We effortlessly and seemingly instantaneously recognize thousands of objects, although we rarely--if ever--see the same image of an object twice. The retinal image of an object can vary by context, size, viewpoint, illumination, and location. The present study examined how the visual system abstracts object category across variations in retinal location. In three experiments, participants viewed images of objects presented to different retinal locations while brain activity was recorded using magnetoencephalography (MEG). A pattern classifier was trained to recover the stimulus position (Experiments 1, 2, and 3) and category (Experiment 3) from the recordings. Using this decoding approach, we show that an object's location in the visual field can be recovered in high temporal resolution (5 ms) and with sufficient fidelity to capture topographic organization in visual areas. Experiment 3 showed that an object's category could be recovered from the recordings as early as 135 ms after the onset of the stimulus and that category decoding generalized across retinal location (i.e., position invariance). Our experiments thus show that the visual system rapidly constructs a category representation for objects that is invariant to position.

Abstract■ Objects occupy space. How does the brain represent the spatial location of objects? Retinotopic early visual cortex has precise location information but can only segment simple objects. On the other hand, higher visual areas can resolve complex objects but only have coarse location information. Thus coarse location of complex objects might be represented by either (a) feedback from higher areas to early retinotopic areas or (b) coarse position encoding in higher areas. We tested these alternatives by presenting various kinds of first-(edgedefined) and second-order (texture) objects. We applied multivariate classifiers to the pattern of EEG amplitudes across the scalp at a range of time points to trace the temporal dynamics of coarse location representation. For edge-defined objects, peak classification performance was high and early and thus attributable to the retinotopic layout of early visual cortex. For texture objects, it was low and late. Crucially, despite these differences in peak performance and timing, training a classifier on one object and testing it on others revealed that the topography at peak performance was the same for both first-and second-order objects. That is, the same location information, encoded by early visual areas, was available for both edge-defined and texture objects at different time points. These results indicate that locations of complex objects such as textures, although not represented in the bottom-up sweep, are encoded later by neural patterns resembling the bottom-up ones. We conclude that feedback mechanisms play an important role in coarse location representation of complex objects. ■

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