Real-world size of objects serves as an axis of object space

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.

Body size as a metric for the affordable world

The physical body of an organism serves as a vital interface for interactions with its environment. Here, we investigated the impact of human body size on the perception of action possibilities (affordances) offered by the environment. We found that the body size delineated a distinct boundary on affordances, dividing objects of continuous real-world sizes into two discrete categories with each affording distinct action sets. Additionally, the boundary shifted with imagined body sizes, suggesting a causal link between body size and affordance perception. Intriguingly, ChatGPT, a large language model lacking physical embodiment, exhibited a modest yet comparable affordance boundary at the scale of human body size, suggesting the boundary is not exclusively derived from organism-environment interactions. A subsequent fMRI experiment offered preliminary evidence of affordance processing exclusively for objects within the body size range, but not for those beyond. This suggests that only objects capable of being manipulated are the objects capable of offering affordance in the eyes of an organism. In summary, our study suggests a novel definition of object-ness in an affordance-based context, advocating the concept of embodied cognition in understanding the emergence of intelligence constrained by an organism's physical attributes.

Cortical topographic motifs emerge in a self-organized map of object space

The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with a large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflect a smooth mapping of a unified representational space.

Spikiness and animacy as potential organizing principles of human ventral visual cortex

Considerable research has been devoted to understanding the fundamental organizing principles of the ventral visual pathway. A recent study revealed a series of 3-4 topographical maps arranged along the macaque inferotemporal (IT) cortex. The maps articulated a two-dimensional space based on the spikiness and animacy of visual objects, with "inanimate-spiky" and "inanimate-stubby" regions of the maps constituting two previously unidentified cortical networks. The goal of our study was to determine whether a similar functional organization might exist in human IT. To address this question, we presented the same object stimuli and images from "classic" object categories (bodies, faces, houses) to humans while recording fMRI activity at 7 Tesla. Contrasts designed to reveal the spikiness-animacy object space evoked extensive significant activation across human IT. However, unlike the macaque, we did not observe a clear sequence of complete maps, and selectivity for the spikiness-animacy space was deeply and mutually entangled with category-selectivity. Instead, we observed multiple new stimulus preferences in category-selective regions, including functional sub-structure related to object spikiness in scene-selective cortex. Taken together, these findings highlight spikiness as a promising organizing principle of human IT and provide new insights into the role of category-selective regions in visual object processing.