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

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...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2022-01, Vol.16, p.813684-813684
Main Authors: Jääskeläinen, Iiro P, Ahveninen, Jyrki, Klucharev, Vasily, Shestakova, Anna N, Levy, Jonathan
Format: Article
Language:English
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
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Summary: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 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.
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2022.813684