Roles of Category, Shape, and Spatial Frequency in Shaping Animal and Tool Selectivity in the Occipitotemporal Cortex

Abstract

Does the nature of representation in the category-selective regions in the occipitotemporal cortex reflect visual or conceptual properties? Previous research showed that natural variability in visual features across categories, quantified by image gist statistics, is highly correlated with the different neural responses observed in the occipitotemporal cortex. Using fMRI, we examined whether category selectivity for animals and tools would remain, when image gist statistics were comparable across categories. Critically, we investigated how category, shape, and spatial frequency may contribute to the category selectivity in the animal-and tool-selective regions. Female and male human observers viewed low-or highpassed images of round or elongated animals and tools that shared comparable gist statistics in the main experiment, and animal and tool images of naturally varied gist statistics in a separate localizer. Univariate analysis revealed robust category-selective responses for images with comparable gist statistics across categories. Successful classification for category (animals/tools), shape (round/elongated), and spatial frequency (low/high) was also observed, with highest classification accuracy for category. Representational similarity analyses further revealed that the activation patterns in the animal-selective regions were most correlated with a model that represents only animal information, whereas the activation patterns in the tool-selective regions were most correlated with a model that represents only tool information, suggesting that these regions selectively represent information of only animals or tools. Together, in addition to visual features, the distinction between animal and tool representations in the occipitotemporal cortex are likely shaped by higher-level conceptual influences such as categorization or interpretation of visual inputs. Significance Statement: Since different categories often vary systematically in both visual and conceptual features, it remains unclear what kinds of information determine categoryselective responses in the occipitotemporal cortex. To minimize the influences of low-and mid-level visual features, here we used a diverse image set of animals and tools that shared comparable gist statistics. We manipulated category (animals/tools), shape (round/elongated) and spatial frequency (low/high), and found that the representational content of the animal-and tool-selective regions is primarily determined by their preferred categories only, regardless of shape or spatial frequency. Our results show that categoryselective responses in the occipitotemporal cortex are influenced by higher-level processing such as categorization or interpretation of visual inputs, and highlight the specificity in these category-selective regions.

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

“…A final note is that our analysis did not find evidence for selective neural responses to several visual features and categories for which ventral visual pathway specializations have been proposed in the past, including animals 45 (which are well represented in the stimulus set) and stubby-shaped and spikey-shaped objects 46 . We also did not see evidence for previously-proposed selectivities for small inanimate objects 45, 47 or tools 48, 49 , although these selectivities may be located more on the lateral than ventral surface of the brain, outside the search window used here. Of course, there are many reasons why selectivities that exist in the brain might not be detected using fMRI, but the failure of previous findings from fMRI to emerge from the current analysis raises questions about whether those selectivities might already be better accounted for by the components found here 50 .…”

A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition

“…A final note is that our analysis did not find evidence for selective neural responses to several visual features and categories for which ventral visual pathway specializations have been proposed in the past, including animals 45 (which are well represented in the stimulus set) and stubby-shaped and spikey-shaped objects. 46 We also did not see evidence for previously proposed selectivities for small inanimate objects 45,47 or tools, 48,49 although these selectivities may be located more on the lateral than ventral surface of the brain, outside the search window used here. Of course, there are many reasons why selectivities that exist in the brain might not be detected using fMRI, but the failure of previous findings from fMRI to emerge from the current analysis raises questions about whether those selectivities might be better accounted for by the components found here.…”

Two ‘what’ pathways for action and object recognition

“…Topographical alignment of action and object representations in the LOTC For each level of representationsingle entities, their movements, action directedness, and conceptual action aspectscategorical preferences are observed in dorsal and ventral subregions of the LOTC: sensitivity to animate entities, body parts, their canonical movements, orientation of persons relative to each other, and actions directed toward persons is preferentially found in dorsal rather than ventral subregions of the LOTC, whereas sensitivity to inanimate entities like tools, action-specific tool motion, and actions directed towards or involving manipulable objects is found in ventral rather than dorsal subregions of the LOTC [4,5,24,27,29,41,42,[48][49][50][51] (Figure 1). The domain-dependent alignment of action and object representations can be explained by the specific roles that objects play in different kinds of actions and their importance for action identification: Recognizing social, person-directed actions like greeting relies on information such as body posture and movements, person orientation, as well as other socially relevant cues like facial expressions.…”