Disentangling sources of variability in decision-making

Area of Science:

• Cognitive Neuroscience
• Computational Psychiatry
• Decision Science

Background:

• Trial-to-trial variability in choice timing and accuracy is a pervasive feature of decision-making.
• This intra-individual variability (IIV) is a key phenotype in clinical disorders, yet its sources are poorly understood.
• Existing computational models have limitations in fully parsing the origins of IIV.

Purpose of the Study:

• To review current limitations of algorithmic models in understanding decision-making variability.
• To highlight recent advances enabling the linkage of IIV components to neural processes.
• To demonstrate new avenues for analyzing the neural origins of IIV for a holistic understanding of decision-making.

Main Methods:

• Discussion of limitations in current algorithmic models for decision-making variability.
• Highlighting advances in behavioral paradigm design and cross-trial analyses of neural dynamics.
• Focus on the development of neurally grounded computational models.

Main Results:

• Recent advances allow for the systematic analysis of distinct components of intra-individual variability.
• These methods enable the linking of specific IIV components to well-defined neural processes.
• Progress is being made in understanding the neural underpinnings of choice behavior variability.

Conclusions:

• New computational and analytical methods are crucial for dissecting the neural origins of decision-making variability.
• These advancements facilitate a more refined and holistic understanding of decision-making in both healthy and clinical populations.
• This approach opens new avenues for studying the neural basis of choice behavior and its disorders.