Adaptive sampling of information in perceptual decision-making

Area of Science:

• Cognitive psychology
• Decision science
• Perceptual decision-making

Background:

• Humans often make decisions by integrating information from multiple, serially sampled sources.
• These information sources can vary in quality and introduce noise into the decision-making process.
• Fixed time constraints are common in real-world decision scenarios.

Purpose of the Study:

• To derive the optimal decision procedure for serial sampling from noisy information sources under fixed time constraints.
• To investigate how accuracy is maximized in two-alternative choice tasks with varying information quality.
• To compare human decision-making strategies with the derived optimal procedure.

Main Methods:

• Derivation of an optimal decision rule for serial information sampling.
• Experimental design involving a perceptual decision-making task with random dot motion patterns.
• Measurement of human observers' time allocation to different information sources.

Main Results:

• The optimal observer strategy allocates sampling time proportionally to the noise levels of information sources.
• Human observers allocated more time to noisier information sources, correlating with their sensory uncertainty.
• Observed human behavior deviated from the optimal predictions, indicating additional influencing factors.

Conclusions:

• Optimal decision-making under time constraints requires balancing information quality and sampling time.
• Human sampling strategies are influenced by factors beyond immediate source quality, including internal uncertainty.
• Further research is needed to understand the full range of factors affecting information sampling in decision-making.