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a neural computational model of incentive salience神经兴奋作用的计算模型
A Neural Computational Model of Incentive Salience
1 1 1 1,2 1
Jun Zhang , Kent C. Berridge *, Amy J. Tindell , Kyle S. Smith , J. Wayne Aldridge
1 Department of Psychology, University of Michigan, Ann Arbor, Michigan, United States of America, 2 McGovern Institute for Brain Research, Massachusetts Institute of
Technology, Cambridge, Massachusetts, United States of America
Abstract
Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli
(cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key
computational question is how incentive salience is generated during a cue re-encounter, which combines both learning
and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for
stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are
also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional
motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs
during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue
value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the
associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and
addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically
rai
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