Computational protocol: From a Somatotopic to a Spatiotopic Frame of Reference for the Localization of Nociceptive Stimuli

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Protocol publication

[…] Analyses were performed on the PSS and JND values. PSS values that exceeded twice the maximum SOA were excluded from the data, together with their corresponding JND values. Extremely large PSS values indicate that participants were not able to perform the task correctly even at large SOA’s, where the task performance is expected to be nearly perfect. Therefore, the results in some conditions are missing for some participants. In order to test if this was influenced by the position of the hands, the difference in missing values between the uncrossed and the crossed posture condition was compared using a chi-squared test for equality of proportions.To address the question of whether there was an attentional bias (due to the capture of attention by the visual cues), we tested whether the PSS differed significantly from 0, using one-sample t-tests.Next, in order to compare the PSS across the different conditions, results were analyzed using the linear mixed effects models as implemented in the R package “Linear and Nonlinear Mixed Effects Models” []. Linear mixed effects models account for the correlations in within-subjects data by estimating subject-specific deviations (or random effects) from each population-level factor (or fixed factor) of interest (see [] for an elaboration). We chose to analyze the data with linear mixed models because it is a more subject-specific model and it allows unbalanced data, unlike the classical general linear models which requires a completely balanced array of data [].The primary outcome variable was the Point of Subjective Simultaneity (PSS). The independent variables were the Laterality (unilateral/bilateral), the Cue Distance (near/far) and the Posture (uncrossed/crossed). These were manipulated within subjects. Each analysis required three steps. First, all relevant factors and interactions were entered in the model as fixed factors, and we assessed whether it was necessary to add a random effect for each of the fixed factors in the analysis: If a random effect significantly increased the fit of the model, it was included in the final model. By default, a random effect was added introducing adjustments to the intercept conditional on the Subject variable. In the second step, we searched for the most parsimonious model that fitted the data. To achieve this, we systematically restricted the full model, comparing the goodness of fit using likelihood-ratio tests. Finally, in the third step, we inspected the ANOVA table of the final model and tested specific hypotheses about possible main effects or interactions (for a similar approach see [–]). Kenward-Roger approximations to the degrees of freedom were used to adjust for small sample sizes []. When an interaction effect was significant, it was further investigated with follow-up contrast analyses, corrected for multiple testing according to the Holm-Bonferroni corrections []. Standardized regression coefficients were reported as a measure of the effect size. The models are presented in the supporting information (Tables A-C in ).The same method was used to assess the influence of the different experimental conditions on the JND. The models are presented in the supporting information (Tables G-I in ). […]

Pipeline specifications

Software tools lme4, nlme
Application Mathematical modeling
Organisms Homo sapiens