Published February 13, 2025
| Version v1
Journal article
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Orthogonal neural representations support perceptual judgments of natural stimuli
- 1. University of Chicago
- 2. University of Pennsylvania
Description
In natural visually guided behavior, observers must separate relevant information from a barrage of irrelevant information. Many studies have investigated the neural underpinnings of this ability using artificial stimuli presented on blank backgrounds. Natural images, however, contain task-irrelevant background elements that might interfere with the perception of object features. Recent studies suggest that visual feature estimation can be modeled through the linear decoding of task-relevant information from visual cortex. So, if the representations of task-relevant and irrelevant features are not orthogonal in the neural population, then variation in the task-irrelevant features would impair task performance. We tested this hypothesis using human psychophysics and monkey neurophysiology combined with parametrically variable naturalistic stimuli. We demonstrate that (1) the neural representation of one feature (the position of an object) in visual area V4 is orthogonal to those of several background features, (2) the ability of human observers to precisely judge object position was largely unaffected by those background features, and (3) many features of the object and the background (and of objects from a separate stimulus set) are orthogonally represented in V4 neural population responses. Our observations are consistent with the hypothesis that orthogonal neural representations can support stable perception of object features despite the richness of natural visual scenes.
Data availability
The data and code that generate the figures in this study have been deposited in a public Github repository https://github.com/ramanujansrinath/UntanglingBananas. MATLAB code for creating and displaying the images for human psychophysical experiments, as well as analyzing the raw data from these experiments, can be found at https://github.com/AmyMNi/NaturalImageThresholds. Request for further information should be directed to and will be fulfilled by the corresponding author David H. Brainard in consultation with the other authors.Files
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Additional details
Identifiers
- DOI
- 10.1038/s41598-025-88910-8
- Other
- oai:uchicago.tind.io:14556
Funding
- Schmidt Sciences, LLC
- Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship
- Simons Foundation
- Simons Collaboration on the Global Brain award
- Simons Foundation
- postdoctoral fellowship
- National Institutes of Health
- R01EY022930
- National Institutes of Health
- R01EY034723
- National Institutes of Health
- RF1NS121913
- National Institutes of Health
- K99NS118117
- National Institutes of Health
- K99EY035362