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Jonathan S. Cant, Yaoda Xu; The flipside of object individuation: Neural representation for object ensembles. Journal of Vision 2010;10(7):111. doi: 10.1167/10.7.111.
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© ARVO (1962-2015); The Authors (2016-present)
Imagine you are in a supermarket looking for apples. You need to first find the right fruit pile by representing collections of objects without encoding each object in the collection in great detail. After you locate the apple pile, you can proceed to pick out the best-looking apples by encoding individual objects with their detailed features. While a huge amount of research effort has been dedicated to understanding how we represent specific objects, our knowledge of the cognitive and neural mechanisms underlying object ensemble representation is still incomplete. Using the fMRI-adaptation paradigm, we showed participants a sequence of three images that were either all identical, all different, or shared object ensemble statistics (e.g. three different non-overlapping snapshots of the same apple pile). Using an independent localizer approach, we found that the lateral occipital complex (LOC) showed a significant release from adaptation (i.e. a rise in activation compared to the ‘identical’ condition) in both the ‘shared’ and ‘different’ conditions (which did not differ from each other). In contrast, ventral medial visual cortex including areas in the collateral sulcus and the parahippocampal place area (PPA) showed statistically equivalent levels of repetition attenuation (i.e. a reduction in activation compared to the ‘different’ condition) in both the ‘identical’ and ‘shared’ conditions (which did not differ). These results indicate that while the LOC is involved in encoding specific object features (which is consistent with previous findings), the ventral medial visual cortex may be involved in representing ensemble statistics from object collections. Notably, although our stimuli contained minimal amount of 3D scene information, the PPA exhibited adaptation when ensemble statistics were repeated. This suggests that the PPA may contribute to scene representation by extracting ensemble statistics rather than the 3D layout of a scene.
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