour.

Citation

2007.Trends Cogn. Sci. (Regul. Ed.), 11(3):118-25.

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Liebana Garcia S, Laffere A, Toschi C, Schilling L, Podlaski J, Fritsche M, Zatka-Haas P, Li Y, Bogacz R, Saxe A, Lak A

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