Human optional stopping in a heteroscedastic world

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.