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Normalized value coding explains dynamic adaptation in the human valuation process

  1. Kenway Louieb,c,1
  1. aDepartment of Economics, Columbia University, New York, NY 10027;
  2. bCenter for Neural Science, New York University, New York, NY 10003;
  3. cInstitute for the Study of Decision Making, New York University, New York, NY 10003
  1. Edited by Randolph Blake, Vanderbilt University, Nashville, TN, and approved October 16, 2017 (received for review August 30, 2017)


Relative processing is a ubiquitous feature of neural and cognitive function. In perception, a prominent example of relative processing is adaptation, in which both sensory neural responses and resulting percepts depend on the history of past stimuli. Neurons in decision-related brain areas also adapt, representing value information relative to previous rewards, but whether adaptive value coding affects behavior is unknown. Here, we show adaptation in the subjective valuation process of human subjects, with values consistently dependent on the recent history of presented values. This adaptive valuation can be explained by divisive normalization, a canonical neural computation widely observed in sensory processing, offering a unifying biological mechanism for temporal context effects in perception and decision making.


The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.


  • ?1To whom correspondence should be addressed. Email: klouie{at}cns.nyu.edu.

Published under the PNAS license.

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