Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks

Beyond linear regression: A reference for analyzing common data types in discipline based education research

[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling—generalized linear models (glms)—and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher’s identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work

DEGREE PREDICTION USING LOGISTIC REGRESSION

To evaluate the efficiency of the previous years or to set visible plan in different aspects for the upcoming years in higher institutions studying students’ time to degree is important. Since logistic regression is a method used to predict a dependent categorical outcome or predict the probability of an event occurrence, studying Students’ time to degree using logistic regression is a reasonable way to predict the probability of students’ time to graduate considering influential factors that magnify and make a difference between different types of students. This difference can be the difference between age, gender, study programmes and so on. Thus, this study explores the prediction of degree at University of Lund Engineering faculty students on time and in the consecutive semesters based on significant factors

Logistic mixed models to investigate implicit and explicit belief tracking

We investigated the proposition of a two-systems Theory of Mind in adults’ belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking

Cognitively diagnostic analysis using the G-DINA model in R

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discusse

Bivariate random effects models for meta-analysis of comparative studies with binary outcomes: Methods for the absolute risk difference and relative risk

Multivariate meta-analysis is increasingly utilized in biomedical research to combine data of multiple comparative clinical studies for evaluating drug efficacy and safety profile. When the probability of the event of interest is rare or when the individual study sample sizes are small, a substantial proportion of studies may not have any event of interest. Conventional meta-analysis methods either exclude such studies or include them through ad-hoc continuality correction by adding an arbitrary positive value to each cell of the corresponding 2 by 2 tables, which may result in less accurate conclusions. Furthermore, different continuity corrections may result in inconsistent conclusions. In this article, we discuss a bivariate Beta-binomial model derived from Sarmanov family of bivariate distributions and a bivariate generalized linear mixed effects model for binary clustered data to make valid inferences. These bivariate random effects models use all available data without ad hoc continuity corrections, and accounts for the potential correlation between treatment (or exposure) and control groups within studies naturally. We then utilize the bivariate random effects models to reanalyze two recent meta-analysis data sets

Trauma and psychosis: synthesising evidence, network modelling and expanding into an interventionist-causal paradigm to investigate mediating mechanisms

The link between childhood trauma and the development of psychosis in adulthood is already well established, but factors which explain this relationship are currently less well understood. This thesis firstly aims to review the current state of evidence, then contribute novel empirical findings to help expand the understanding of how early traumatic experience leads to the development of psychosis. Information about causal factors is essential to the future development of effective therapeutic interventions for psychosis. Firstly an extensive systematic review of studies which examines potential mediating mechanisms between trauma and psychosis is undertaken. Data from 37 studies were used to analyse 232 mediation models, taking into account the magnitude and significance of effects, along with study quality. Judgements are offered on the strongest areas of evidence, and implications for future research are discussed. The first empirical study uses network analysis to generate a data-driven model of trauma, sub-clinical psychotic experiences and other relevant factors using data gathered from an online survey in a general population sample. Exploratory analyses were undertaken to derive a hypothetical model, which was then analysed statistically using structural equation modelling. The model hypothesis was pre-registered then prospectively tested in a second sample of data. Results and implications are discussed in the context of psychological models of psychosis. The second empirical study uses an interventionist-causal paradigm to conduct a randomised controlled trial in a clinical psychosis population with experience of paranoia. An emotion regulation skills intervention was tested against an active control condition, with participants providing pre- and post- data, along with experience sampling data collected using mobile phones for analysis of individual and group change. Although limited by small sample size, findings are discussed in terms of acceptability, feasibility and implications for research and practice