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This paper introduces a novel procedure that can increase the signal-to-noise ratio in psychological experiments that use accuracy as a selection variable for another dependent variable. This procedure relies on the fact that some correct responses result from guesses and reclassifies them as incorrect responses using a trial-by-trial reclassification evidence such as response time. It selects the optimal reclassification evidence criterion beyond which correct responses should be reclassified as incorrect responses. We show that the more difficult the task and the fewer the response alternatives, the more to be gained from this reclassification procedure. We illustrate the procedure on behavioral and ERP data from two different datasets (Caplette et al. NeuroImage 218, 116994, 2020; Faghel-Soubeyrand et al. Journal of Experimental Psychology: General 148, 1834-1841, 2019) using response time as reclassification evidence. In both cases, the reclassification procedure increased signal-to-noise ratio by more than 13%. Matlab and Python implementations of the reclassification procedure are openly available ( https://github.com/GroupeLaboGosselin/Reclassification )., (© 2023. The Psychonomic Society, Inc.)

The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response's latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization. [ABSTRACT FROM AUTHOR]

In this article, we are interested in the computational modeling of visual attention. We report methods commonly used to assess the performance of these kinds of models. We survey the strengths and weaknesses of common assessment methods based on diachronic eye-tracking data. We then illustrate the use of some methods to benchmark computational models of visual attention. [ABSTRACT FROM AUTHOR]