The visual system has an extraordinary capability to extract categorical information from complex natural scenes. For example, subjects are able to rapidly detect the presence of object categories such as animals or vehicles in new scenes that are presented very briefly. This is even true when subjects do not pay attention to the scenes and simultaneously perform an unrelated attentionally demanding task, a stark contrast to the capacity limitations predicted by most theories of visual attention. Here we show a neural basis for rapid natural scene categorization in the visual cortex, using functional magnetic resonance imaging and an object categorization task in which subjects detected the presence of people or cars in briefly presented natural scenes. The multi-voxel pattern of neural activity in the object-selective cortex evoked by the natural scenes contained information about the presence of the target category, even when the scenes were task-irrelevant and presented outside the focus of spatial attention. These findings indicate that the rapid detection of categorical information in natural scenes is mediated by a category-specific biasing mechanism in object-selective cortex that operates in parallel across the visual field, and biases information processing in favour of objects belonging to the target object category.
Each trial started with a spatial cue indicating the relevant stimulus locations (which were held constant within a run). This was followed by the four pictures presented for, on average, 130 ms. Presentation time of the pictures was adjusted for each subject to arrive at ~80% accuracy. The pictures were followed by perceptual masks (270 ms). The next trial started, on average, 1300 ms after the offset of the masks.
Figure 2. Schematic overview of analysis approach
Figure 2. Schematic overview of analysis approach
The approach of the multi-voxel pattern analysis was…
Figure 2. Schematic overview of analysis approach
The approach of the multi-voxel pattern analysis was to correlate patterns of activation to conditions in the main experiment (depicted on the left) with patterns of activation to conditions in the category localizer (depicted on the right). The thickness of the lines between patterns indicates the hypothesized strengths of the correlations. For example, higher correlations were expected between patterns evoked by scenes containing people and isolated body pictures (within-category comparison) than with isolated car pictures (between-category comparison). This approach allowed us to measure the influence of search task and spatial attention on category information in visual cortex.
Figure 3. Results of multi-voxel pattern analysis
Figure 3. Results of multi-voxel pattern analysis
a, The top panel shows the ventral cluster…
Figure 3. Results of multi-voxel pattern analysis
a, The top panel shows the ventral cluster of object-selective cortex (intact vs. scrambled objects) in a group-average analysis at P < 0.005 (Talairach coordinates of peak: x = 35, y = −41, z = −18). The lower panel shows category information as a function of Category, Task and Attention in individually-defined object-selective cortex. Category information was calculated by taking the difference between within-category comparisons and between-category comparisons, and reflects the amount of category information in multi-voxel patterns of activation (see Fig. 2 and Methods summary). Error bars indicate ± s.e.m. b, The top panel shows the result of the Category × Task contrast in the group-average searchlight analysis at P < 0.005 (uncorrected). The lower panel shows category information as a function of Category, Task and Attention in the sphere surrounding the peak voxel of the activation from the group-average searchlight analysis (Talairach coordinates of peak: x = 35, y = −44, z = −18). Error bars indicate ± s.e.m.
Figure 3. Results of multi-voxel pattern analysis
Figure 3. Results of multi-voxel pattern analysis
a, The top panel shows the ventral cluster…
Figure 3. Results of multi-voxel pattern analysis
a, The top panel shows the ventral cluster of object-selective cortex (intact vs. scrambled objects) in a group-average analysis at P < 0.005 (Talairach coordinates of peak: x = 35, y = −41, z = −18). The lower panel shows category information as a function of Category, Task and Attention in individually-defined object-selective cortex. Category information was calculated by taking the difference between within-category comparisons and between-category comparisons, and reflects the amount of category information in multi-voxel patterns of activation (see Fig. 2 and Methods summary). Error bars indicate ± s.e.m. b, The top panel shows the result of the Category × Task contrast in the group-average searchlight analysis at P < 0.005 (uncorrected). The lower panel shows category information as a function of Category, Task and Attention in the sphere surrounding the peak voxel of the activation from the group-average searchlight analysis (Talairach coordinates of peak: x = 35, y = −44, z = −18). Error bars indicate ± s.e.m.
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