A computational approach to "free will" constrained by the games we play

Area of Science:

• Neuroscience
• Computational Psychology
• Behavioral Economics

Background:

• Human choice is constrained by biology, yet exhibits variability in capacity and preference.
• The concept of 'free will' requires theoretical frameworks for empirical investigation.
• Psychiatric illnesses are characterized by diminished capacity for willful choice.

Purpose of the Study:

• To explore computational approaches for understanding human choice behavior and its neurobiology.
• To investigate the biological variables underlying the capacity for choice.
• To examine how choice capacity is altered in psychiatric disorders.

Main Methods:

• Integration of game theory and computational reinforcement learning with empirical methods.
• Application of quantitative models to study strategic and adaptive human choice.
• Utilizing human functional magnetic resonance imaging (fMRI) and sub-second electrophysiological measurements.

Main Results:

• Computational approaches provide valuable insights into the biological basis of choice.
• These methods have been successfully applied to investigate optimal and adaptive decision-making.
• Psychiatric patients exhibit a fundamental deficit in willful choice capacity.

Conclusions:

• Cross-disciplinary approaches combining computational modeling and empirical neuroscience are promising.
• These methods can reveal neurobiological mechanisms underlying variations in human choice capacity.
• Further research can illuminate the basis of choice deficits in brain disorders.