Cognitive NeuroscienceNeurosciencePeer-reviewed journal article
Supracategorical fear information revealed by aversively conditioning multiple categories
Seth M. LevineiD, Miriam KumpfiD, Rainer RupprechtiD, Jens Volkmar SchwarzbachiD
Abstract
Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear--and accordingly respond to--pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity-patterns of stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we designed a three-phase (i.e., baseline, conditioned, extinction) experiment using fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also an increase in similarity between the CS+ categories. Using representational similarity analysis, we correlated a set of models to activity-patterns underlying several regions of interest and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the superior frontal gyrus (SFG) and the right temporal pole (rTP). Activity patterns in the object-selective lateral occipital cortex tended to prefer the semantic model, regardless of the experimental phase. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for the SFG and rTP in potentially linking discrete information or abstractly representing supracategorical information during fear-learning for the purpose of proper generalization.
- organism: human
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 6
- FWCI
- 0.54
- Field percentile
- top 38%
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