Behavioral Experience-Sampling Methods in Neuroimaging Studies With Movie and Narrative Stimuli

• Published in: Frontiers in human neuroscience Vol. 16; p. 813684
• Main Authors: Jääskeläinen, Iiro P., Ahveninen, Jyrki, Klucharev, Vasily, Shestakova, Anna N., Levy, Jonathan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 27.01.2022; Frontiers Media S.A
• Subjects: attention; Cognitive ability; Data analysis; EEG; Electroencephalography; emotion; Emotional regulation; Emotions; Functional magnetic resonance imaging; Image processing; language; Learning algorithms; Machine learning; Medical imaging; memory; Motion pictures; Narratives; naturalistic stimuli; Neuroimaging; Neuroscience; Questionnaires; Ratings & rankings; Reviews; Scanners; social cognition; movies; narratives; emotion; memory; social cognition; naturalistic stimuli; attention; language
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2022.813684

Movies and narratives are increasingly utilized as stimuli in functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and electroencephalography (EEG) studies. Emotional reactions of subjects, what they pay attention to, what they memorize, and their cognitive interpretations are all examples of inner experiences that can differ between subjects during watching of movies and listening to narratives inside the scanner. Here, we review literature indicating that behavioral measures of inner experiences play an integral role in this new research paradigm via guiding neuroimaging analysis. We review behavioral methods that have been developed to sample inner experiences during watching of movies and listening to narratives. We also review approaches that allow for joint analyses of the behaviorally sampled inner experiences and neuroimaging data. We suggest that building neurophenomenological frameworks holds potential for solving the interrelationships between inner experiences and their neural underpinnings. Finally, we tentatively suggest that recent developments in machine learning approaches may pave way for inferring different classes of inner experiences directly from the neuroimaging data, thus potentially complementing the behavioral self-reports.