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Biased orientation representations can be explained by experience with nonuniform training set statistics

Visual acuity is better for vertical and horizontal compared to other orientations. This cross-species phenomenon is often explained by “efficient coding,” whereby more neurons show sharper tuning for the orientations most common in natural vision. However, it is unclear if experience alone can acco...

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Autores principales: Henderson, Margaret, Serences, John T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354037/
https://www.ncbi.nlm.nih.gov/pubmed/34351397
http://dx.doi.org/10.1167/jov.21.8.10
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author Henderson, Margaret
Serences, John T.
author_facet Henderson, Margaret
Serences, John T.
author_sort Henderson, Margaret
collection PubMed
description Visual acuity is better for vertical and horizontal compared to other orientations. This cross-species phenomenon is often explained by “efficient coding,” whereby more neurons show sharper tuning for the orientations most common in natural vision. However, it is unclear if experience alone can account for such biases. Here, we measured orientation representations in a convolutional neural network, VGG-16, trained on modified versions of ImageNet (rotated by 0°, 22.5°, or 45° counterclockwise of upright). Discriminability for each model was highest near the orientations that were most common in the network's training set. Furthermore, there was an overrepresentation of narrowly tuned units selective for the most common orientations. These effects emerged in middle layers and increased with depth in the network, though this layer-wise pattern may depend on properties of the evaluation stimuli used. Biases emerged early in training, consistent with the possibility that nonuniform representations may play a functional role in the network's task performance. Together, our results suggest that biased orientation representations can emerge through experience with a nonuniform distribution of orientations, supporting the efficient coding hypothesis.
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spelling pubmed-83540372021-08-24 Biased orientation representations can be explained by experience with nonuniform training set statistics Henderson, Margaret Serences, John T. J Vis Article Visual acuity is better for vertical and horizontal compared to other orientations. This cross-species phenomenon is often explained by “efficient coding,” whereby more neurons show sharper tuning for the orientations most common in natural vision. However, it is unclear if experience alone can account for such biases. Here, we measured orientation representations in a convolutional neural network, VGG-16, trained on modified versions of ImageNet (rotated by 0°, 22.5°, or 45° counterclockwise of upright). Discriminability for each model was highest near the orientations that were most common in the network's training set. Furthermore, there was an overrepresentation of narrowly tuned units selective for the most common orientations. These effects emerged in middle layers and increased with depth in the network, though this layer-wise pattern may depend on properties of the evaluation stimuli used. Biases emerged early in training, consistent with the possibility that nonuniform representations may play a functional role in the network's task performance. Together, our results suggest that biased orientation representations can emerge through experience with a nonuniform distribution of orientations, supporting the efficient coding hypothesis. The Association for Research in Vision and Ophthalmology 2021-08-05 /pmc/articles/PMC8354037/ /pubmed/34351397 http://dx.doi.org/10.1167/jov.21.8.10 Text en Copyright 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Henderson, Margaret
Serences, John T.
Biased orientation representations can be explained by experience with nonuniform training set statistics
title Biased orientation representations can be explained by experience with nonuniform training set statistics
title_full Biased orientation representations can be explained by experience with nonuniform training set statistics
title_fullStr Biased orientation representations can be explained by experience with nonuniform training set statistics
title_full_unstemmed Biased orientation representations can be explained by experience with nonuniform training set statistics
title_short Biased orientation representations can be explained by experience with nonuniform training set statistics
title_sort biased orientation representations can be explained by experience with nonuniform training set statistics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354037/
https://www.ncbi.nlm.nih.gov/pubmed/34351397
http://dx.doi.org/10.1167/jov.21.8.10
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