Multivariate pattern analysis of electroencephalography data reveals information predictive of charitable giving

Area of Science:

• Neuroscience
• Social Psychology
• Behavioral Economics

Background:

• Charitable donations involve altruistic behavior influenced by psychological factors like cost-benefit analysis and perceived benefits.
• The ventral medial prefrontal cortex (MPFC) is crucial for value assignment in social decision-making, integrating emotional and empathy-related inputs.
• Multivariate pattern analysis (MVPA) of fMRI data has predicted donation behavior from MPFC activity, highlighting emotion's role.

Purpose of the Study:

• To investigate the impact of incidental emotional states and perceived charity urgency on donation behavior.
• To explore the utility of MVPA on electroencephalography (EEG) data for predicting donation amounts.
• To understand the neural timing and topography of value computation and emotional influences on charitable giving.

Main Methods:

• Participants viewed positive, negative, or neutral images to induce emotional states before making donation decisions.
• Donations were directed towards charities focused on current suffering versus future prevention.
• Support vector regression was applied to EEG data to predict donation amounts on a trial-by-trial basis.

Main Results:

• Donations were significantly higher in a negative emotional state compared to neutral or positive states.
• Participants donated more to charities addressing current suffering than future suffering.
• MVPA of EEG data revealed that electrophysiological activity from emotion-priming and charity cues predicted donation magnitude.

Conclusions:

• Incidental emotional states and the perceived urgency of a charitable cause significantly modulate donation behavior.
• EEG-based MVPA can predict individual donation variations, offering insights into the neural underpinnings of altruism.
• This research contributes to understanding the complex interplay of emotion, cognition, and prosocial behavior.