Effects of gestational age at birth on cognitive performance : a function of cognitive workload demands

Objective: Cognitive deficits have been inconsistently described for late or moderately preterm children but are consistently found in very preterm children. This study investigates the association between cognitive workload demands of tasks and cognitive performance in relation to gestational age at birth. Methods: Data were collected as part of a prospective geographically defined whole-population study of neonatal at-risk children in Southern Bavaria. At 8;5 years, n = 1326 children (gestation range: 23–41 weeks) were assessed with the K-ABC and a Mathematics Test. Results: Cognitive scores of preterm children decreased as cognitive workload demands of tasks increased. The relationship between gestation and task workload was curvilinear and more pronounced the higher the cognitive workload: GA2 (quadratic term) on low cognitive workload: R2 = .02, p<0.001; moderate cognitive workload: R2 = .09, p<0.001; and high cognitive workload tasks: R2 = .14, p<0.001. Specifically, disproportionally lower scores were found for very (<32 weeks gestation) and moderately (32–33 weeks gestation) preterm children the higher the cognitive workload of the tasks. Early biological factors such as gestation and neonatal complications explained more of the variance in high (12.5%) compared with moderate (8.1%) and low cognitive workload tasks (1.7%). Conclusions: The cognitive workload model may help to explain variations of findings on the relationship of gestational age with cognitive performance in the literature. The findings have implications for routine cognitive follow-up, educational intervention, and basic research into neuro-plasticity and brain reorganization after preterm birth

Catastrophe Models for Cognitive Workload and Fatigue

We reconceptualised several problems concerning the measurement of cognitive workload – fixed versus variable limits on channel capacity, work volume versus time pressure, adaptive strategies, resources demanded by tasks when performed simultaneously, and unclear distinctions between workload and fatigue effects – as two cusp catastrophe models: buckling stress resulting from acute workload, and fatigue resulting from extended engagement. Experimental participants completed a task that was intensive on non-verbal episodic memory and had an automatically speeded component. For buckling stress, the epoch of maximum (speeded) performance was the asymmetry parameter; however, anxiety did not contribute to bifurcation as expected. For fatigue, the bifurcation factor was the total work accomplished, and arithmetic, a compensatory ability, was the asymmetry parameter; R2 for the cusp models outperformed the linear comparison models in both cases. A research programme is outlined that revolves around the two models with different types of task and resource configurations

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

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

Maritime cognitive workload assessment

The human factor plays the key role for safety in many industrial and civil every-day operations in our technologized world. Human failure is more likely to cause accidents than technical failure, e.g. in the challenging job of tugboat captains. Here, cognitive workload is crucial, as its excess is a main cause of dangerous situations and accidents while being highly participant and situation dependent. However, knowing the captain’s level of workload can help to improve man-machine interaction. The main contributions of this paper is a successful workload indication and a transfer of cognitive workload knowledge from laboratory to realistic settings

In-ear SpO2 for classification of cognitive workload

The brain is the most metabolically active organ in the body, which increases its metabolic activity, and thus oxygen consumption, with increasing cognitive demand. This motivates us to question whether increased cognitive workload may be measurable through changes in blood oxygen saturation. To this end, we explore the feasibility of cognitive workload tracking based on in-ear SpO2 measurements, which are known to be both robust and exhibit minimal delay. We consider cognitive workload assessment based on an N-back task with randomised order. It is shown that the 2-back and 3-back tasks (high cognitive workload) yield either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a measurable and statistically significant decrease in blood oxygen in response to increased cognitive workload. This makes it possible to classify the four N-back task categories, over 5 second epochs, with a mean accuracy of 90.6%, using features derived from in-ear pulse oximetry, including SpO2, pulse rate and respiration rate. These findings suggest that in-ear SpO2 measurements provide sufficient information for the reliable classification of cognitive workload over short time windows, which promises a new avenue for real time cognitive workload tracking

The Effects of Online Course Pedagogies on Learning and Cognitive Workload

The use of recorded lecture video is a common online practice, although the implementation of this pedagogy varies and may have differential impacts on learning depending on the extent to which it increases cognitive workload. This study compared two different online statistics lecture formats: interactive and non-interactive. The interactive lecture video stopped approximately every minute and the participant had to answer a question regarding the material before moving on to the next section of the video. The non-interactive lecture video was the exact same video edited to remove all interactive breaks in the video. Cognitive workload was measured using the NASA Task Load Index and learning was assessed using performance on a quiz. Seventy participants from the San Jose State research pool were included in the analysis. The researcher predicted that cognitive workload would be a mediating variable in the relationship between online pedagogy and learning, such that an interactive pedagogy would be associated with lower cognitive workload and result in larger learning gains compared to a non-interactive pedagogy. The researcher concluded that cognitive workload was not a mediating variable for online pedagogy and learning. Online pedagogy did not predict learning. However, the type of pedagogy had an effect on cognitive workload, such that the non-interactive group had lower cognitive workload scores. Developers of online pedagogy should recognize that cognitive workload differs across pedagogies and the impact on learning should remain the final assessment of any given approach

A USER’S COGNITIVE WORKLOAD PERSPECTIVE IN NEGOTIATION SUPPORT SYSTEMS: AN EYE-TRACKING EXPERIMENT

Replying to several research calls, I report promising results from an initial experiment which com-pares different negotiation support system approaches concerning their potential to reduce a user’s cognitive workload. Using a novel laboratory-based non-intrusive objective measurement technique which derives the user’s cognitive workload from pupillary responses and eye-movements, I experi-mentally evaluated a standard, a chat-based, and an argumentation-based negotiation support system and found that a higher assistance level of negotiation support systems actually leads to a lower user’s cognitive workload. In more detail, I found that an argumentation-based system which fully automates the generation of the user’s arguments significantly decreases the user’s cognitive workload compared to a standard system. In addition I found that a negotiation support system implementing an additional chat function significantly causes higher cognitive workload for users compared to a standard system

Cognitive workload measurement and modeling under divided attention

Motorists often engage in secondary tasks unrelated to driving that increase cognitive workload, resulting in fatal crashes and injuries. An International Standards Organization method for measuring a driver's cognitive workload, the detection response task (DRT), correlates well with driving outcomes, but investigation of its putative theoretical basis in terms of finite attention capacity remains limited. We address this knowledge gap using evidence-accumulation modeling of simple and choice versions of the DRT in a driving scenario. Our experiments demonstrate how dual-task load affects the parameters of evidence-accumulation models. We found that the cognitive workload induced by a secondary task (counting backward by 3s) reduced the rate of evidence accumulation, consistent with rates being sensitive to limited-capacity attention. We also found a compensatory increase in the amount of evidence required for a response and a small speeding in the time for nondecision processes. The International Standards Organization version of the DRT was found to be most sensitive to cognitive workload. A Wald-distributed evidence-accumulation model augmented with a parameter measuring response omissions provided a parsimonious measure of the underlying causes of cognitive workload in this task. This work demonstrates that evidence-accumulation modeling can accurately represent data produced by cognitive workload measurements, reproduce the data through simulation, and provide supporting evidence for the cognitive processes underlying cognitive workload. Our results provide converging evidence that the DRT method is sensitive to dynamic fluctuations in limited-capacity attention

WHAT TASKS TO AUTOMATE? AN INVESTIGATION OF WHAT TASKS MAKE SENSE TO AUTOMATE FOR FUTURE AVIATION PLATFORMS

The Army is developing a new generation of aircraft called Future Vertical Lift (FVL). These aircraft will integrate new technologies that change Army Aviation’s machinery, methods, and aircrew domains. Key to this effort is the development of automation to reduce pilot cognitive workload and prevent cognitive overload. The purpose of this research was to develop an understanding of the factors that influence pilot cognitive workload and to provide insight into what tasks make sense to automate for FVL. Researchers used a mixed methods approach, relying on scholarly literature and semi-structured interviews to elicit cognitive workload data from Army rotary-wing pilots. Researchers used the data from a simple and a complex MEDEVAC flight scenario to develop an influence diagram that models pilot cognitive workload based on influencing factors and subfactors. At a high level, the data indicate that pilot task demand and environmental factors have the most influence on cognitive workload during complex missions in challenging conditions. At a low level, the data indicate that light factors, intra-flight coordination, and task complexity are most influential on cognitive workload. The results suggest that tasks impacting these factors should be considered for automation to prevent pilot cognitive overload in FVL.DEVCOM AvMCCaptain, United States ArmyMajor, United States ArmyCaptain, United States ArmyMajor, United States ArmyApproved for public release. Distribution is unlimited