Spatiotemporal neural dynamics of object recognition under uncertainty in humans
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eLife assessment
This study investigates the spatiotemporal characteristics of human brain activities during object recognition under noisy and ambiguous conditions. By using state-of-the-art data analysis and model-driven fusion of MEG and 7T, this work demonstrates distinct representational profiles in ventral and dorsal pathways, contributing new perspectives to our understanding of the neural implementation of object recognition under uncertainty.
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Abstract
While there is a wealth of knowledge about core object recognition—our ability to recognize clear, high-contrast object images—how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.
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eLife assessment
This study investigates the spatiotemporal characteristics of human brain activities during object recognition under noisy and ambiguous conditions. By using state-of-the-art data analysis and model-driven fusion of MEG and 7T, this work demonstrates distinct representational profiles in ventral and dorsal pathways, contributing new perspectives to our understanding of the neural implementation of object recognition under uncertainty.
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Reviewer #1 (Public Review):
The study employs state-of-art techniques and model-driven fusion of MEG and 7T to characterize the fine spatiotemporal profiles of object recognition in human brains when stimuli are noisy. By using two models, the recognition and the two-state models, to characterize the representational format, the work demonstrates that the ventral visual pathway is more toward two-state representation while the dorsal visual pathway tends to display the recognition-like profile. Overall it is an interesting work addressing an important question. My major concern is on the two selected models and whether they could be fairly compared to address the question. Moreover, some details need more clarification and statistical support.
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Reviewer #2 (Public Review):
This is an excellent study performed by a world-leading research group in the field of the neural mechanisms of perceptual processing. The strengths of this work are the application of the MEG-fMRI fusion approach that links spatial locations in fMRI and time points in MEG and rigorous model-based analyses. The weaknesses may be a lack of a more concise visual illustration of the main findings and an in-depth discussion of some of the findings. The weaknesses are minor and the authors' conclusions are well justified by their data.
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