A decision task in a social context: Human experiments, models, and analyses of behavioral data

Andrea Nedic, Damon Tomlin, Philip Holmes, Deborah A. Prentice, J. D. Cohen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


To investigate the influence of information about fellow group members in a constrained decision-making context, we develop four two-armed bandit tasks in which subjects freely select one of two options (A or B) and are informed of the resulting reward following each choice. Rewards are determined by the fraction x of past A choices by two functions fA(x),fB(x) (unknown to the subject) which intersect at a matching point x that does not generally represent globally optimal behavior. Playing individually, subjects typically remain close to the matching point, although some discover the optimum. Each task is designed to probe a different type of behavior, and subjects work in parallel in groups of five with feedback of other group members' choices, of their rewards, of both, or with no knowledge of others' behavior. We employ a soft-max choice model that emerges from a drift-diffusion process, commonly used to model perceptual decision making with noisy stimuli. Here the stimuli are replaced by estimates of expected rewards produced by a temporal-difference reinforcement-learning algorithm, augmented to include appropriate feedback terms. Models are fitted for each task and feedback condition, and we compare choice allocations averaged across subjects and individual choice sequences to highlight differences between tasks and intersubject differences. The most complex model, involving both choice and reward feedback, contains only four parameters, but nonetheless reveals significant differences in individual strategies. Strikingly, we find that rewards feedback can be either detrimental or advantageous to performance, depending upon the task.

Original languageEnglish (US)
Article number06069518
Pages (from-to)713-733
Number of pages21
JournalProceedings of the IEEE
Issue number3
StatePublished - Mar 2012

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Electrical and Electronic Engineering


  • Decision making
  • drift-diffusion model
  • exploitation
  • exploration
  • group dynamics
  • human behavior
  • reinforcement learning
  • social information
  • two-armed bandit task


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