Literature DB >> 36042819

Audiovisual Representations of Valence: a Cross-study Perspective.

Svetlana V Shinkareva1, Chuanji Gao1, Douglas Wedell1.   

Abstract

Hedonic valence describes the pleasantness or unpleasantness of psychological states elicited by stimuli and is conceived as a fundamental building block of emotional experience. Multivariate pattern analysis approaches contribute to the study of valence representation by allowing identification of valence from distributed patterns of activity. However, the issue of construct validity arises in that there is always a possibility that classification results from a single study are driven by factors other than valence, such as the idiosyncrasies of the stimuli. In this work, we identify valence across participants from six different fMRI studies that used auditory, visual, or audiovisual stimuli, thus increasing the likelihood that classification is driven by valence and not by the specifics of the experimental paradigm of a particular study. The studies included a total of 93 participants and differed on stimuli, task, trial duration, number of participants, and scanner parameters. In a leave-one-study-out cross-validation procedure, we trained the classifiers on fMRI data from five studies and predicted valence, positive or negative, for each of the participants in the left-out study. Whole-brain classification demonstrated a reliable distinction between positive and negative valence states (72% accuracy). In a searchlight analysis, the representation of valence was localized to the right postcentral and supramarginal gyri, left superior frontal and middle frontal cortices, and right pregenual anterior cingulate and superior medial frontal cortices. The demonstrated cross-study classification of valence enhances the construct validity and generalizability of the findings from the combined studies. © The Society for Affective Science 2020.

Entities:  

Keywords:  MVPA; Valence; fMRI

Year:  2020        PMID: 36042819      PMCID: PMC9382970          DOI: 10.1007/s42761-020-00023-9

Source DB:  PubMed          Journal:  Affect Sci        ISSN: 2662-2041


  59 in total

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Authors:  Nikolaus Kriegeskorte; Rainer Goebel; Peter Bandettini
Journal:  Proc Natl Acad Sci U S A       Date:  2006-02-28       Impact factor: 11.205

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Authors:  Ajay B Satpute; Kristen A Lindquist
Journal:  Trends Cogn Sci       Date:  2019-08-16       Impact factor: 20.229

3.  Modelling audiovisual integration of affect from videos and music.

Authors:  Chuanji Gao; Douglas H Wedell; Jongwan Kim; Christine E Weber; Svetlana V Shinkareva
Journal:  Cogn Emot       Date:  2017-05-02

4.  An fMRI Study of Affective Congruence across Visual and Auditory Modalities.

Authors:  Chuanji Gao; Christine E Weber; Douglas H Wedell; Svetlana V Shinkareva
Journal:  J Cogn Neurosci       Date:  2020-02-28       Impact factor: 3.225

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Authors:  P J Lang
Journal:  Am Psychol       Date:  1995-05

6.  A study in affect: Predicting valence from fMRI data.

Authors:  Jongwan Kim; Christine E Weber; Chuanji Gao; Selena Schulteis; Douglas H Wedell; Svetlana V Shinkareva
Journal:  Neuropsychologia       Date:  2020-04-22       Impact factor: 3.139

7.  Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth.

Authors:  Marcel Adam Just; Lisa Pan; Vladimir L Cherkassky; Dana L McMakin; Christine Cha; Matthew K Nock; David Brent
Journal:  Nat Hum Behav       Date:  2017-10-30

8.  The theory of constructed emotion: an active inference account of interoception and categorization.

Authors:  Lisa Feldman Barrett
Journal:  Soc Cogn Affect Neurosci       Date:  2017-11-01       Impact factor: 3.436

9.  Smoothness without smoothing: why Gaussian naive Bayes is not naive for multi-subject searchlight studies.

Authors:  Rajeev D S Raizada; Yune-Sang Lee
Journal:  PLoS One       Date:  2013-07-26       Impact factor: 3.240

10.  Exploring the impact of analysis software on task fMRI results.

Authors:  Alexander Bowring; Camille Maumet; Thomas E Nichols
Journal:  Hum Brain Mapp       Date:  2019-05-02       Impact factor: 5.038

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