Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST

Attention, Perception & Psychophysics
Mike D RinderknechtRoger Gassert

Abstract

Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the beha...Continue Reading

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Citations

Jun 9, 2018·Journal of Neuroengineering and Rehabilitation·Mike D RinderknechtRoger Gassert
Jan 11, 2020·Frontiers in Psychology·Giulio VidottoEgidio Robusto

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