Decision mechanisms underlying mood-congruent emotional classification

Area of Science:

• Cognitive Psychology
• Affective Science

Background:

• Understanding mood's influence on affective processing is crucial, yet prior research shows mixed results.
• Previous studies often failed to dissociate decision components in affective processing and response biases.

Purpose of the Study:

• To investigate the distinct roles of affective processing and response biases in mood-congruent effects.
• To utilize a drift-diffusion model (DDM) to analyze decision-making components in affective judgments.

Main Methods:

• Two experiments involved participants classifying word valence (positive/negative) under different music-induced moods.
• A drift-diffusion model (DDM) was applied to behavioral data to separate processing speed and response bias.

Main Results:

• Behavioral data revealed weak and inconsistent mood-congruency effects.
• The drift-diffusion model (DDM) identified significant effects driven by response expectation bias, not stimulus processing.
• Music-induced mood modulated expectations about upcoming emotional stimuli.

Conclusions:

• Mood primarily influences response expectations rather than the direct processing of affective stimuli.
• The findings highlight the utility of the drift-diffusion model (DDM) for dissecting mood effects in affective science.
• Future research should consider response biases when examining mood and emotional classification.