Title |
Identifying Emotions on the Basis of Neural Activation
|
---|---|
Published in |
PLOS ONE, June 2013
|
DOI | 10.1371/journal.pone.0066032 |
Pubmed ID | |
Authors |
Karim S. Kassam, Amanda R. Markey, Vladimir L. Cherkassky, George Loewenstein, Marcel Adam Just |
Abstract |
We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 14% |
United Kingdom | 3 | 9% |
Canada | 2 | 6% |
Italy | 2 | 6% |
Sweden | 1 | 3% |
Mexico | 1 | 3% |
Denmark | 1 | 3% |
France | 1 | 3% |
Norway | 1 | 3% |
Other | 0 | 0% |
Unknown | 18 | 51% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 23 | 66% |
Scientists | 8 | 23% |
Practitioners (doctors, other healthcare professionals) | 3 | 9% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 2% |
Germany | 5 | <1% |
Japan | 4 | <1% |
Italy | 2 | <1% |
Netherlands | 2 | <1% |
Portugal | 2 | <1% |
Spain | 2 | <1% |
Brazil | 2 | <1% |
United Kingdom | 2 | <1% |
Other | 7 | 1% |
Unknown | 480 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 129 | 25% |
Researcher | 81 | 16% |
Student > Master | 75 | 14% |
Student > Bachelor | 47 | 9% |
Student > Doctoral Student | 34 | 7% |
Other | 80 | 15% |
Unknown | 75 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 192 | 37% |
Neuroscience | 51 | 10% |
Computer Science | 35 | 7% |
Engineering | 31 | 6% |
Agricultural and Biological Sciences | 23 | 4% |
Other | 93 | 18% |
Unknown | 96 | 18% |