Inclusion of neural effort in cost function can explain perceptual decision suboptimality

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Decision Theory

Background:

• The optimality approach is a valuable framework for understanding decision-making.
• However, current models may not fully capture the complexities of perceptual decision-making.
• Existing models often overlook crucial factors like information processing costs.

Purpose of the Study:

• To propose a more general and powerful optimality approach for modeling perceptual decision-making.
• To enhance the explanatory power of optimality models by incorporating additional decision-making aspects.
• To advocate for refining, rather than abandoning, the optimality approach in behavioral science.

Main Methods:

• Theoretical modeling integrating information processing costs into optimality criteria.
• Review and synthesis of recent advancements in decision optimization theory.
• Application of the enhanced optimality framework to perceptual decision-making paradigms.

Main Results:

• A generalized optimality approach significantly increases explanatory power for perceptual decision-making.
• Incorporating information processing costs refines predictions of decision strategies.
• The enhanced model provides a more robust framework for analyzing behavioral data.

Conclusions:

• The optimality approach remains a powerful tool when appropriately generalized.
• Future research should focus on integrating computational costs into decision models.
• A refined optimality approach offers deeper insights into the mechanisms of perceptual decision-making.