Dear Users

Today (4-5 pm in YNiC) there will be a YNiC project proposal presentation given by Chris Racey.

The title of the talk is "The effects of parametrically manipulating view exposure range on representational invariance in object selective cortex". Please see the abstract below.

Everyone is welcome to attend.

N.B. The starting time of the YNiC seminars will be 4 pm this term.

Best wishes Rebecca

*Abstract* Previously we have carried out two experiments in which view shift of objects and landscape scenes was parametrically manipulated. In both experiments participants viewed blocks of stimuli changing in view by 0, 5, 10, and 15°. Different levels of adaptation across these view shift conditions allow us to derive a measure of view sensitivity for any given voxel or region. We were able to show variation in the degree of view sensitivity between category selective regions for both objects and landscape stimuli. Our previous work has shed new light on how category specific visual information is processed and represented during perception. However, it is not yet clear what the effect of object learning and long term storage is on these ventral stream representations. Previous research on this issue is limited, and there have been few studies investigating the effects of prior learning on neural adaptation. We aim to apply our parametric manipulation of view shift paradigm during an encoding experiment with unfamiliar objects prior to scanning. Participants will under training, and learn to recognise objects where their range of view exposure will vary parametrically, from 0°, 10°, and 20°. In the scanner, all of the stimuli will be presented from previously unseen views in an fMR-adaptation block design, with blocks of images shifting in view in 10° steps. For each pre-exposure condition, the presentation conditions in the scanner will be identical, and any differences across conditions must be due to differences during the encoding of items. We predict that objects learned with a greater range of views will be associated with richer, more invariant, representations, and show greater degrees of invariant adaptation than objects learned under a narrower range of views.