Abstract
Humans can rapidly extract the 'gist' of images, using global image and summary statistics. This allows for quick extraction of information for multiple categories, but these outputs can interfere destructively depending on the task at hand (Evans et al., 2011). Using rapid event-related fMRI and EEG in two experiments, we investigated the neuronal correlates of gist processing and their modulation due to changing task contingencies. In the fMRI experiment a combination of noise masks and two different category images were presented in quadrants of the visual field simultaneously for 200 ms. Observers reported the presence and quadrant of a pre-defined target category. We measured BOLD responses in pre-localised, category selective cortical regions and conducted additional whole-brain analyses. In the EEG experiment observers were also asked to categorize briefly presented (25 ms) pre-cued images from six categories. Multivariate pattern analysis (MVPA) of EEG responses was used to identify patterns of activity across the scalp. FMRI results show category-selective activation in extrastiate areas, supporting their involvement in gist perception, and EEG data revealed that gist is discriminable from 50 ms post stimulus onset. No top-down-driven activation in target locations was observed in early visual cortex, consistent with the observation of gist extraction without the ability to localize the target. Responses to changes in the image category task contingencies during the experiment were evident only in frontal areas. Consistent with this, changes in task contingency influenced the pattern of EEG responses only from around 300 ms post stimulus onset. In conclusion, we find that activity in category specific extrastriate visual areas correlates with spatially non-specific, rapid gist perception and that these areas presumably pool signals from earlier areas with lower featural selectivity. Lastly, the effects of task contingencies modulate this rapid gist processing only at the decisional stage.
Meeting abstract presented at VSS 2017