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Area of Science:

  • Cognitive psychology
  • Neuroscience
  • Decision science

Background:

  • Decision-making involves a trade-off between acquiring information and the cost of delay.
  • Natural environments present challenges with unpredictable information reliability (heteroscedasticity).

Purpose of the Study:

  • To model how humans decide when to stop gathering information and commit to a choice.
  • To investigate adaptive strategies for decision-making under variable information reliability.

Main Methods:

  • Human participants performed a categorization task with sequential, continuously valued sensory samples.
  • Behavior was modeled using a system that adaptively weighted signals by inverse prediction error and integrated with urgency.
  • The model's performance was compared against a Bayesian ideal observer and neural data.

Main Results:

  • Human decision-making behavior was accurately captured by a model incorporating adaptive signal weighting and urgency.
  • This model approximated Bayesian optimal decision-making and predicted neural signal adaptations.
  • The findings suggest a mechanism for optional stopping in decision-making.

Conclusions:

  • Adaptive weighting of sensory information, modulated by an urgency signal, is a key strategy for efficient decision-making.
  • This mechanism is crucial for navigating environments with unpredictable information quality (heteroscedasticity).
  • Such adaptive strategies may have evolved to optimize decision-making under natural conditions.