Evidence accumulation is biased by motivation: A computational account

PLoS Comput Biol. 2019 Jun 27;15(6):e1007089. doi: 10.1371/journal.pcbi.1007089. eCollection 2019 Jun.


To make good judgments people gather information. An important problem an agent needs to solve is when to continue sampling data and when to stop gathering evidence. We examine whether and how the desire to hold a certain belief influences the amount of information participants require to form that belief. Participants completed a sequential sampling task in which they were incentivized to accurately judge whether they were in a desirable state, which was associated with greater rewards than losses, or an undesirable state, which was associated with greater losses than rewards. While one state was better than the other, participants had no control over which they were in, and to maximize rewards they had to maximize accuracy. Results show that participants' judgments were biased towards believing they were in the desirable state. They required a smaller proportion of supporting evidence to reach that conclusion and ceased gathering samples earlier when reaching the desirable conclusion. The findings were replicated in an additional sample of participants. To examine how this behavior was generated we modeled the data using a drift-diffusion model. This enabled us to assess two potential mechanisms which could be underlying the behavior: (i) a valence-dependent response bias and/or (ii) a valence-dependent process bias. We found that a valence-dependent model, with both a response bias and a process bias, fit the data better than a range of other alternatives, including valence-independent models and models with only a response or process bias. Moreover, the valence-dependent model provided better out-of-sample prediction accuracy than the valence-independent model. Our results provide an account for how the motivation to hold a certain belief decreases the need for supporting evidence. The findings also highlight the advantage of incorporating valence into evidence accumulation models to better explain and predict behavior.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Computational Biology
  • Computer Simulation
  • Female
  • Humans
  • Judgment / physiology*
  • Male
  • Models, Psychological*
  • Motivation / physiology*
  • Psychometrics
  • Reward*