An affective computing approach to physiological emotion specificity: Toward subject-independent and stimulus-independent classification of film-induced emotions
The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quad...
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| Published in | Psychophysiology Vol. 48; no. 7; pp. 908 - 922 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
01.07.2011
Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k‐nearest neighbors method), and cross‐validation method (subject‐ and stimulus‐(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10‐min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed. |
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| AbstractList | The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-nearest neighbors method), and cross-validation method (subject- and stimulus-(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10-min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed.The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-nearest neighbors method), and cross-validation method (subject- and stimulus-(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10-min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed. The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k-nearest neighbors method), and cross-validation method (subject- and stimulus-(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10-min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed. [PUBLICATION ABSTRACT] The hypothesis of physiological emotion specificity has been tested using pattern classification analysis (PCA). To address limitations of prior research using PCA, we studied effects of feature selection (sequential forward selection, sequential backward selection), classifier type (linear and quadratic discriminant analysis, neural networks, k‐nearest neighbors method), and cross‐validation method (subject‐ and stimulus‐(in)dependence). Analyses were run on a data set of 34 participants watching two sets of three 10‐min film clips (fearful, sad, neutral) while autonomic, respiratory, and facial muscle activity were assessed. Results demonstrate that the three states can be classified with high accuracy by most classifiers, with the sparsest model having only five features, even for the most difficult task of identifying the emotion of an unknown subject in an unknown situation (77.5%). Implications for choosing PCA parameters are discussed. |
| Author | Roth, Walton T. Gross, James J. Kolodyazhniy, Vitaliy Kreibig, Sylvia D. Wilhelm, Frank H. |
| Author_xml | – sequence: 1 givenname: Vitaliy surname: Kolodyazhniy fullname: Kolodyazhniy, Vitaliy organization: Department of Clinical Psychology, Psychotherapy, and Health Psychology, University of Salzburg, Salzburg, Austria – sequence: 2 givenname: Sylvia D. surname: Kreibig fullname: Kreibig, Sylvia D. organization: Department of Psychology, Stanford University, Palo Alto, California, USA – sequence: 3 givenname: James J. surname: Gross fullname: Gross, James J. organization: Department of Psychology, Stanford University, Palo Alto, California, USA – sequence: 4 givenname: Walton T. surname: Roth fullname: Roth, Walton T. organization: Department of Psychology, Stanford University, Palo Alto, California, USA – sequence: 5 givenname: Frank H. surname: Wilhelm fullname: Wilhelm, Frank H. organization: Department of Clinical Psychology, Psychotherapy, and Health Psychology, University of Salzburg, Salzburg, Austria |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21261632$$D View this record in MEDLINE/PubMed |
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| Notes | istex:7343219D5771D354337EBC7605BAD1D3268940B0 ark:/67375/WNG-WFX9DZQT-W ArticleID:PSYP1170 The first and second authors contributed equally to this work. This research was supported by the 6th Framework Programme Project EUCLOCK (018741) funded by the European Commission (F.W., V.K.), the Basel Scientific Society (F.W.), and the National Center of Competence in Research Affective Sciences funded by the Swiss National Science Foundation (51NF40‐104897) and hosted by the University of Geneva (S.K.) and the Swiss National Science Foundation (PBGEP1‐125914; S.K.). Some of these data were presented at the annual meeting of the Society for Psychophysiological Research (October, 2007). SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 |
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| Title | An affective computing approach to physiological emotion specificity: Toward subject-independent and stimulus-independent classification of film-induced emotions |
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