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Fixational eye movements predict visual sensitivity.


ABSTRACT: During steady fixation, observers make small fixational saccades at a rate of around 1-2 per second. Presentation of a visual stimulus triggers a biphasic modulation in fixational saccade rate-an initial inhibition followed by a period of elevated rate and a subsequent return to baseline. Here we show that, during passive viewing, this rate signature is highly sensitive to small changes in stimulus contrast. By training a linear support vector machine to classify trials in which a stimulus is either present or absent, we directly compared the contrast sensitivity of fixational eye movements with individuals' psychophysical judgements. Classification accuracy closely matched psychophysical performance, and predicted individuals' threshold estimates with less bias and overall error than those obtained using specific features of the signature. Performance of the classifier was robust to changes in the training set (novel subjects and/or contrasts) and good prediction accuracy was obtained with a practicable number of trials. Our results indicate a tight coupling between the sensitivity of visual perceptual judgements and fixational eye control mechanisms. This raises the possibility that fixational saccades could provide a novel and objective means of estimating visual contrast sensitivity without the need for observers to make any explicit judgement.

SUBMITTER: Scholes C 

PROVIDER: S-EPMC4633872 | biostudies-other | 2015 Oct

REPOSITORIES: biostudies-other

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Fixational eye movements predict visual sensitivity.

Scholes Chris C   McGraw Paul V PV   Nyström Marcus M   Roach Neil W NW  

Proceedings. Biological sciences 20151001 1817


During steady fixation, observers make small fixational saccades at a rate of around 1-2 per second. Presentation of a visual stimulus triggers a biphasic modulation in fixational saccade rate-an initial inhibition followed by a period of elevated rate and a subsequent return to baseline. Here we show that, during passive viewing, this rate signature is highly sensitive to small changes in stimulus contrast. By training a linear support vector machine to classify trials in which a stimulus is ei  ...[more]

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