Property-defined regions fit the traditional category-selective regions in the higher visual cortex
Poster No:
2079
Submission Type:
Abstract Submission
Authors:
Qiande Zhao1, Deying Li2, Congying Chu3, Lingzhong Fan4
Institutions:
1Brainnetome center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2Institute of Automation, Chinese Academy of Science, Beijing, China, 3Institute of Automation, Chinese Academy of Sciences, Beijing, China, 4Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
First Author:
Co-Author(s):
Lingzhong Fan
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China
Introduction:
Understanding how the visual cortex encodes object is a core issue in neuroscience. Recent studies have focused on discrete object categories[1] or continuous dimensions[2], which could not be reconciled in the current views. Using the THINGS datasets[3], we provide an explainable object property space to study the relationship between object category and property-defined dimensions.
Methods:
The object property space was constructed using the human ratings of properties. Then, We applied voxel-wise encoding models to map property space to human brain fMRI data. This encoding model revealed the property representations in the brain. Further, the effect of object property space on the category representations was analyzed through a brain-like visual computing paradigm.
Results:
Dimension reduction of human object property ratings identified three key dimensions: grasp, animacy, and feeling, which organize an object property space. Then, through encoding model weights, we produced property maps, highlighting the role of property in category regions. And clustering analysis using these maps parcellated the higher-level visual cortex into three regions, each associated with specific category regions and property tunings. Further, we build mappings between brain responses and deep layer features of TDANN (topographic deep artificial neural network)[4], an advanced computational model to simulate ventral visual cortex. And deep layer units were split into subsets corresponding to each brain cluster based on the mappings we obtained. Finally, lesion or stimulation of these cluster-related unit sets lead to performance change of different category classifications.
Conclusions:
In conclusion, this study analyzes the object representations from the object property space, and property-defined new regions could interpret the representations of category-selective regions. These results give us a more comprehensive view about the neural mechanism of object recognition.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Methods Development 2
Perception, Attention and Motor Behavior:
Perception: Visual 1
Keywords:
Computational Neuroscience
Modeling
Vision
1|2Indicates the priority used for review
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[2] Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature reviews. Neuroscience, 15(8), 536–548.
[3] Hebart, M. N., Contier, O., Teichmann, L., Rockter, A. H., Zheng, C. Y., Kidder, A., Corriveau, A., Vaziri-Pashkam, M., & Baker, C. I. (2023). THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. eLife, 12, e82580.
[4] Margalit, E., Lee, H., Finzi, D., DiCarlo, J. J., Grill-Spector, K., & Yamins, D. L. K. (2024). A unifying framework for functional organization in early and higher ventral visual cortex. Neuron, 112(14), 2435–2451.e7.