A mechanistic account of visual discomfort
Creators: name: Penacchio, Olivier
name: Otazu, Xavier
name: Wilkins, Arnold J.
name: Haigh, Sarah M.
Publisher: name: Zenodo
Year: 2023
Subjects: visual discomfort
visual stress
interindividual differences
natural scenes
efficient coding
hypermetabolism
urban scenes
computational modelling

description: Much of the neural machinery of the early visual cortex, from the extraction of local orientations to contextual modulations through lateral interactions, is thought to have developed to provide a sparse encoding of contour in natural scenes, allowing the brain to process efficiently most of the visual scenes we are exposed to. Certain visual stimuli, however, cause visual stress, a set of adverse effects ranging from simple discomfort to migraine attacks, and epileptic seizures in the extreme, all phenomena linked with an excessive metabolic demand. The theory of efficient coding suggests a link between excessive metabolic demand and images that deviate from natural statistics. Yet, the mechanisms linking energy demand and image spatial content in discomfort remain elusive. Here, we used theories of visual coding that link image spatial structure and brain activation to characterize the response to images observers reported as uncomfortable. Our biologically based neurodynamic model of the early visual cortex included excitatory and inhibitory layers to implement contextual influences. We found three clear markers of aversive images: a larger overall activation in the model, a less sparse response, and a more unbalanced distribution of activity across spatial orientations. When the ratio of excitation over inhibition was increased in the model, a phenomenon hypothesised to underlie interindividual differences in susceptibility to visual discomfort, the three markers of discomfort progressively shifted towards values typical of the response to uncomfortable stimuli. Overall, these findings propose a unifying mechanistic explanation for why there are differences between images and between observers, suggesting how visual input and idiosyncratic hyperexcitability give rise to abnormal brain responses that result in visual stress.
description: The software required for the statistical analysis in Experiment 1 and 2 is R (Team, R. C. (2020). "R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.) The software required for running the simulations, extracting the markers of discomfort and generating the figures is Matlab (proprietary; MATLAB and Statistics Toolbox Release 2019b, 9.7.0.1190202 (R2019b). Natick, Massachusetts, The MathWorks Inc.). An open-source alternative to run the Matlab routines is Octave (https://octave.org/).
Funding provided by: Maria Zambrano Fellowship for attraction of international talent for the requalification of the Spanish university system—NextGeneration EU (ALRC)*Crossref Funder Registry ID: Award Number: 
Funding provided by: NIH COBRE*Crossref Funder Registry ID: Award Number: PG20GM103650
Funding provided by: NIH R15 AREA grant*Crossref Funder Registry ID: Award Number: MH122935
Funding provided by: Spanish Ministerio de Ciencia e Innovación, Gobierno de España*Crossref Funder Registry ID: Award Number: PID2020-118254RB-I00/AEI/10.13039/501100011033
Funding provided by: Agencia de Gestió d'Ajuts Univesitaris i de Recerca (AGAUR)*Crossref Funder Registry ID: Award Number: 2021-SGR-01470
Funding provided by: CERCA Programme / Generalitat de Catalunya*Crossref Funder Registry ID: Award Number:
description: Psychophysics: the ratings for discomfort were collected online using Qualtrics, in agreement with COVID-19 protocols. The protocol was approved by the Institutional Review Board at the University of Nevada, Reno (333057), and was conducted in accordance with the Declaration of Helsinki. 

Numerical simulation: the simulations of the neurodynamic model of the early visual system were done in Matlab. Please see README.md file for details on Experiment 1 and Experiment 2.