When estimating the influence of sentence complexity on reading, researchers typically opt for one of two main approaches: Measuring syntactic complexity (SC) or transitional probability (TP). Comparisons of the predictive power of both approaches have yielded mixed results. To address this inconsistency, we conducted a self-paced reading experiment. Participants read sentences of varying syntactic complexity. From two alternatives, we selected the set of SC and TP measures, respectively, that provided the best fit to the self-paced reading data. We then compared the contributions of the SC and TP measures to self-paced reading times when entered into the same model. Our results showed that while both measures explained significant portions of variance in reading times (over and above control variables: word/sentence length, word frequency and word position) when included in independent models, their contributions changed drastically when SC and TP were entered into the same model. Specifically, we only observed significant effects of TP. We conclude that in our experiment the control variables explained the bulk of variance. When comparing the small effects of SC and TP, the effects of TP appear to be more robust.

Emerging efforts toward prevention of stress-related mental disorders have created a need for unobtrusive real-life monitoring of stress-related symptoms. We used ecological momentary assessments (EMA) combined with wearable biosensors to investigate whether these can be used to detect periods of prolonged stress. During stressful high-stake exam (versus control) weeks, participants reported increased negative affect and decreased positive affect. Intriguingly, physiological arousal was decreased on average during the exam week. Time-resolved analyses revealed peaks in physiological arousal associated with both self-reported stress and self-reported positive affect, while the overall decrease in physiological arousal was mediated by lower positive affect during the stress period. We then used machine learning to show that a combination of EMA and physiology yields optimal classification of week types. Our findings highlight the potential of wearable biosensors in stress-related mental-health monitoring, but critically show that psychological context is essential for interpreting physiological arousal detected using these devices.

When estimating the influence of sentence complexity on reading, researchers typically opt for one of two main approaches: Measuring syntactic complexity (SC) or transitional probability (TP). Comparisons of the predictive power of both approaches have yielded mixed results. To address this inconsistency, we conducted a self-paced reading experiment. Participants read sentences of varying syntactic complexity. From two alternatives, we selected the set of SC and TP measures, respectively, that provided the best fit to the self-paced reading data. We then compared the contributions of the SC and TP measures to reading times when entered into the same model. Our results showed that both measures explained significant portions of variance in self-paced reading times. Thus, researchers aiming to measure sentence complexity should take both SC and TP into account. All of the analyses were conducted with and without control variables known to influence reading times (word/sentence length, word frequency and word position) to showcase how the effects of SC and TP change in the presence of the control variables.