Flexible contextual modulation of naturalistic texture perception in peripheral vision

Herrera, Daniel; Coen-Cagli, Ruben; Gómez-Sena, Leonel

doi:10.1101/2020.01.24.918813

Cited by 3 publications

(4 citation statements)

References 133 publications

(284 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flankers outside Bouma's window can suppress crowding up to the performance level of the unflanked target ( Figure 2f , grid condition). Similar effects have been shown previously with various stimuli such as Verniers ( Manassi et al, 2012 ; Manassi et al, 2013 ; Manassi et al, 2015 ; Manassi et al, 2016 ; Sayim et al, 2010 ), Gabors ( Levi & Carney, 2009 ; Livne & Sagi, 2007 ; Maus, Fischer, & Whitney, 2011 ; Saarela et al, 2009 ; Saarela & Herzog, 2008 ), shapes ( Kimchi & Pirkner, 2015 ), letters ( Reuther & Chakravarthi, 2014 ; Saarela et al, 2010 ), textures ( Herrera-Esposito, Coen-Cagli, & Gomez-Sena, 2020 ), as well as in haptics ( Overvliet & Sayim, 2016 ) and audition ( Oberfeld & Stahn, 2012 ). Feature similarity is important but not decisive because strong crowding can also occur with flankers of different contrast polarity and color ( Manassi et al, 2012 ; Sayim et al, 2008 ).…”

Section: Discussionsupporting

confidence: 87%

“…A model that can explain the major characteristics of crowding, in a nutshell, does not exist yet. We are, however, optimistic that such a model exists, since crowding shows universal characteristics across all types of stimuli ( Herrera-Esposito et al, 2020 ; Herzog et al, 2015 ; Kimchi & Pirkner, 2015 ; Levi & Carney, 2009 ; Pelli, Palomares, & Majaj, 2004 ; Reuther & Chakravarthi, 2014 ; Saarela et al, 2009 ; van den Berg et al, 2007 ; Wallace & Tjan, 2011 ), tasks ( Farzin, Rivera, & Whitney, 2009 ; Fischer & Whitney, 2011 ; Yeh, He, S., & Cavanagh, 2012 ), and modalities ( Oberfeld & Stahn, 2012 ; Overvliet & Sayim, 2016 ). Understanding crowding may unearth the strategies that are used to make sense of the outer world.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dissecting (un)crowding

et al. 2021

View full text Add to dashboard Cite

In crowding, perception of a target deteriorates in the presence of nearby flankers. Surprisingly, perception can be rescued from crowding if additional flankers are added (uncrowding). Uncrowding is a major challenge for all classic models of crowding and vision in general, because the global configuration of the entire stimulus is crucial. However, it is unclear which characteristics of the configuration impact (un)crowding. Here, we systematically dissected flanker configurations and showed that (un)crowding cannot be easily explained by the effects of the sub-parts or low-level features of the stimulus configuration. Our modeling results suggest that (un)crowding requires global processing. These results are well in line with previous studies showing the importance of global aspects in crowding.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Dissecting (un)crowding

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In this paper we evaluate how similar these models are, via their derived stimuli, with a set of controlled human psychophysics experiments where we vary the retinal eccentricity of the stimuli. are motivated by empirical work that suggests that the human visual periphery represents input in a texture-like scrambled way, that can appear quite different than how information is processed in the fovea (Rosenholtz, 2016;Stewart et al, 2020;Herrera-Esposito et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Harrington¹,

Deza²

2022

Preprint

View full text Add to dashboard Cite

Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise . The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations . Despite appearing closer to human visual perception, it is unclear if the constraints in robust DNN representations match biological constraints found in human vision. Human vision seems to rely on texture-based/summary statistic representations in the periphery, which have been shown to explain phenomena such as crowding (Balas et al., 2009) and performance on visual search tasks (Rosenholtz et al., 2012). To understand how adversarially robust optimizations/representations compare to human vision, we performed a psychophysics experiment using a metamer task similar to Freeman & Simoncelli ( 2011); Wallis et al. (2019); Deza et al. (2019b)where we evaluated how well human observers could distinguish between images synthesized to match adversarially robust representations compared to nonrobust representations and a texture synthesis model of peripheral vision (Texforms (Long et al., 2018)). We found that the discriminability of robust representation and texture model images decreased to near chance performance as stimuli were presented farther in the periphery. Moreover, performance on robust and texture-model images showed similar trends within participants, while performance on non-robust representations changed minimally across the visual field. These results together suggest that (1) adversarially robust representations capture peripheral computation better than non-robust representations and (2) robust representations capture peripheral computation similar to current state-of-the-art texture peripheral vision models. More broadly, our findings support the idea that localized texture summary statistic representations may drive human invariance to adversarial perturbations and that the incorporation of such representations in DNNs could give rise to useful properties like adversarial robustness. Link to

show abstract

“…For this reason, PS textures, often called "naturalistic", have been instrumental to understanding image processing in the visual cortex beyond area V1 [12,30,59,60,31]. The PS texture model also accounts for various aspects of visual perception including categorization [2], crowding [3] and visual search [39], and has been proposed as a general model of peripheral vision [11,38] despite some limitations [52,54,21]. However, different from simple Gaussian textures, PS textures cannot be easily modeled, and it is difficult to identify perceptually relevant axes in the space of PS summary statistics.…”

Section: Introductionmentioning

confidence: 99%

Texture Interpolation for Probing Visual Perception

Vacher,

Davila,

Kohn

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Texture synthesis models are important to understand visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental to understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (i.e. the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. The comparison to alternative interpolation methods suggests that ours matches more closely the geometry of texture perception, and is better suited to study its statistical nature. We demonstrate our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys. * https://jonathanvacher.github.io Preprint. Under review.

show abstract

Flexible contextual modulation of naturalistic texture perception in peripheral vision

Cited by 3 publications

References 133 publications

Dissecting (un)crowding

Dissecting (un)crowding

Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks

Texture Interpolation for Probing Visual Perception

Contact Info

Product

Resources

About