Cognitive fatigue is a psychological state characterised by feelings of tiredness and impaired cognitive functioning arising from high cognitive demands. This paper examines the recent research progress on the assessment of cognitive fatigue and provides informed recommendations for future research. Traditionally, cognitive fatigue is introspectively assessed through self-report or objectively inferred from a decline in behavioural performance. However, more recently, researchers have attempted to explore the biological underpinnings of cognitive fatigue to understand and measure this phenomenon. In particular, there is evidence indicating that the imbalance between sympathetic and parasympathetic nervous activity appears to be a physiological correlate of cognitive fatigue. This imbalance has been indexed through various heart rate variability indices that have also been proposed as putative biomarkers of cognitive fatigue. Moreover, in contrast to traditional inferential methods, there is also a growing research interest in using data-driven approaches to assessing cognitive fatigue. The ubiquity of wearables with the capability to collect large amounts of physiological data appears to be a major facilitator in the growth of data-driven research in this area. Preliminary findings indicate that such large datasets can be used to accurately predict cognitive fatigue through various machine learning approaches. Overall, the potential of combining domain-specific knowledge gained from biomarker research with machine learning approaches should be further explored to build more robust predictive models of cognitive fatigue.

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ObjectivesWe examined the combined effects of behavioural inhibition and behavioural activation, on one hand, and locus of control, on the other hand, on different categories of smoking behaviour (non-smoking, ex-smoking, occasional smoking, daily smoking).DesignThis study adopted a cross-sectional design. Participants completed questionnaires regarding demographics, smoking patterns, behavioural inhibition/behavioural activation systems and locus of control.SettingThe study was conducted across four companies from the transportation, cooling plant and education sectors in Singapore.ParticipantsThree hundred sixty-nine male working adults were included in the final sample.ResultsCorroborating previous research, a logistic regression model examining behavioural inhibition/behavioural activation systems revealed that the fun-seeking aspect of behavioural activation was a unique predictor in distinguishing non-smokers from daily smokers (OR=1.24, p=0.012). By contrast, in a separate model examining locus of control, external locus of control was found to be a unique predictor in distinguishing non-smokers from daily smokers (OR=1.13, p<0.001). In addition, a third model combining both behavioural inhibition/behavioural activation systems and locus of control found that only external locus of control remained a significant predictor (OR=1.12, p<0.001). Further analyses revealed a mediating effect of external locus of control on the relationship between fun-seeking and smoking behaviour. That is, the increase in the odds of daily smoking due to fun-seeking was explained by external locus of control (direct pathway OR=1.20, p=0.058; indirect pathway OR=1.04, p<0.050).ConclusionsOverall, fun-seeking through its influence on external locus of control indirectly affects daily smoking behaviour, suggesting a more complex relationship than shown in previous research.