A Common Mechanism Underlying Food Choice and Social Decisions.

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Behavioral Economics

Background:

• Humans make diverse decisions daily, from simple perceptions to complex social interactions.
• Understanding the unifying principles of decision-making across different contexts remains a challenge.
• Mathematical models offer insights into brain computations but their generalizability is debated.

Purpose of the Study:

• To investigate whether decision-making models can generalize across distinct choice domains.
• To test if a model developed for perceptual decisions can predict social choices.

Main Methods:

• Adapted a mathematical model from perceptual decision-making.
• Estimated the model's parameters using choices related to primary rewards (food).
• Validated the model's predictive power on independent datasets of social decision-making.

Main Results:

• The adapted model accurately predicted both choices and reaction times in social decision tasks.
• Model performance remained robust across four independent subject groups.
• Behavioral data support a common decision-making process applicable to varied situations.

Conclusions:

• A single computational framework can explain decision-making across perceptual, reward, and social contexts.
• This provides strong behavioral evidence for shared neural mechanisms underlying diverse human choices.
• Findings advance our understanding of generalizable decision-making principles in the brain.