Using physiologically validated questionnaires in which the peak of circadian arousal is determined through morningness-eveningness preferences, individuals can be categorized into morning or evening chronotypes. Typically, individuals with such chronotypes are assumed to show better cognitive performance at their subjective peak of circadian arousal than at off peak. Although this so-called synchrony effect is accepted as common knowledge, empirical evidence is rather mixed. This may be explained by two methodical challenges. First, most studies are underpowered. Second, they include one task, but tasks differ across studies. Here, we tested the synchrony effect by focusing on two cognitive constructs that are assumed to underlie a wide variety of behaviors, that is: short-term maintenance and attentional control. Short-term maintenance refers to our ability to maintain information temporarily. Attentional control refers to our ability to avoid being distracted by irrelevant information. We addressed the methodical challenges by asking 446 young adults to perform eight tasks at on- and off-peak times. Four tasks were used to assess temporary maintenance of information (i.e., short-term memory). Four tasks were used to assess temporary maintenance and manipulation of information (i.e., working memory). Using structural equation modeling, we modeled attentional control as the goal-directed nature of the working-memory tasks without their maintenance aspects. At the individual-task level, there was some evidence for a synchrony effect. However, the evidence was weak and limited to two tasks. Moreover, at the latent-variable level, the results showed no evidence for a robust and general synchrony effect. These results were observed for the full sample (N = 446) and the subsample including participants with moderate to definite morning or evening chronotypes (N = 191). We conclude that the synchrony effect is most likely a methodical artefact.
In this paper, an R package was used to improve the reproducibility of the analyses. Therefore, it would be good to know to what extent this works. The R package includes the following analyses: (1) data trimming and preparation, (2) descriptive statistics, (3) reliability and correlations, (4) t-tests and Bayesian t-tests, (5) latent-change models (structural equation modeling approach), and (6) multiverse analyses. Furthermore, all deidentified data, experiment codes, research materials, and results are publicly accessible on the Open Science Framework (OSF) at https://osf.io/ngfxv. The study’s design and the analyses were pre-registered on OSF. The preregistration can be accessed at https://osf.io/ tywu7.
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