The level of mental effort when performing everyday activities depends on many factors, and it is constrained by the working memory capacity. Factors such as the type of work, the work schedule,...Show moreThe level of mental effort when performing everyday activities depends on many factors, and it is constrained by the working memory capacity. Factors such as the type of work, the work schedule, and special events like exams, deadlines, or holidays may cause temporary increases or decreases to the cognitive load of daily routines. Given the dominance of circadian clocks in human behaviours and cognitive output, is mental effort also consistently influenced by the time of day? This study explored the possibility that cognitive load fluctuates in a consistent temporal pattern that is close to diurnal cycles observed in human behaviour and cognition. The approach was based on the assumption that smartphones tapping data can act as a proxy for cognitive load levels in order to capture the daily dynamic changes unobtrusively and in vivo. The sample data was collected from 64 healthy individuals, with a duration period on average of 33 days. A spectral analysis on the time-series of the tapping sessions revealed the existence of a diurnal cycle of 24 h in both low and high cognitive load measures, with no significant differences in the cycles’ amplitude. This suggests that people distribute their mental effort consistently across the day with similar intensity between activities that require low and high cognitive resources. This finding was again confirmed at the individual level by a cross correlation analysis. Interestingly, the participants exhibit barely visible weekly cycles with relatively weak signals, which could be a reflection of the time of the study during coronavirus lockdown; hence the influence of weekday/weekend rhythm may be diminished. Further research should examine the time of day when cognitive load peaks and declines, as well as the individual differences. The information can be valuable for various domains that are concerned with learning, performance and health.Show less
Smartphones have become an integral part of our daily lives. Along with this integration comes a concern for smartphone and internet addictions. In order to fully understand how these addictions...Show moreSmartphones have become an integral part of our daily lives. Along with this integration comes a concern for smartphone and internet addictions. In order to fully understand how these addictions might work, it is imperative that we develop more accurate measures of smartphone behaviours. Past studies have often included selfreported questionnaires that gathered data about the duration of smartphone use and other smartphone behaviours, but it is now known that self-reports are not entirely reliable. This study will investigate the difference between the self-reported duration of smartphone use and the actual duration provided by the users’ smartphones. Additionally, the study will explore the possibility of habitual checking behaviours as a predictor of recall error. Data collected from 122 participants were analysed via one-sample t-tests and multiple linear regressions. Results from the one-sample ttests support previous research, showing that participants were indeed inaccurate at recalling the time spent on their phones. Thus, smartphones are an important tool for providing objective data on smartphone behaviour. Contrary to former research, the level of smartphone usage did not make a difference on the amount of recall error, such that participants were inaccurate regardless of actual time spent on their smartphones. The multiple linear regressions found a relationship between one measure of time-based checking habits and recall error, but not between tap-based checking habits and recall error. These results indicate that unconscious, automatic smartphone habits may play a role in the inability to accurately recall smartphone behaviour.Show less
