Educational and Cognitive Predictors of Pro- and Antisaccadic Performance

Abstract

Voluntary gaze control allows people to direct their attention toward selected targets while avoiding distractors. Failure in this ability could be related to dysfunctions in the neural circuits underlying executive functions. Interestingly, recent evidence suggests that factors such as years of schooling and literacy may positively influence goaldirected behavior and inhibitory control. However, we do not yet know whether these factors also have a significant impact on the inhibitory control of oculomotor responses. Using pro-and antisaccadic tasks to assess the behavioral responses of healthy adults, we tested the contribution of years of schooling and reading proficiency to their oculomotor control, while simultaneously analyzing the effects of other individual characteristics related to demographic, cognitive and motor profiles. This approach allowed us to test the hypothesis that schooling factors are closely related to oculomotor performance. Indeed, a regression analysis revealed important contributions of reading speed and intellectual functioning to the choices on both pro-and antisaccadic tasks, while years of schooling, age and block sequence emerged as important predictors of the kinematic properties of eye movements on antisaccadic tasks. Thus, our findings show that years of schooling and reading speed had a strong predictive influence on the oculomotor measures, although age and order of presentation also influenced saccadic performance, as previously reported. Unexpectedly, we found that an indirect measure of intellectual ability also proved to be a good predictor of the control of saccadic movements. The methods and findings of this study will be useful for identifying and breaking down the cognitive and educational components involved in assessing voluntary and automatic responses.

Clustering of neuropsychological traits of preschoolers

“…We explored whether domestic and educational environments of the children could predict their classification cluster. We used a multivariable linear regression model (MLRM) to quantify the regression coefficients for these predictors, as described before 65 .…”

Isomorphic decisional biases across perceptual tasks

“…We compared the probability distributions using Kolmogorov-Smirnov (KS) tests. To determine the MLRM analyses' significance, we compared the actual regression coefficients against 1000 surrogate data sets generated by shuffling the predictors [ 33 ]. With this approach, we established the empirical significance of the observed coefficients by comparing them against coefficients obtained with randomly permuted predictors (surrogates) to test the null hypothesis that the regression coefficients might have been generated by chance.…”

An Automated Water Task to Test Visual Discrimination Performance, Adaptive Strategies and Stereotyped Choices in Freely Moving Mice

“…All comparisons were followed by Bonferroni's or Wilcoxon Signed Rank post hoc tests. In Figure 4D , we tested the null hypothesis by creating 1,000 surrogates of the original data set, as previously described (Chamorro et al, 2017 ). We illustrate all our results as averages ± S.E.M.…”