Abstract
Previous research has shown an inverse relation between response times in elementary cognitive tasks and intelligence, but findings are inconsistent as to which is the most informative score. We conducted a study (N = 200) using a battery of elementary cognitive tasks, working memory capacity (WMC) paradigms, and a test of fluid intelligence (gf). Frequently used candidate scores and model parameters derived from the response time (RT) distribution were tested. Results confirmed a clear correlation of mean RT with WMC and to a lesser degree with gf. Highly comparable correlations were obtained for alternative location measures with or without extreme value treatment. Moderate correlations were found as well for scores of RT variability, but they were not as strong as for mean RT. Additionally, there was a trend towards higher correlations for slow RT bands, as compared to faster RT bands. Clearer evidence was obtained in an ex-Gaussian decomposition of the response times: the exponential component was selectively related to WMC and gf in easy tasks, while mean response time was additionally predictive in the most complex tasks. The diffusion model parsimoniously accounted for these effects in terms of individual differences in drift rate. Finally, correlations of model parameters as trait-like dispositions were investigated across different tasks, by correlating parameters of the diffusion and the ex-Gaussian model with conventional RT and accuracy scores.
Highlights
Intelligence researchers have investigated the correlation of mental speed with measures of intelligence since the mid-20th century [1]
Clearer evidence was obtained in an ex-Gaussian decomposition of the response times: the exponential component was selectively related to working memory capacity (WMC) and gf in easy tasks, while mean response time was predictive in the most complex tasks
response time (RT) variability scores were inversely correlated with ability, but their correlations were not as high as for mean RT
Summary
Intelligence researchers have investigated the correlation of mental speed with measures of intelligence since the mid-20th century [1]. Further prerequisites comprise their generality (at least across a set of tasks assigned to one processing domain) and validity with other parameters and criterion variables (e.g., shown in [38]); Second, with respect to relations across the diffusion model and the ex-Gaussian model, it has been consistently shown that correlations are only moderate [37,38,43] in spite of sufficiently high (parallel test) reliability [38] and temporal stability [42] This situation suggests that parameters of different models may, at most, tap different processes. Such correlations were expected to result if the cognitive measures require speedy processing as a confounding factor [10]
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