Support for the efficient coding account of visual discomfort

O'Hare, L ORCID: https://orcid.org/0000-0003-0331-3646 and Hibbard, PB, 2024. Support for the efficient coding account of visual discomfort. Visual Neuroscience. ISSN 0952-5238 (Forthcoming)

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Abstract

Sparse coding theories suggest that the visual brain is optimised to encode natural visual stimuli to minimise metabolic cost. It is thought that images that do not have the same statistical properties of natural images are unable to be coded efficiently and result in visual discomfort. Conversely, artworks are thought to be even more efficiently processed compared to natural images and so are aesthetically pleasing. This project investigated visual discomfort in uncomfortable images, natural scenes and artworks using a combination of low-level image statistical analysis, mathematical modelling and EEG measures. Results showed that the model response predicted discomfort judgements. Moreover, low-level image statistics including edge predictability predict discomfort judgements, whereas contrast information predicts the SSVEP responses. In conclusion, this study demonstrates that discomfort judgements for a wide set of images can be influenced by contrast and edge information, and can be predicted by our models of low-level vision, whilst neural responses are more defined by contrast-based metrics, when contrast is allowed to vary.

Item Type:	Journal article
Publication Title:	Visual Neuroscience
Creators:	O'Hare, L. and Hibbard, P.B.
Publisher:	Cambridge University Press (CUP)
Date:	30 September 2024
ISSN:	0952-5238
Identifiers:	Number Type 2233736 Other
Rights:	Accepted for publication in Visual Neuroscience
Divisions:	Schools > School of Social Sciences
Record created by:	Laura Ward
Date Added:	01 Oct 2024 09:10
Last Modified:	01 Oct 2024 09:10
URI:	https://irep.ntu.ac.uk/id/eprint/52332

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