bioRxivNeurosciencePreprint
Common neural representation between visual perception and imagery in eidetikers
Ritsuko Nabata, Mitsuki NiidaiD, Kosaku TakahashiiD, Kenji OgawaiD
Abstract
Eidetic imagery is experienced as "seen" in external space, unlike typical visual mental imagery, which occurs in the minds eye. To investigate whether eidetic imagery and perception share common neural substrates, we measured brain activity in eidetikers and typical imagers using fMRI while they performed perception and imagery tasks. Multi-voxel pattern analysis (MVPA) revealed that imagined objects could be classified based on perceptual activation patterns in right BA19 for eidetikers, but not for typical imagers. This effect was specific to BA19 and was not observed in early visual areas (BA17/18). Subjective ratings confirmed that eidetikers experienced images as percept-like. These findings provide neural evidence for percept-like mental imagery and show that individual differences in subjective experience are reflected in the high-level visual cortex. Significance StatementHow does the brain support individual differences in subjective visual experience? Visual mental imagery partially shares neural substrates with perception, but how this shared activity relates to subjective conscious experience remains unclear. Some individuals, known as eidetikers, report images that are literally seen and percept-like, providing a unique opportunity to investigate the neural basis of individual variation. Using fMRI and multivariate pattern analysis, we show that in eidetikers, the neural representations of imagined objects are more strongly shared with perceptual representations in a higher-level visual area (BA19) than in typical imagers. These findings provide neural evidence for eidetic imagery and reveal how individual differences in subjective experience are reflected in the organization of visual representations in the human brain.
- organism: human
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- Citations
- 0
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