Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational...

Area of Science:

• Cognitive Neuroscience
• Decision Science
• Affective Science

Background:

• Affective states and risk-taking are typically studied using aggregate statistics from decision-making tasks.
• The impact of ongoing, within-task reward and punishment experiences on future decisions and affect remains under-investigated.
• Understanding this interplay is crucial for accurately using decision-making as a marker of affective states.

Purpose of the Study:

• To investigate the dynamic relationships between reward/loss experiences, affective states, and decision-making in humans.
• To explore how short-term, within-task outcomes influence subsequent choices and emotional valence.

Main Methods:

• Utilized a novel judgment bias task designed to assess decision-making under varying conditions.
• Employed a novel computational model to analyze task data and quantify decision biases.
• Measured subjective affective states and correlated them with task performance and outcomes.

Main Results:

• Within-task favorability significantly influenced decision-making, leading to more risk-averse choices after positive outcomes and higher average earnings.
• Individuals with more negative affect exhibited increased risk-seeking behavior and poorer time estimation, according to the computational model.
• Positive affective valence correlated with periods of more favorable prediction errors and decision outcomes during the task.

Conclusions:

• Short-term, within-task rewarding and punishing experiences dynamically shape future decision-making strategies.
• Subjectively experienced affective states are influenced by the valence of recent outcomes and prediction errors.
• This study provides novel evidence for the reciprocal influence of experience, affect, and decision-making within a single task.