Computational and neural correlates of Gain-loss differences in intertemporal choice

Area of Science:

• Neuroeconomics
• Cognitive Neuroscience
• Decision Science

Background:

• Intertemporal choice shows systematic differences between gain and loss contexts.
• The computational mechanisms and neural correlates of these differences are not fully understood.

Purpose of the Study:

• To investigate the computational mechanisms and neural correlates of gain-loss asymmetries in intertemporal decision-making.

Main Methods:

• Fifty-three healthy adults performed a delay discounting task in gain and loss contexts during functional magnetic resonance imaging (fMRI).
• Behavioral data were analyzed using drift diffusion modeling (DDM).

Main Results:

• Behaviorally, participants chose smaller-sooner options more in the gain context than larger-later options in the loss context.
• DDM revealed context-dependent decision dynamics: higher drift rates, stronger starting-point biases, and longer non-decision times in the gain context.
• fMRI showed increased activation in the bilateral hippocampus and right insula during intertemporal choices in the gain context.

Conclusions:

• Gain-loss context significantly influences decision dynamics and associated neural activity.
• Findings provide converging computational and neural evidence for gain-loss asymmetries in intertemporal decision-making.