Is the d2 Test of Attention Rasch Scalable? Analysis With the Rasch Poisson Counts Model

Real-time estimation of EEG-based engagement in different tasks

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Impact of a 36-hour Nonstop Training Course on Academic, Physical, and Mental Skills as well as Psychological Stress Perception in Cardiac Surgery Residents

BACKGROUND

Making the right decision in stressful situations is required for goal-oriented action in cardiac surgery. Current labor laws prevent residents to be subjected to situations that test their stress tolerance. These situations often occur only later in the career. We simulated such conditions in a structured non-stop 36-hour cardiac surgical training course and assessed the participant's performance.

METHODS

Fourteen advanced residents/junior staff surgeons were selected. The course was conducted in collaboration with the national antiterror police forces that provided coaching for teamplay, leadership, and responsibility awareness. The candidates attended graded and evaluated workshops/lectures and performed academic and surgical tasks. Psychological and surgical skill assessments were conducted at times 0, 12, 24, 36 hours.

RESULTS

Progressive reductions in individual motivation, associated with increased stress and irritability levels, worsening mood, and fatigue were observed. Long- and short-term memory functions were unaffected and practical surgical performance even increased over time.

CONCLUSION

Among the candidates, 36 hours of sleep deprivation did not lead to relevant changes in the skills required from a cardiac surgeon in daily life. Importantly, group dynamics substantially improved during the course, suggesting advances in the perception of responsibility and teamwork.

A flexible approach to modelling over-, under- and equidispersed count data in IRT: The Two-Parameter Conway-Maxwell-Poisson Model

Several psychometric tests and self-reports generate count data (e.g., divergent thinking tasks). The most prominent count data item response theory model, the Rasch Poisson Counts Model (RPCM), is limited in applicability by two restrictive assumptions: equal item discriminations and equidispersion (conditional mean equal to conditional variance). Violations of these assumptions lead to impaired reliability and standard error estimates. Previous work generalized the RPCM but maintained some limitations. The two-parameter Poisson counts model allows for varying discriminations but retains the equidispersion assumption. The Conway-Maxwell-Poisson Counts Model allows for modelling over- and underdispersion (conditional mean less than and greater than conditional variance, respectively) but still assumes constant discriminations. The present work introduces the Two-Parameter Conway-Maxwell-Poisson (2PCMP) model which generalizes these three models to allow for varying discriminations and dispersions within one model, helping to better accommodate data from count data tests and self-reports. A marginal maximum likelihood method based on the EM algorithm is derived. An implementation of the 2PCMP model in R and C++ is provided. Two simulation studies examine the model's statistical properties and compare the 2PCMP model to established models. Data from divergent thinking tasks are reanalysed with the 2PCMP model to illustrate the model's flexibility and ability to test assumptions of special cases.

Optimum display luminance under a wide range of ambient light for cockpit displays

The self-luminous cockpit displays need to be adaptive to a wide range of ambient light levels, which changes from very low illuminance to very high levels. Yet, current studies on evaluation and luminance setting of displays in bright surroundings are still limited. In this study, a three-dimensional visual ergonomic experiment was carried out to investigate how bright a cockpit display should be to meet aircrew operational requirements under different illuminance. A lab study with a within-subjects (N = 12) design was conducted in a simulated cockpit. According to the Weber-Fechner's Law, human observers evaluated five display luminance conditions (10¹, 10^1.5, 10², 10^2.5, 10³ cd/m²) under five ambient illuminance conditions (10°, 10¹, 10², 10³, 10⁴ lx). Visual performance, visual fatigue and visual comfort were used as evaluation bases, which were measured by d2 task, subjective fatigue questionnaire and visual perception semantic scales. Nonlinear function fitting was used to calculate the optimal luminance under a certain illuminance. Finally, curvilinear regression was used to analyze the illuminance and its corresponding optimal luminance. Based on Silverstein luminance power function, a luminance adjustment model with the form of power function was obtained. The proposed three-dimensional model fits the experimental data well and is consistent with the existing studies. It can be regarded as a supplement and optimization of the previous model under high ambient illuminance. This study can contribute not only to the pleasing luminance setting of panel displays in aircraft cockpits but also to other self-luminous devices, such as tablet devices, outdoor monitoring equipment and advertising screens.

Restoration of Attention by Rest in a Multitasking World: Theory, Methodology, and Empirical Evidence

In this work, we evaluate the status of both theory and empirical evidence in the field of experimental rest-break research based on a framework that combines mental-chronometry and psychometric-measurement theory. To this end, we (1) provide a taxonomy of rest breaks according to which empirical studies can be classified (e.g., by differentiating between long, short, and micro-rest breaks based on context and temporal properties). Then, we (2) evaluate the theorizing in both the basic and applied fields of research and explain how popular concepts (e.g., ego depletion model, opportunity cost theory, attention restoration theory, action readiness, etc.) relate to each other in contemporary theoretical debates. Here, we highlight differences between all these models in the light of two symbolic categories, termed the resource-based and satiation-based model, including aspects related to the dynamics and the control (strategic or non-strategic) mechanisms at work. Based on a critical assessment of existing methodological and theoretical approaches, we finally (3) provide a set of guidelines for both theory building and future empirical approaches to the experimental study of rest breaks. We conclude that a psychometrically advanced and theoretically focused research of rest and recovery has the potential to finally provide a sound scientific basis to eventually mitigate the adverse effects of ever increasing task demands on performance and well-being in a multitasking world at work and leisure.

Reliability of researcher capacity estimates and count data dispersion: a comparison of Poisson, negative binomial, and Conway-Maxwell-Poisson models

Item-response models from the psychometric literature have been proposed for the estimation of researcher capacity. Canonical items that can be incorporated in such models to reflect researcher performance are count data (e.g., number of publications, number of citations). Count data can be modeled by Rasch’s Poisson counts model that assumes equidispersion (i.e., mean and variance must coincide). However, the mean can be larger as compared to the variance (i.e., underdispersion), or b) smaller as compared to the variance (i.e., overdispersion). Ignoring the presence of overdispersion (underdispersion) can cause standard errors to be liberal (conservative), when the Poisson model is used. Indeed, number of publications or number of citations are known to display overdispersion. Underdispersion, however, is far less acknowledged in the literature. In the current investigation the flexible Conway-Maxwell-Poisson count model is used to examine reliability estimates of capacity in relation to various dispersion patterns. It is shown, that reliability of capacity estimates of inventors drops from .84 (Poisson) to .68 (Conway-Maxwell-Poisson) or .69 (negative binomial). Moreover, with some items displaying overdispersion and some items displaying underdispersion, the dispersion pattern in a reanalysis of Mutz and Daniel’s (2018b) researcher data was found to be more complex as compared to previous results. To conclude, a careful examination of competing models including the Conway-Maxwell-Poisson count model should be undertaken prior to any evaluation and interpretation of capacity reliability. Moreover, this work shows that count data psychometric models are well suited for decisions with a focus on top researchers, because conditional reliability estimates (i.e., reliability depending on the level of capacity) were highest for the best researchers.

D-optimal design for the Rasch counts model with multiple binary predictors

In this paper we derive optimal designs for the Rasch Poisson counts model and its extended version of the (generalized) negative binomial counts model incorporating several binary predictors for the difficulty parameter. To efficiently estimate the regression coefficients of the predictors, locally D-optimal designs are developed. After an introduction to the Rasch Poisson counts model and its extension, we will specify these models as particular generalized linear models. Based on this embedding, optimal designs for both models including several binary explanatory variables will be presented. Therefore, we will derive conditions on the effect sizes for certain designs to be locally D-optimal. Finally, it is pointed out that the results derived for the Rasch Poisson models can be applied for more general Poisson regression models which should receive more attention in future psychological research.