From Bayes-optimal to heuristic decision-making in a two-alternative forced choice task with an information-theoretic...

Area of Science:

• Cognitive psychology
• Decision science
• Computational neuroscience

Background:

• Decision-making research often contrasts Bayes-optimal strategies with heuristic approaches.
• Bounded rationality offers a potential framework to unify these seemingly opposing decision-making models.
• Information-processing limitations are key to understanding this unification.

Purpose of the Study:

• To investigate how time constraints influence decision-making strategies in a two-alternative forced choice task.
• To explore whether bounded rationality can bridge Bayes-optimal and heuristic decision-making.
• To model human behavioral changes under varying time pressures.

Main Methods:

• Subjects performed a two-alternative forced choice task involving multi-component symbolic patterns.
• Response behavior and decision weights were analyzed under varying time constraints.
• A bounded rational decision model with adjustable information-processing capacity was fitted to subject data.

Main Results:

• Increased time pressure led to decreased response precision.
• Decision-making shifted from multi-feature weighting (optimal) to single-feature reliance (heuristic) under high time pressure.
• The bounded rational model successfully captured these behavioral shifts.

Conclusions:

• Bounded rationality effectively bridges Bayes-optimal and heuristic decision-making by accounting for information-processing constraints.
• Human decision-making dynamically adapts strategies based on available resources, exhibiting both optimal and heuristic characteristics.
• The proposed model offers a unified account of decision-making under varying cognitive loads.