Individual Differences in the Experience of Cognitive Workload

This study investigated the roles of four psychosocial variables – anxiety, conscientiousness, emotional intelligence, and Protestant work ethic – on subjective ratings of cognitive workload as measured by the Task Load Index (TLX) and the further connections between the four variables and TLX ratings of task performance. The four variables represented aspects of an underlying construct of elasticity versus rigidity in response to workload. Participants were 141 undergraduates who performed a vigilance task under different speeded conditions while working on a jigsaw puzzle for 90 minutes. Regression analysis showed that anxiety and emotional intelligence were the two variables most proximally related to TLX ratings. TLX ratings contributed to the prediction of performance on the puzzle, but not the vigilance task. Severity error bias was evident in some of the ratings. Although working in pairs improved performance, it also resulted in higher ratings of temporal demand and perceived performance pressure

Open Science Perspectives on Machine Learning for the Identification of Careless Responding:A New Hope or Phantom Menace?

Powerful methods for identifying careless respondents in survey data are not just important to ensure the validity of subsequent data analyses, they are also instrumental for studying the psychological processes that drive humans to respond carelessly. Conversely, a deeper understanding of the phenomenon of careless responding enables the development of improved methods for the identification of careless respondents. While machine learning has gained substantial attention and popularity in many scientific fields, it is largely unexplored for the detection of careless responding. On the one hand, machine learning algorithms can be highly powerful tools due to their flexibility. On the other hand, science based on machine learning has been criticized in the literature for a lack of reproducibility. We assess the potential and the pitfalls of machine learning approaches for identifying careless respondents from an open science perspective. In particular, we discuss possible sources of reproducibility issues when applying machine learning in the context of careless responding, and we give practical guidelines on how to avoid them. Furthermore, we illustrate the high potential of an unsupervised machine learning method for the identification of careless respondents in a proof-of-concept simulation experiment. Finally, we stress the necessity of building an open data repository with labeled benchmark data sets, which would enable the evaluation of methods in a more realistic setting and make it possible to train supervised learning methods. Without such a data repository, the true potential of machine learning for the identification of careless responding may fail to be unlocked.</p

Open Science Perspectives on Machine Learning for the Identification of Careless Responding:A New Hope or Phantom Menace?

Powerful methods for identifying careless respondents in survey data are not just important to ensure the validity of subsequent data analyses, they are also instrumental for studying the psychological processes that drive humans to respond carelessly. Conversely, a deeper understanding of the phenomenon of careless responding enables the development of improved methods for the identification of careless respondents. While machine learning has gained substantial attention and popularity in many scientific fields, it is largely unexplored for the detection of careless responding. On the one hand, machine learning algorithms can be highly powerful tools due to their flexibility. On the other hand, science based on machine learning has been criticized in the literature for a lack of reproducibility. We assess the potential and the pitfalls of machine learning approaches for identifying careless respondents from an open science perspective. In particular, we discuss possible sources of reproducibility issues when applying machine learning in the context of careless responding, and we give practical guidelines on how to avoid them. Furthermore, we illustrate the high potential of an unsupervised machine learning method for the identification of careless respondents in a proof-of-concept simulation experiment. Finally, we stress the necessity of building an open data repository with labeled benchmark data sets, which would enable the evaluation of methods in a more realistic setting and make it possible to train supervised learning methods. Without such a data repository, the true potential of machine learning for the identification of careless responding may fail to be unlocked.</p

Buffering of Physiological and Affective Reactivity By a Single Proactive 5-minute Stress Management Technique

A plethora of recent research highlights the long-term chronic disease risks of elevated blood pressure (BP), heart rate (HR) and affective and cognitive responses to mental stressors and how traditional forms of mindfulness meditation (MM) and progressive muscle relaxation (PMR) may help offset these long-term risks. On top of that, briefer forms of MM (e.g., 3-day training sessions) have shown benefits for emotional and physical health. Further, perseverative cognitions, or the tendency to worry and rumination about stressful events, is linked with heightened CV reactivity, and may impede the success of stress management techniques. The purpose of this study was to investigate the feasibility of using a single, 5-minute session of mindfulness meditation to reduce physiological reactivity and acute psychological mood and stress responses to a stressor (compared to an alternative stress management technique and an active control condition). We conducted the following experimental protocol: 1) collected 10-minute baseline measures of BP and HR, as well as mood and perceived stress, 2) conducted a single 5-minute stress reduction technique (MM or PMR) or control, 3) ran a short version of the Trier Social Stress Test, 4) collect mood and perceived stress measures, and 5) conducted a 10-minute recovery period to allow participants’ BP and HR levels to return to baseline. We hypothesized that (1) those in the stress management groups would show less BP and HR reactivity during the stress induction, as well as increased positive affect and/or decreased negative affect and reduced perceived stress after the stress induction compared to the control group and (2) this benefit would be greater for those who score low (vs. high) on trait PCs. Statistical analyses included mixed design repeated-measures ANOVA to assess the relationships of intervention type (MM vs PMR vs control) and period (mean BP or HR scores at each time point) with repeats on the period variable. There were no significant findings for MM or PMR reducing reactivity, perceived stress, or negative mood (nor increases in positive mood) to the stressor

Buffering of Physiological and Affective Reactivity By a Single Proactive 5-Minute Stress Management Technique

A plethora of recent research highlights the long-term chronic disease risks of elevated blood pressure (BP), heart rate (HR) and affective and cognitive responses to mental stressors and how traditional forms of mindfulness meditation (MM) and progressive muscle relaxation (PMR) may help offset these long-term risks. On top of that, briefer forms of MM (e.g., 3-day training sessions) have shown benefits for emotional and physical health. Further, perseverative cognitions, or the tendency to worry and rumination about stressful events, is linked with heightened CV reactivity, and may impede the success of stress management techniques. The purpose of this study was to investigate the feasibility of using a single, 5-minute session of mindfulness meditation to reduce physiological reactivity and acute psychological mood and stress responses to a stressor (compared to an alternative stress management technique and an active control condition). We conducted the following experimental protocol: 1) collected 10-minute baseline measures of BP and HR, as well as mood and perceived stress, 2) conducted a single 5-minute stress reduction technique (MM or PMR) or control, 3) ran a short version of the Trier Social Stress Test, 4) collect mood and perceived stress measures, and 5) conducted a 10-minute recovery period to allow participants’ BP and HR levels to return to baseline. We hypothesized that (1) those in the stress management groups would show less BP and HR reactivity during the stress induction, as well as increased positive affect and/or decreased negative affect and reduced perceived stress after the stress induction compared to the control group and (2) this benefit would be greater for those who score low (vs. high) on trait PCs. Statistical analyses included mixed design repeated-measures ANOVA to assess the relationships of intervention type (MM vs PMR vs control) and period (mean BP or HR scores at each time point) with repeats on the period variable. There were no significant findings for MM or PMR reducing reactivity, perceived stress, or negative mood (nor increases in positive mood) to the stressor

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems

Effects of Work-Related Positive Affect on Stress Appraisals and Cardiovascular Stress Response

Introduction: Work-based affects have been implicated in employees’ health and well-being and have been identified as predictors of occupational stress and coping mechanisms. Occupational stress has been implicated in the genesis of cardiovascular disease, the number one killer in the US and other industrialized countries. Furthermore, arousal levels within affective experiences lead to differential activation of the central nervous system. Given the lack of research on the different levels of arousal of work-related positive affect (PA) within the context of stress and health correlates, the purpose of this study was to examine the associations between work-related high-arousal and low-arousal PA and cognitive appraisals and cardiovascular reactivity to induced psychological stress. It was hypothesized that: 1) Work-related PA (high and low arousal) will differentially but negatively predict threat appraisals during stress tasks, while challenge appraisals will be differentially but positively predicted; 2) Work-related high- arousal PA would be positively correlated with BP and CRV measures at baseline, while work-related low- arousal PA would be negatively correlated; 3) Work-related high-arousal PA will positively predict cardiovascular reactivity variables and work-related low arousal PA will negatively predict cardiovascular reactivity variables. Methods: The sample consisted of 70 (M= 19.74, SD=3.674) university undergraduate students. Baseline cardiovascular measures were collected including blood pressure and heart rate variability measures. Participants completed the Stress Appraisal Measure (SAM) during both segments of the Trier Social Stress Task (TSST). At the completion of the task, all participants completed the Job Related Affective Well Being Scale. Results: No associations were found between work-related PA (high and low arousal) and appraisals of threat and challenge during the speech and mental arithmetic (MA) tasks. No significant correlations were found between work-related PA (high and low arousal) with any of the cardiovascular variables during the baseline period. No significant associations were present between work-related PA and BP and all cardiovascular reactivity variables during the speech task. However, during the MA task, results showed that work-related low-arousal PA was associated with a decrease in DBP and the interaction term between high and low arousal PA and DBP was significant. The results also indicated that high-arousal PA was associated with a significant decrease in low frequency, whereas low-arousal PA was associated with a significant increase in low frequency. Low-arousal PA was also associated with an increase in LF /HF ratio, whereas high arousal PA was marginally associated with a decrease in LF/HF ratio. Discussion: In general, work-related high-arousal and low-arousal positive affect did not predict cognitive appraisals of stress. Low-arousal PA did predict decrease in DBP in response to stress. These results demonstrate that low-arousal work-related PA is important to investigate in relation to occupational stress and cardiovascular health. Unexpectedly, high-arousal work related PA negatively predicted LF (ms2), a measure often used as an indicator of sympathetic nervous system domination. [Keywords: Occupational stress, work-related positive affect, cardiovascular reactivity, heart rate variability reactivity, cognitive stress appraisals]http://deepblue.lib.umich.edu/bitstream/2027.42/133951/1/Elsiss - EFFECTS OF WORK-RELATED POSITIVE AFFECT ON STRESS APPRAISALS AND CARDIOVASCULAR STRESS RESPONSE.pdf13Description of Elsiss - EFFECTS OF WORK-RELATED POSITIVE AFFECT ON STRESS APPRAISALS AND CARDIOVASCULAR STRESS RESPONSE.pdf : Master's Thesi

A Study of Nonlinear Dynamics of EEG Responses to Simulated Unmanned Vehicle Tasks

In the contemporary world, mental workload becomes higher as technology evolves and task demand becomes overwhelming. The operators of a system are usually required to complete tasks with higher complicity within a shorter period of time. Continuous operation under a high level of mental workload can be a major source of risk and human error, thus put the operator in a hazardous working environment. Therefore, it is necessary to monitor and assess mental workload. In this study, an unmanned vehicle operation with visual detection tasks was investigated by means of nonlinear analysis of EEG time series. Nonlinear analysis is considered more advantageous compared with traditional power spectrum analysis of EEG. Besides, nonlinear analysis is more capable to capture the nature of EEG data and human performance, which is a process that subjects to constant changes. By examining the nonlinear dynamics of EEG, it is more likely to obtain a deeper understanding of brain activity. The objective of this study is to investigate the mental workload under different task levels through the examination of brain activity via nonlinear dynamics of EEG time series in simulated unmanned ground vehicle visual detection tasks. The experiment was conducted by the team lead by Dr. Lauren Reinerman Jones at Institute for Simulation & Training, University of Central Florida. One hundred and fifty subjects participated the experiment to complete four visual detection task scenarios (1) change detection, (2) threat detection task, (3) dual task with different change detection task rates, and (4) dual task with different threat detection task rates. Their EEG was recorded during performing the tasks at nine EEG channels. This study develops a massive data processing program to calculate the largest Lyapunov exponent, correlation dimension of the EEG data. This study also develops the program for performing 0-1 test on the EEG data in Python language environment. The result of this study verifies the existence of chaotic dynamics in EEG time series, reveals the change in brain activity as the effect of changing task demand in more detailed level, and obtains new insights from the psychophysiological mental workload measurement used in the preliminary study. The results of this study verified the existence of the chaotic dynamics in the EEG time series. This study also supported the hypothesis that EEG data exhibits change in the level of nonlinearity corresponding to differed task levels. The nonlinear analysis of EEG time series data is able to discriminate the change in brain activity derived from the changes in task load. All nonlinear dynamics analysis techniques used in this study is able to find the difference of nonlinearity in EEG among task levels, as well as between single task scenario and dual task scenario