Aversive learning modulates cortical representations of object categories.
Experimental studies of conditioned learning reveal activity changes in the amygdala and unimodal sensory cortex underlying fear acquisition to simple stimuli. However, real-world fears typically involve complex stimuli represented at the category level. A consequence of category-level representations of threat is that aversive experiences with particular category members may lead one to infer that related exemplars likewise pose a threat, despite variations in physical form. Here, we examined the effect of category-level representations of threat on human brain activation using 2 superordinate categories (animals and tools) as conditioned stimuli. Hemodynamic activity in the amygdala and category-selective cortex was modulated by the reinforcement contingency, leading to widespread fear of different exemplars from the reinforced category. Multivariate representational similarity analyses revealed that activity patterns in the amygdala and object-selective cortex were more similar among exemplars from the threat versus safe category. Learning to fear animate objects was additionally characterized by enhanced functional coupling between the amygdala and fusiform gyrus. Finally, hippocampal activity co-varied with object typicality and amygdala activation early during training. These findings provide novel evidence that aversive learning can modulate category-level representations of object concepts, thereby enabling individuals to express fear to a range of related stimuli.
Duke Scholars
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Related Subject Headings
- Young Adult
- Psychophysics
- Photic Stimulation
- Pattern Recognition, Visual
- Oxygen
- Male
- Magnetic Resonance Imaging
- Image Processing, Computer-Assisted
- Humans
- Female
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Young Adult
- Psychophysics
- Photic Stimulation
- Pattern Recognition, Visual
- Oxygen
- Male
- Magnetic Resonance Imaging
- Image Processing, Computer-Assisted
- Humans
- Female