Classification for Predicting Attitudes Using fNIRS.


A Synchrony-Based Classification Approach for Predicting Attitudes Using fNIRS.

Social neuroscience research has demonstrated that those who are like-minded are also “like-brained.” Studies have shown that people who share similar viewpoints have greater neural synchrony with one another, and less synchrony with people who “see things differently.” Although these effects have been demonstrated at the group level, little work has been done to predict the viewpoints of specific individuals using neural synchrony measures. Furthermore, the studies that have made predictions using synchrony-based classification at the individual level used expensive and immobile neuroimaging equipment (e.g. fMRI) in highly controlled laboratory settings, which may not generalize to real-world contexts. Thus, this study uses a simple synchrony-based classification method, which we refer to as the neural reference groups approach, to predict individuals’ dispositional attitudes from data collected in a mobile “pop-up neuroscience” lab. Using functional near infrared spectroscopy (fNIRS) data, we predicted individuals’ partisan stances on a sociopolitical issue by comparing their neural timecourses to data from two partisan neural reference groups. We found that partisan stance could be identified at above-chance levels using data from dorsomedial prefrontal cortex (dmPFC). These results indicate that the neural reference groups approach can be used to investigate naturally-occurring, dispositional differences anywhere in the world.

PMID: 33025001 [PubMed – as supplied by publisher]

Soc Cogn Affect Neurosci. 2020 Oct 07;:

Authors: Dieffenbach MC, Gillespie GSR, Burns SM, McCulloh IA, Ames DL, Dagher MM, Falk EB, Lieberman MD




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