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what has fmri taught us about object recognition using barcode generating for none control to generate, create none image in none applications.ean 13 Figure 6.6. LO re none for none sponses during fMRI-A experiments of rotation sensitivity.

Each line represents response after adapting with a front (dashed black) or back view (solid gray) of an object. The nonadapted response is indicated by the diamonds (black for front view, and gray for back view). The open circles indicate signi cant adaptation, lower than nonadapted, P < 0.

05, paired t-test across subjects. (a) Vehicle data. (b) Animal data.

Responses are plotted relative to a blank xation baseline. Error bars indicate SEM across eight subjects. Adapted from Andreson, Vinberg, and Grill-Spector (2009).

. GS1 supported barcodes higher for the fr ont view than the back view (compare black and gray circles in Fig. 6.6(b) right).

In addition fMRI-A effects across rotation varied according to the adapting view (Fig. 6(b) right). When adapting with the back view of animals, we found recovery from adaptation for rotations of 120 or larger, but when adapting with the front view of animals, there was no signi cant recovery from adaptation across rotations.

One interpretation is that there is less sensitivity to rotation when adapting with front views than back views of animals. However, the behavioral performance of subjects in a discrimination task across object rotations showed that they are equally sensitive to rotations (performance decreases with rotation level) whether rotations are relative to the front or back of an animal (Andresen et al. 2008), which suggests that this interpretation is unlikely.

Alternatively, the apparent adaptation across a 180 rotation relative to a front animal view, may just re ect lower responses to a back animal view. To better characterize the underlying representations and examine which representations may lead to our observed results, we simulated putative neural responses and predicted the resultant fMRI responses in a voxel. In the model, each voxel contains a mixture of neural populations, each of which is tuned to a different object view.

kalanit grill-spector Figure 6.7. Simul ations predicting fMRI responses of putative voxels containing a mixture of view-dependent neural populations.

Left, Schematic illustration of the view tuning and distribution of neural populations tuned to different views in a voxel. For illustration purposes we show a putative voxel with 4 neural populations. Right, Result of model simulations illustrating the predicted fMRI-A data.

In all panels, the model includes 6 Gaussians tuned to speci c views around the viewing circle, separated 60 apart. Across columns, the view tuning width varies. Across rows, the distribution of neural populations preferring speci c views varies.

Diamond, Responses without adaptation (black for back view, and gray for front view). Lines, Response after adaptation with a front view (dashed gray line) or back view (solid black line). (a) Mixture of view-dependent neural populations that are equally distributed in a voxel.

Narrower tuning (left) shows recovery from fMRI-A for smaller rotations than wider view tuning (right). This model predicts the same pattern of recovery from adaptation when adapting with the front or back view. (b) Mixture of view-dependent neural populations in a voxel with a higher proportion of neurons that prefer the front view.

The number on the right indicates the ratio between the percentages neurons tuned to the front versus the back view. Top row, ratio = 1.2.

Bottom row, ratio = 1.4. Because there are more neurons tuned to the front view in this model, it predicts higher BOLD responses to frontal views without adaptation (gray vs.

black diamonds) and a atter pro le of fMRI-A across rotations when adapting with the front view. Adapted from Andreson, Vinberg, and Grill-Spector (in press)..

(Fig. 6.7 and And none for none resen et al.

2008, in press). fMRI responses were modeled to be proportional to the sum of responses across all neural populations in a voxel. We simulated the fMRI responses in fMRI-A experiments for a set of putative voxels that varied in the view-tuning width of neural populations, the preferred view of different neural populations, the number of different neural populations, and the distribution of populations tuned to different views within a voxel.

Results of the simulations indicate that two main parameters affected the pattern of fMRI data: (1) the view-tuning width of the neural population, and (2) the proportion of neurons in a voxel that prefer a speci c object view..
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