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Area of Science:

  • Cognitive psychology
  • Neuroscience
  • Computational modeling

Background:

  • Humans often repeat action sequences that yield rewards.
  • Psychological theories suggest action selection may also be influenced by repetition history, irrespective of reward.
  • Empirical evidence for this 'repetition bias' in value-based decision-making is limited.

Purpose of the Study:

  • To provide empirical evidence for a repetition bias in action sequences.
  • To investigate the influence of this bias on human value-based decision-making.
  • To explore the interplay between goal-directed reward seeking and repetition tendencies.

Main Methods:

  • A sequential decision-making task was employed with 70 human participants.
  • Computational modeling was used to analyze choice behavior.
  • Model comparison was utilized to determine the best explanatory model of decision-making.

Main Results:

  • Evidence for a repetition bias in action sequences was demonstrated.
  • Computational modeling confirmed the learning and influence of this bias on decisions.
  • Decisions were best explained by a combination of reward-seeking and repetition bias.
  • Significant individual differences in repetition bias strength were observed.

Conclusions:

  • Human decision-making is influenced by both reward-driven learning and a tendency to repeat action sequences.
  • The findings support the integration of repetition bias into models of value-based choice.
  • Further research is warranted on the interaction between goal-directed behavior and habitual action repetition.