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Publication Detail
How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Moutoussis M, Dolan RJ, Dayan P
  • Publication date:
    22/07/2016
  • Pagination:
    e1004965
  • Journal:
    PLoS computational biology
  • Volume:
    12
  • Issue:
    7
  • Medium:
    Electronic-eCollection
  • Print ISSN:
    1553-734X
  • Language:
    eng
  • Addresses:
    Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
Abstract
The weight with which a specific outcome feature contributes to preference quantifies a person's 'taste' for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person's choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person's choice variability. Temporal discounting coefficients are an important example of taste-for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others' choices. We examined discounting preferences in a novel, large community study of 14-24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person's choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.
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