A Neuromathematical Model for Geometrical Optical Illusions

Franceschiello, Benedetta; Sarti, Alessandro; Citti, Giovanna

doi:10.1007/s10851-017-0740-6

Cited by 27 publications

(47 citation statements)

References 49 publications

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“…The cortically inspired computational models introduced in (B. Franceschiello et al, 2019;Benedetta Franceschiello et al, 2017) have proven to be effective in reproducing the qualitative responses of the primary visual cortices in response to geometrical optical illusions. The model, tested in (B.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…The model, tested in (B. Franceschiello et al, 2019;Benedetta Franceschiello et al, 2017) from a purely image processing perspective, takes into account as a first step the orientation selection mechanisms discovered by Hubel and Wiesel (Hubel & Wiesel, 1962) and modelled computationally by Daugman (Daugman, 1985) and Jones and Palmer (Jones & Palmer, 1987). For a full description of the model, we refer readers to (B.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…For a full description of the model, we refer readers to (B. Franceschiello et al, 2019;Benedetta Franceschiello et al, 2017). Here, we briefly summarize the approach.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…A stable prediction of the misperception in GOIs could be explained by a model considering three main neurophysiological features (B. Franceschiello, Mashtakov, Citti, & Sarti, 2019;Benedetta Franceschiello, Sarti, & Citti, 2017): 1) orientation selectivity process performed by visual areas V1 and V2 (Hubel & Wiesel, 1962), 2) interference between receptive profiles of simple cells in these areas, and 3) long-range connectivity (Bosking, William H., 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Second, we establish the practice of tuning the computational models in (B. Franceschiello et al, 2019;Benedetta Franceschiello et al, 2017) using psychophysical data: computational models are strongly dependent on the choice of parameters. A part of them is only computationally driven, while another part represents neurophysiological correlates of the studied processes.…”

Section: Introductionmentioning

confidence: 99%

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A psychophysically-tuned computational model of human primary visual cortex produces geometric optical illusions

Retsa

Ariza

Noordanus

et al. 2020

Preprint

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AbstractGeometrical optical illusion (GOIs) are mismatches between physical stimuli and perception. GOIs provide an access point to study the interplay between sensation and perception, yet there is scant quantitative investigation of the extent to which different GOIs rely on similar or distinct brain mechanisms. We addressed this knowledge gap. First, 30 healthy adults reported quantitatively their perceptual biases with three GOIs, whose physical properties parametrically varied on a trial-by-trial basis. Biases observed with one GOI were unrelated to those observed with another GOI, suggestive of (partially) distinct underlying mechanisms. Next, we used these psychophysical results to tune a computational model of primary visual cortex that combines parameters of orientation, selectivity, intra-cortical connectivity, and long-range interactions. We showed that similar biases could be generated in-silico, mirroring those observed in humans. Such results provide a roadmap whereby computational modelling, informed by human psychophysics, can reveal likely mechanistic underpinnings of perception.

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Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

“…For a full description of the model, we refer readers to (B. Franceschiello et al, 2019;Benedetta Franceschiello et al, 2017). Here, we briefly summarize the approach.…”

Section: Computational Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A psychophysically-tuned computational model of human primary visual cortex produces geometric optical illusions

Retsa

Ariza

Noordanus

et al. 2020

Preprint

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A Cortical-Inspired Model for Orientation-Dependent Contrast Perception: A Link with Wilson-Cowan Equations

Bertalmío

Calatroni

Franceschi

et al. 2019

Lecture Notes in Computer Science

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We consider a differential model describing neuro-physiological contrast perception phenomena induced by surrounding orientations. The mathematical formulation relies on a cortical-inspired modelling [10] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [12,19,13]. Our model connects to Wilson-Cowan-type equations [23] and it is analogous to the one used in [3,2,14] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confirm the validity of the model, we report some numerical tests showing its ability to explain orientation-dependent phenomena (such as grating induction) and geometric-optical illusions [21,16] classically explained only by filtering-based techniques [6,18].

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