How bad becomes good: A neurocomputational model of affect-informed choice

Area of Science:

• Neuroscience
• Decision Science
• Affective Science

Background:

• Individuals frequently utilize current emotional states to guide choices.
• The neural mechanisms underlying affect-informed decision-making remain largely unexplored.
• Key questions address the neural overlap between affect generation and value computation, and factors influencing contextual flexibility in affect reliance.

Purpose of the Study:

• To develop and test a neurocomputational model of affect-informed choice.
• To investigate the temporal and neural distinctions between affect generation and valuation.
• To examine individual differences in the contextual flexibility of affect weighting.

Main Methods:

• Developed a neurocomputational model of affect-informed choice.
• Recorded electroencephalography (EEG) and facial electromyography (fEMG) during a novel affective choice task.
• Analyzed data from a diverse undergraduate sample (N=?).

Main Results:

• The developed model of affect-informed choice was validated.
• Affective response generation was found to precede and be neurally distinct from affect valuation.
• Individual differences in contextual flexibility of affective weighting correlated with later valuation, not earlier affect generation.

Conclusions:

• Affect generation and valuation involve distinct neural processes occurring at different times.
• Contextual flexibility in using emotions for decisions is linked to later-stage valuation processes.
• Findings offer significant theoretical insights into emotion, emotion regulation, and decision-making processes.