Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in...

Area of Science:

• Cognitive Neuroscience
• Psychiatry
• Computational Psychiatry

Background:

• Negative affective biases are common in mood disorders, leading to biased information processing.
• Depression is associated with a reduced preference for high rewards in ambiguous decision-making.
• The underlying mechanisms of this negative affective bias remain unclear.

Purpose of the Study:

• To investigate the mechanisms driving negative affective bias in mood disorders.
• To examine the relationship between affective bias and reward sensitivity, value sensitivity, and reward learning rate.
• To understand how reduced reward sensitivity and increased response variability contribute to negative affective bias.

Main Methods:

• Utilized three online behavioral tasks: a decision-making task for affective bias, a probabilistic reward learning task, and a gambling task.
• Employed computational modeling, including dynamic signal detection theory and expectation-maximization prospect theory.
• Recruited 148 participants to complete the study.

Main Results:

• Reward sensitivity and setting noise from the probabilistic reward task significantly predicted the affective bias score.
• A logistic regression model indicated that both factors were significant predictors (p < 0.05).
• Findings suggest a link between reward processing and negative information prioritization.

Conclusions:

• Negative affective bias, in this context, appears partly driven by diminished sensitivity to rewards.
• Increased response variability may also contribute to the observed affective bias.
• These findings offer insights into the computational mechanisms underlying mood disorders.