Abstract
We studied attentional effects for stimulus shape and location in anterior inferotemporal cortex (AIT, ventral stream) and lateral intraparietal cortex (LIP, dorsal stream). Monkeys performed two delayed-match-to-sample tasks. Stimuli were identical in both tasks, but in one the monkey attended to sample shape (shape attention task) and in the other to sample location (location attention task). There was also a third passive task in which the monkey maintained central fixation while the same stimuli were presented in the same locations. We examined data from all shapes at the most effective location (shape representations), or all locations using the most effective shape (location representations). At the single cell level, there was a broad range of attentional gain factors for stimulus shape and location in both brain areas. At the population level, responses of all neurons to each shape or each location formed a response vector. Mean distance between response vectors for different shapes was greater (more distinctive) in AIT than LIP, while mean distance between locations was greater (more distinctive) in LIP than AIT. We determined the effect of different attentional conditions on mean distance between response vectors for all shapes or all locations. In AIT, mean response distance between shapes was significantly larger under the shape attention task compared to the location attention task. In contrast, in LIP, mean response distances for locations were not significantly different between the two attention tasks. Even when changes in mean responses were factored out, multidimensional scaling still showed significant task differences in AIT but not LIP, indicating that attention was globally distorting neural representation spaces only in AIT. Despite single-cell attentional modulations in both areas, we suggest that attentional modulations of population representations may be weaker in the dorsal stream because it must maintain more veridical representations for visuomotor control.
Meeting abstract presented at VSS 2017