Human preferences are biased towards associative information

Sabrina Trapp, Amitai Shenhav, Sebastian Bitzer, Moshe Bar

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

There is ample evidence that the brain generates predictions that help interpret sensory input. To build such predictions the brain capitalizes upon learned statistical regularities and associations (e.g., “A” is followed by “B”; “C” appears together with “D”). The centrality of predictions to mental activities gave rise to the hypothesis that associative information with predictive value is perceived as intrinsically valuable. Such value would ensure that this information is proactively searched for, thereby promoting certainty and stability in our environment. We therefore tested here whether, all else being equal, participants would prefer stimuli that contained more rather than less associative information. In Experiments 1 and 2 we used novel, meaningless visual shapes and showed that participants preferred associative shapes over shapes that had not been associated with other shapes during training. In Experiment 3 we used pictures of real-world objects and again demonstrated a preference for stimuli that elicit stronger associations. These results support our proposal that predictive information is affectively tagged, and enhance our understanding of the formation of everyday preferences.

Original languageEnglish
Pages (from-to)1054-1068
Number of pages15
JournalCognition and Emotion
Volume29
Issue number6
DOIs
StatePublished - 18 Aug 2015

Bibliographical note

Publisher Copyright:
© 2014 Taylor & Francis.

Keywords

  • Affect
  • Novelty
  • Perception
  • Prediction
  • Preference
  • Statistical learning

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