Decision making: from neuroscience to psychiatry

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Psychiatry

Background:

• Adaptive behaviors are crucial for survival and well-being, but optimal decision-making is challenging due to environmental and social complexities.
• Brain mechanisms underlying decision-making can result in suboptimal outcomes, a problem often worsened in neurological and psychiatric disorders.
• The neurobiological underpinnings of these decision-making deficits in clinical populations are not fully understood.

Purpose of the Study:

• To review theoretical frameworks from economics and machine learning applied to behavioral and neurobiological studies of decision-making.
• To explore the application of these frameworks in understanding and potentially treating various neurological and psychiatric disorders.
• To lay the groundwork for improved diagnostics and therapeutic interventions.

Main Methods:

• Literature review of theoretical frameworks in economics and machine learning.
• Analysis of recent behavioral and neurobiological studies integrating these frameworks.
• Examination of clinical applications in substance abuse, Parkinson's disease, ADHD, schizophrenia, mood disorders, and autism.

Main Results:

• Theoretical models from economics and machine learning offer valuable insights into decision-making processes.
• These models have been successfully applied to understand behavioral and neurobiological patterns in various disorders.
• The integration of these approaches provides a foundation for novel diagnostic and treatment strategies.

Conclusions:

• Understanding decision-making through economic and machine learning lenses can illuminate suboptimal behaviors in health and disease.
• This interdisciplinary approach holds significant promise for advancing the diagnosis and treatment of a range of neurological and psychiatric conditions.
• Further research integrating computational models with neurobiological data is essential for clinical translation.