Studies focusing on laterality differences in LOC have reported larger adaptation effects for repeated presentations of different exemplars of the same object category in the left hemisphere39,40, and greater sensitivity to changes in viewpoint in the right hemisphere54.
We focus our initial analysis upon the left lateral occipital cortex (left LO)38, as LO is believed to be central to object shape perception and previous work has found that adaptation effects in LO for repeated presentations of different exemplars of the same shape category are most prominent in the left hemisphere39,40.
Many of the photos included the same objects photographed from different viewpoints, or different exemplars of the same type of objects.
This delay can vary considerably (from tens to a few hundred milliseconds) across both different call types and across different exemplars of the same call type.
Several different exemplars of the same textures were used to make sure that rats did not use specific cues attached to one particular object.
Within this sequence, different exemplars of the same stimulus class usually followed exemplars from different classes, thus ensuring that the order in which the stimuli were played to the birds (which was the same for every bird and every session) could not account for the observed pattern.
Note that, within this sequence, different exemplars of the same class usually followed exemplars from different classes, thus ensuring that the order in which the vocalizations were played to the birds (which was the same for every bird and every session) could not account for the observed pattern.
The numbers attached to the stimuli indicate different exemplars of the same CV stimulus.
At the perceptual extreme of tuning to stimulus form, recovery might show an "image-selective" pattern, with continued adaptation only to an identical picture and full recovery to any picture with different stimulus form in 2D retinotopic coordinates, including different exemplars of the same type of animal that have been reversed in their left/right orientation.
In contrast, HMAX is typically trained with large databases of pictures of different exemplars of, for example, hats and beer mugs as in the Caltech databases, which do not provide the basis for invariant representations of objects to be learned, but are aimed at object classification.
