Reliable and predictive non-perceptual representations in primary visual cortex during attempts at visual imagery in aphantasia
Journal of Vision(2023)
摘要
Visual imagery is an important function of the human brain, and previous research on visual imagery reported that imagined stimuli can be decoded in early visual areas, while some other studies claimed that visual imagery generation relied more on higher-level cortical regions. Using fMRI the current study used multi-voxel decoding of attempts at generating visual mental imagery in early visual areas in individuals with aphantasia (lack the ability to voluntarily generate visual imagery). Participants with aphantasia (N = 14) and control (N = 18) groups were asked to generate mental imagery of Gabor patches of different orientations in the left or right visual field. We found that, activation patterns in the V1 and V2 of both aphantasic and control groups could be used to decode the imagined stimuli. However, for aphantasia, the patterns in early visual areas during imagery could not be used to decode the patterns during passively perceiving the same stimuli, whereas accuracies of this cross-task decoding for the control group were significant. These results suggest that aphantasic individuals, are not able to generate neural representations corresponding to perceptual sensory information in the early visual areas via voluntary visual imagery. Consistent with this notion, averaged BOLD activity in the early visual areas of the aphantasic individuals were also atypical during visual imagery, whereas during visual perception a typical contralateral visual field effect was found although slightly weaker than in the control group. For the first time, the current study reports that those with aphantasia do indeed have reliable and predictable neural patterns in early visual cortex during attempts at visual imagery, however these representations diverge from perceptual representations in the same individuals.
更多查看译文
关键词
primary visual cortex,visual imagery,non-perceptual
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要