Decoding the neural representation of affective states

Decoding the neural representation of affective states. Baucom, L. B, Wedell, D. H, Wang, J., Blitzer, D. N, & Shinkareva, S. V NeuroImage, 59(1):718–727, 2012. Publisher: Elsevier Inc. ISBN: 1053-8119

Paper doi abstract bibtex

Brain activity was monitored while participants viewed picture sets that reflected high or low levels of arousal and positive, neutral, or negative valence. Pictures within a set were presented rapidly in an incidental viewing task while fMRI data were collected. The primary purpose of the study was to determine if multi-voxel pattern analysis could be used within and between participants to predict valence, arousal and combined affective states elicited by pictures based on distributed patterns of whole brain activity. A secondary purpose was to determine if distributed patterns of whole brain activity can be used to derive a lower dimensional representation of affective states consistent with behavioral data. Results demonstrated above chance prediction of valence, arousal and affective states that was robust across a wide range of number of voxels used in prediction. Additionally, individual differences multidimensional scaling based on fMRI data clearly separated valence and arousal levels and was consistent with a circumplex model of affective states. ?? 2011 Elsevier Inc.

@article{baucom_decoding_2012,
	title = {Decoding the neural representation of affective states},
	volume = {59},
	issn = {10538119},
	url = {http://dx.doi.org/10.1016/j.neuroimage.2011.07.037},
	doi = {10.1016/j.neuroimage.2011.07.037},
	abstract = {Brain activity was monitored while participants viewed picture sets that reflected high or low levels of arousal and positive, neutral, or negative valence. Pictures within a set were presented rapidly in an incidental viewing task while fMRI data were collected. The primary purpose of the study was to determine if multi-voxel pattern analysis could be used within and between participants to predict valence, arousal and combined affective states elicited by pictures based on distributed patterns of whole brain activity. A secondary purpose was to determine if distributed patterns of whole brain activity can be used to derive a lower dimensional representation of affective states consistent with behavioral data. Results demonstrated above chance prediction of valence, arousal and affective states that was robust across a wide range of number of voxels used in prediction. Additionally, individual differences multidimensional scaling based on fMRI data clearly separated valence and arousal levels and was consistent with a circumplex model of affective states. ?? 2011 Elsevier Inc.},
	number = {1},
	journal = {NeuroImage},
	author = {Baucom, Laura B and Wedell, Douglas H and Wang, Jing and Blitzer, David N and Shinkareva, Svetlana V},
	year = {2012},
	pmid = {21801839},
	note = {Publisher: Elsevier Inc.
ISBN: 1053-8119},
	keywords = {Affective states, Arousal, INDSCAL, Multi-voxel pattern analysis, Valence},
	pages = {718--727},
}

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