Method for Improving Psychophysical Threshold Estimates by Detecting Sustained Inattention

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 w...

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Veröffentlicht in:bioRxiv
Hauptverfasser: Rinderknecht, Mike Domenik, Ranzani, Raffaele, Popp, Werner Louis, Lambercy, Olivier, Gassert, Roger
Format: Paper
Sprache:Englisch
Veröffentlicht: Cold Spring Harbor Cold Spring Harbor Laboratory Press 03.03.2018
Cold Spring Harbor Laboratory
Ausgabe:1.1
Schlagworte:
Algorithms
Attention
Clinical trials
Experiments
Mathematical models
Neuroscience
Parameter estimation
Perception
Proprioception
Psychophysics
Sampling
adaptive algorithm
proprioception assessment
parameter estimation by sequential testing (PEST)
bias correction
psychophysics
ISSN:2692-8205, 2692-8205
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Zusammenfassung: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., <300). We present a method to detect inattention periods on a trial-by-trial basis to improve 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 behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The algorithm complements existing approaches and can be adapted to sampling procedures other than PEST. Besides being applicable post-hoc, it could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2692-8205
2692-8205
DOI:10.1101/275594