Abstract
People with higher IQ scores also tend to perform better on elementary cognitive-perceptual tasks, such as deciding quickly whether an arrow points to the left or the right Jensen (2006). The worst performance rule (WPR) finesses this relation by stating that the association between IQ and elementary-task performance is most pronounced when this performance is summarized by people’s slowest responses. Previous research has shown that the WPR can be accounted for in the Ratcliff diffusion model by assuming that the same ability parameter—drift rate—mediates performance in both elementary tasks and higher-level cognitive tasks. Here we aim to test four qualitative predictions concerning the WPR and its diffusion model explanation in terms of drift rate. In the first stage, the diffusion model was fit to data from 916 participants completing a perceptual two-choice task; crucially, the fitting happened after randomly shuffling the key variable, i.e., each participant’s score on a working memory capacity test. In the second stage, after all modeling decisions were made, the key variable was unshuffled and the adequacy of the predictions was evaluated by means of confirmatory Bayesian hypothesis tests. By temporarily withholding the mapping of the key predictor, we retain flexibility for proper modeling of the data (e.g., outlier exclusion) while preventing biases from unduly influencing the results. Our results provide evidence against the WPR and suggest that it may be less robust and less ubiquitous than is commonly believed.
Highlights
Over the past decades, the field of mental chronometry has revealed several robust associations between high-level cognitive ability (e.g., IQ, working memory) and response times (RT) in elementary cognitive-perceptual tasks (Jensen, 2006; Van Ravenzwaaij et al, 2011)
The second explanation is that the drift rate is a more specific measure of general processing speed than response times
To test whether individual differences in response caution have confounded the worst performance rule on the raw RT data, we explore how analyses 1a, 1b, and 2 turn out when we perform them on ten subgroups with similar response caution
Summary
The field of mental chronometry has revealed several robust associations between high-level cognitive ability (e.g., IQ, working memory) and response times (RT) in elementary cognitive-perceptual tasks (Jensen, 2006; Van Ravenzwaaij et al, 2011). The main finding is that people with relatively high IQ-scores tend to respond relatively quickly in simple RT tasks that do not appear to involve deep cognitive processing; one example of such a task is the random dot kinematogram, which requires participants to detect the direction of apparent motion in a cloud of dot stimuli Another important finding is known as the worst performance rule (WPR): the fact that the worst performance in these simple tasks—that is, the slowest responses—is most indicative of high-level cognitive ability (Baumeister and Kellas, 1968; Larson & Alderton, 1990). A novel element to our preregistration proposal is the inclusion of a blinding procedure, where an analyst (in this case, author JV) is sent the data with the key variable shuffled (MacCoun & Perlmutter, 2015) This way, the analyst is free to (1) resolve ambiguities and oversights in the preregistration document; and (2) adjust the analysis to unexpected peculiarities of the data. After the analyst had committed to the analysis plan was the key variable unshuffled
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