Dynamics of individual perceptual decisions

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Decision Science

Background:

• Perceptual decision making is vital across many disciplines.
• Existing research has advanced understanding, but decision-making dynamics (time/frequency) remain unclear.
• This review focuses on human findings, incorporating animal data where relevant.

Purpose of the Study:

• To synthesize current knowledge on perceptual decision-making dynamics.
• To contextualize disparate findings using formal dynamic analysis and published models.
• To propose a unified framework for understanding decision-making processes.

Main Methods:

• Review of human and animal studies on perceptual decision making.
• Application of dynamic analysis to existing computational models.
• Integration of concepts like high-pass filters and evidence accumulators.

Main Results:

• Dynamic analysis can reconcile seemingly unrelated findings in perceptual decision making.
• Existing models can be enhanced with dynamic mechanisms (e.g., high-pass filters) to explain diverse data.
• Disparate findings may be explained by incorporating phasic neural mechanisms, evidence accumulators, or mid-trial decision mechanisms.

Conclusions:

• Perceptual decision making involves dynamic neural processes.
• Hypothesized mechanisms include phasic neural activity, evidence accumulation, and mid-trial decision circuits (e.g., peak detectors, decision boundaries).
• A dynamic systems approach offers a unified perspective on decision-making research.