Identifying Emotions on the Basis of Neural Activation

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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Within-subject and between-subject classification rank accuracies by emotion.

Error bars represent standard error.

Figure 2. Average normalized ranks for all emotions derived from within-subject identification.

Each line illustrates, for the target emotion listed on the left, the average normalized rank of classifier guesses. Error bars shown for the three highest ranked emotions represent standard error. Standard errors for all emotions ranged from 0.02 to 0.10, with a mean standard error for average positions of 0.06. See text for additional details.

Figure 3. Group-level image of voxels used for within-subject classification.

Images of the 240 most stable voxels across six presentations of emotion words were superimposed on one another, with resulting clusters of 25 or more voxels depicted. Color intensity reflects the number of participants for whom the voxel was among the 240 highest in stability.

Figure 4. Voxel groups derived from factor analysis, threshold of 0.5, cluster size of 10.

See text for additional details.