A Multiple-Well Framework for Human Perceptual Decision-Making

Area of Science:

• Cognitive Science
• Quantum Physics
• Decision Neuroscience

Background:

• Human decision-making involves complex cognitive processes.
• Understanding the role of cognitive control and arousal is crucial.
• Existing models may not fully capture these influences.

Purpose of the Study:

• To present a novel quantum cognitive model integrating cognitive control into perceptual decision-making.
• To quantitatively model the influence of cognitive arousal on task performance, specifically addressing the Yerkes-Dodson law.
• To provide a framework for understanding subjective probabilities and evidence accumulation.

Main Methods:

• Developed a quantum cognitive model using a multiple-square-well potential.
• Each well represents a decision outcome, with depth encoding signal strength and width encoding domain generality.
• Validated the model using the dot motion two-alternative forced-choice (2AFC) task and applied it to the Yerkes-Dodson law.

Main Results:

• The model successfully replicated key empirical findings from the 2AFC task, including correlations between motion coherence and drift rates.
• The model captured the inverted U-shaped relationship between task accuracy and cognitive arousal, characteristic of the Yerkes-Dodson law.
• Comparison of two arousal modeling approaches (eigenenergy vs. kinetic energy) yielded distinct predictions.

Conclusions:

• This work presents the first quantitative model of arousal's impact on human perceptual decision-making.
• The quantum cognitive model provides a robust framework for understanding decision-making under varying cognitive control and arousal levels.
• The study lays the groundwork for precisely defining the functional form of the Yerkes-Dodson law.