Deep Neural Networks for Modeling Visual Perceptual Learning
Open accessAbstract
Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well -known instance of deep neural network (DNN), whereas not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could transfer asymmetrically to coarse discriminations when the stimulus conditions varied. Consistent with the behavioral findings, the distribution of plasticity moved toward lower layers when task precision increased and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL, can serve as a test bed for theories, and assists in generating predictions for physiological investigations. SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced behavioral and physiological patterns similar to those found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition, but was not designed specifically for VPL; however, it fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology.
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Computational modeling shows that models trained under higher-precision discrimination conditions generalize more broadly to lower-precision conditions than vice versa.
“Computational modeling is also consistent with this possibility, showing that models trained under higher-precision discrimination conditions generalize more broadly to lower-precision conditions than vice versa (Wenliang & Seitz, 2018).”
From The asymmetry of working memory training transfer: A systematic review and meta-analysis (2026) · cites this paper as Wenliang & Seitz, 2018
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“To ensure that units were truly driven by the stimuli, only units with a mean firing rate greater than 0.001 before and after training were included in the analyses 19 .”
InfluentialApplication of Machine Learning Models for Tracking Participant Skills in Cognitive Training
Sandeep, S., Shelton, C. R., Pahor, A., Jaeggi, S. M., & Seitz, A. R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01532 · Cited by 12 · see citing works
“There are increasing examples of how artificial neural networks are being studied to understand how information may be represented in biological neural networks (Kriegeskorte, 2015) as well to understand the mechanisms of neural plasticity related to learning (Wenliang and Seitz, 2018).”
BackgroundSelf-citationHulse, S. V., Renoult, J. P., & Mendelson, T. C. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101486 · Cited by 13 · see citing works
Can multisensory training aid visual learning? A computational investigation
Jacobs, R. A., & Xu, C. (2019) · Journal of Vision · doi:10.1167/19.11.1 · Cited by 12 · see citing works
“Previous researchers have found that processing in networks using convolution and pooling resembles biological visual processing in intriguing ways (Krie-geskorte, 2015; Wenliang & Seitz, 2018; Yamins & DiCarlo, 2016).”
BackgroundAsymmetric stimulus representations bias visual perceptual learning
Laamerad, P., Awada, A., Pack, C. C., & Bakhtiari, S. (2024) · Journal of Vision · doi:10.1167/jov.24.1.10 · Cited by 11 · see citing works
“Previously proposed theories of VPL have primarily suggested optimal changes either in the sensory representations (referred to as retuning) (Karni & Sagi, 1991; Wenliang & Seitz, 2018) or in the readout of sensory neurons (referred to as reweighting) (Lu & Dosher, 2022; Sotiropoulos et al., 2011).”
BackgroundMethodInfluentialCan machine learning account for human visual object shape similarity judgments?
German, J., & Jacobs, R. A. (2020) · Vision Research · doi:10.1016/j.visres.2019.12.001 · Cited by 11 · see citing works
Liu, Y. H., Zhu, J., Constantinidis, C., & Zhou, X. (2021) · iScience · doi:10.1016/j.isci.2021.103178 · Cited by 11 · see citing works
“Examining RNN activity during the course of training the network has also shed insights on processes such as perceptual andmotor learning (Feulner and Clopath, 2021; Wenliang and Seitz, 2018) and the emergence, after training, of properties encountered in neural circuits such as grid-like representations, low-dimensio…”
BackgroundMethodEffects of daily training amount on visual motion perceptual learning
Song, Y., Chen, N., & Fang, F. (2021) · Journal of Vision · doi:10.1167/jov.21.4.6 · Cited by 11 · see citing works
“The idea that training precision modulates the degree of transfer in perceptual learning has been suggested in earlier psychophysical studies (Ahissar & Hochstein, 1997; Jeter et al., 2009) and is recently modeled using a deep neural network (Wenliang & Seitz, 2018).”
MethodStimulation of the ventral tegmental area induces visual cortical plasticity at the neuronal level
Herpers, J., Arsenault, J. T., Vanduffel, W., & Vogels, R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109998 · Cited by 12 · see citing works
Perceptual Learning: Policy Insights From Basic Research to Real-World Applications
Seitz, A. R., Sekuler, A. B., Dosher, B. A., Wright, B. A., Huang, C., Green, C. S., Pack, C. C., Sagi, D., Levi, D. M., Tadin, D., et al. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231195268 · Cited by 8 · see citing works
“Speci fi city found in early PL research may have resulted from targeting single features, for which there is not a computational need or bene fi t to generalizing; this is also true of arti fi cial neural networks trained on PL tasks (Wenliang & Seitz, 2018).”
MethodSelf-citationEYE-LASH IN THE LACRIMAL PUNCTUM
Boase, A. J. (1949) · British Journal of Ophthalmology · doi:10.1136/bjo.33.8.513 · Cited by 7 · see citing works
Minimum perturbation theory of deep perceptual learning
Shan, H., & Sompolinsky, H. (2022) · Physical review. E · doi:10.1103/physreve.106.064406 · Cited by 7 · see citing works
Time courses of brain plasticity underpinning visual motion perceptual learning
Song, Y., Wang, Q., & Fang, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120897 · Cited by 6 · see citing works
“Future research could further explore this issue in human subjects, especially with the aid of deep neural networks (Wu and Liang, 2010; Wenliang and Seitz, 2018; Manenti et al., 2023) and intracranial recordings (e.g., stereoelectroencephalography, sEEG) that can provide”
BackgroundProfiles of visual perceptual learning in feature space
Shen, S., Sun, Y., Lu, J., Li, C., Chen, Q., Mo, C., Fang, F., & Zhang, X. (2024) · iScience · doi:10.1016/j.isci.2024.109128 · Cited by 6 · see citing works
“Similar to the deep learning model from Wenliang and Seitz, 68 we took original five conv layers and discarded the last two fc layers to reduce model complexity for the AlexNet-GOD.”
MethodResultJacques, T., & Seitz, A. R. (2020) · Vision Research · doi:10.1016/j.visres.2020.02.003 · Cited by 6 · see citing works
Self-citationDelius, J. D., & Delius, J. A. M. (2019) · Scientific Reports · doi:10.1038/s41598-019-50212-1 · Cited by 7 · see citing works
Emergent Virtual Analytics: Modeling Contextual Control of Derived Stimulus Relations
Ninness, C., & Ninness, S. K. (2020) · Behavior and Social Issues · doi:10.1007/s42822-020-00032-0 · Cited by 6 · see citing works
“…Ninness et al., 2013; Tovar & Westermann, 2017), physiological (e.g., Landers, Brusso, Cavanaugh, & Collmus, 2016; Ninness et al., 2012; Plis et al., 2014; Wenliang & Seitz, 2018), and epidemiological (e.g., Flouris & Duffy, 2006; Rodríguez-González, Zanin, & Menasalvas-Ruiz, 2019) investigations.”
Perceptual learning improves discrimination but does not reduce distortions in appearance
Szpiro, S., Burlingham, C. S., Simoncelli, E. P., & Carrasco, M. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012980 · Cited by 5 · see citing works
PLFest: A New Platform for Accessible, Reproducible, and Open Perceptual Learning Research
Jayakumar, S., Maniglia, M., Guan, Z., Green, C. S., & Seitz, A. R. (2024) · Journal of Cognitive Enhancement · doi:10.1007/s41465-024-00299-w · Cited by 5 · see citing works
“Additional work by Seitz utilizing deep neural networks (Wenliang & Seitz, 2018) has shown that differences in training thresholds are sufficient to account for the differences in neural findings found between Schoups (Schoups et al., 1995; Schoups et al., 2001) and Ghose (Ghose et al., 2002).”
Self-citationPerceptual Learning: How Does the Visual Circuit Change through Experience?
Seitz, A. R. (2020) · Current Biology · doi:10.1016/j.cub.2020.08.097 · Cited by 5 · see citing works
Self-citationTalwar, A., Huys, Q. J. M., Cormack, F. K., & Roiser, J. P. (2024) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-024-01223-7 · Cited by 5 · see citing works
“Despite the superior performance of backpropagation-based algorithms for predicting human performance in a range of tasks (Kell et al., 2018; Wenliang & Seitz, 2018), we acknowledge the historical scepticism around its implementation in the brain owing to biological constraints (Crick, 1989).”
BackgroundLu, X., Yuan, Z., Zhang, Y., Ai, H., Cheng, S., Ge, Y., Fang, F., & Chen, N. (2024) · Science China Technological Sciences · doi:10.1007/s11431-024-2748-3 · Cited by 4 · see citing works
“(5) [22]. where w i is the i th element in the N -dimensional weight vector at each layer in the pre-trained model. δw indicates the change in the weight vector after training.”
Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations
Neri, P. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.04.023 · Cited by 4 · see citing works
Can Deep Learning Model Perceptual Learning?
Bakhtiari, S. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2209-18.2018 · Cited by 4 · see citing works
“The RHT does not suggest any mechanism for the training process that would cause selective plasticity dictated by task precision, but in a recent study, Wenliang and Seitz (2018) showed that the properties of the RHT emerge from a deep neural network (DNN), with appropriate train-ReceivedAug ing.”
BackgroundMethodInfluentialInterfering with a memory without erasing its trace
Lange, G., Senden, M., Radermacher, A., & Weerd, P. D. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.09.027 · Cited by 4 · see citing works
Characterizing emergent representations in a space of candidate learning rules for deep networks
Cao, Y., Summerfield, C., & Saxe, A. (2020) · neural information processing systems · Cited by 4 · see citing works
A Structural Oriented Training Method for GAN Based Fast Compressed Sensing MRI
An, H., & Zhang, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34110-7_40 · Cited by 4 · see citing works
If deep learning is the answer, then what is the question?Preprint
Saxe, A., Nelli, S., & Summerfield, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07580 · Cited by 8 · see citing works
“This opens the door to studies of perceptual learning that can attempt to confirm or refute these predictions 87 .”
BackgroundResultWei, B., Zhao, Y., Hao, K., & Gao, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03391 · Cited by 4 · see citing works
Wenger, M., Maimon, A., Yizhar, O., Snir, A., Sasson, Y., & Amedi, A. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1353490 · Cited by 4 · see citing works
“With regard to perceptual learning, specifically in the visual domain, research findings have shown physiological and behavioral schemas corresponding to those seen in human experiments in trained DNNs (Wenliang and Seitz, 2018; Bakhtiari, 2019).”
BackgroundInfluentialA New Look at Visual System Plasticity
Maniglia, M., & Seitz, A. R. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.11.002 · Cited by 3 · see citing works
Self-citationPerceptual learning: Breaking specificity by variability
Vogels, R. (2023) · Current Biology · doi:10.1016/j.cub.2023.01.025 · Cited by 3 · see citing works
Sörensen, L. K. A., DiCarlo, J. J., & Kar, K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.27.630539 · Cited by 3 · see citing works
Jang, H., & Tong, F. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013529 · Cited by 2 · see citing works
Zhu, J.-P., & Zhang, J.-Y. (2024) · Journal of Vision · doi:10.1167/jov.24.5.2 · Cited by 2 · see citing works
“Recently, Manenti et al. (2023) trained a deep neural network model designed by Wenliang and Seitz (2018) under high and low task-irrelevant variability conditions, indicating that the networks develop invariant representations of the task-irrelevant feature when trained with highly varied inputs.”
BackgroundDosher, B. A., & Lu, Z. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.11 · Cited by 2 · see citing works
A computational deep learning investigation of animacy perception in the human brain
Duyck, S., Costantino, A. I., Bracci, S., & de Beeck, H. O. (2024) · Communications Biology · doi:10.1038/s42003-024-07415-8 · Cited by 2 · see citing works
ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating GridPreprint
Sun, E., & Dekel, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09019 · Cited by 2 · see citing works
Perceptual learning improves discrimination while distorting appearancePreprint
Szpiro, S., Burlingham, C. S., Simoncelli, E. P., & Carrasco, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.08.507104 · Cited by 2 · see citing works
Cheng, Y.-A., Sanayei, M., Chen, X., Jia, K., Li, S., Fang, F., Watanabe, T., Thiele, A., & Zhang, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.13.566963 · Cited by 2 · see citing works
Dantec, C. C. L., & Seitz, A. R. (2020) · Journal of Vision · doi:10.1167/jov.20.6.7 · Cited by 1 · see citing works
“…single unit recording studies in animals (Adab & Vogels, 2011; Schoups et al., 2001), and computational studies (Poggio, Fahle, & Edelman, 1992; Wenliang & Seitz, 2018) provide complementary evidence that perceptual learning can involve plasticity in V1, although later brain regions are also…”
BackgroundSelf-citationHierarchical and parallel processing: From primate visual cortex to artificial intelligence
Chen, N., Ai, H., Chen, J., & Zhang, P. (2025) · Science China Technological Sciences · doi:10.1007/s11431-025-2971-2 · Cited by 1 · see citing works
da Costa, D., Goebel, R., & Senden, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.02.466130 · Cited by 1 · see citing works
“Wenliang and Seitz (2018) have, for example, used convolutional neural networks (CNNs) to study plasticity resulting from visual perceptual learning.”
BackgroundYang, Y., Zhuo, Y., Zuo, Z., Zhuo, T., & Chen, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.02.573923 · Cited by 1 · see citing works
Huang, L., Shen, S., Sun, Y., Ou, S., Zhang, R., de Lange, F. P., & Zhang, X. (2025) · eLife · doi:10.1101/2024.08.26.609781 · Cited by 1 · see citing works
The asymmetry of working memory training transfer: A systematic review and meta-analysis
Fu, J., Zhou, Y., Yuan, M., & Cai, Y. (2026) · Psychonomic Bulletin & Review · doi:10.3758/s13423-026-02994-5
“Computational modeling is also consistent with this possibility, showing that models trained under higher-precision discrimination conditions generalize more broadly to lower-precision conditions than vice versa (Wenliang & Seitz, 2018).”
Moore, E. (2019) · University of Minnesota Digital Conservancy (University of Minnesota)
Beyond Capacity: The Importance of Attentional Coordination
Strong, R. W. (2019) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Perceptual learning: experimental and computational considerations
Dunn-Weiss, E. (2020)
Neri, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.02.482629
“Even if we accept that some form of back-propagation may be operating in biological substrates during learning [41], the human visual system would not a ff ord this process unfettered access to every neuronal connection throughout its architecture from early visual cortex to decision-making…”
BackgroundKossowsky, H., & Nisky, I. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.26.550361
Yang, Y., Zhuo, Y., Zuo, Z., Zhuo, T., & Chen, L. (2024) · eLife · doi:10.7554/elife.93959.1
Yang, Y., Zhuo, Y., Zuo, Z., Zhuo, T., & Chen, L. (2024) · eLife · doi:10.7554/elife.93959
Thiele, A., Chen, X., Sanayei, M., Chicharro, D., Distler, C., & Panzeri, S. (2024) · Journal of Cognitive Enhancement · doi:10.1007/s41465-024-00298-x
“This also differs from results obtained in a deep neural-network model of perceptual learning, which supports learning specificity for fine but not easy discriminations, but similar to our data argues for increasing changes in sensitivity with increasing hierarchy (Wenliang & Seitz, 2018).”
Trend and Pattern in Research on 6E Learning by Design in Science: Bibliometric Approach
Rochman, S., Rustaman, N. Y., Ramalis, T. R., Amri, K., Zukmadini, A. Y., & Supriyadi, S. (2024) · KnE Social Sciences · doi:10.18502/kss.v9i19.16537
Can Information About Stiffness Perception be Inferred from Action Signals Using Models?
Kossowsky, H., & Nisky, I. (2024) · eNeuro · doi:10.1523/eneuro.0495-23.2024
“We used k -fold validation, as in Wenliang and Seitz (2018), where we selected k = 10.”
Maniglia, M. (2024) · Brain Communications · doi:10.1093/braincomms/fcae468
“This mounting evidence led the field of VPL to embrace a tale of increasing complexity, with recent models and framework of VPL 16,19-21 describing learning as the product of different regions and mechanisms depending on the characteristics of the stimulus and the needs of the task.”
Yang, Y., Zhuo, Y., Zuo, Z., Zhuo, T., & Chen, L. (2025) · eLife · doi:10.7554/elife.93959.2
Center-surround inhibition by expectation: a neuro-computational accountPreprint
Huang, L., Shen, S., Sun, Y., Ou, S., Zhang, R., de Lange, F. P., & Zhang, X. (2025) · eLife · doi:10.7554/elife.107301.1
The curriculum effect in visual learning: The role of readout dimensionality
Volk, C., Pack, C. C., & Bakhtiari, S. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014553
“We modified the model from the version proposed by Wenliang and Seitz [17] to add skip connections from every layer of the model directly to the readout layer.”
InfluentialCenter-surround inhibition by expectation: a neuro-computational accountPreprint
Huang, L., Shen, S., Sun, Y., Ou, S., Zhang, R., de Lange, F. P., & Zhang, X. (2025) · eLife · doi:10.7554/elife.107301.2
- Authors
- Wenliang, L. K., & Seitz, A. R.
- Affiliations
- Gatsby Computational Neuroscience Unit (GB)
- University of California, Riverside (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2018-05-23
- Volume
- 38
- Issue
- 27
- Pages
- 6028-6044
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6031581
- OpenAlex
- W2804915272
- Cited by
- 83
- References
- 79
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 18 · 2023: 6 · 2022: 11 · 2021: 8 · 2020: 15 · 2019: 13 · 2018: 1 · 2017: 1
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Gatsby Charitable Foundation
- National Institutes of Health
- National Eye Institute (R01EY023582)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- visual perceptual learning, deep neural network, orientation discrimination, transfer of learning, visual hierarchy, computational models of learning, Gabor stimuli
- MeSH terms
- Deep Learning, Animals, Computer Simulation, Humans, Learning, Models, Neurological, Visual Perception
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