With the advancement of technologies like in-car navigation and smartphones, concerns around how cognitive functioning is influenced by "workload" are increasingly prevalent.Research shows that spreading effort across multiple tasks can impair cognitive abilities through an overuse of resources, and that similar overload effects arise in difficult single-task paradigms. We developed a novel lab-based extension of the Detection Response Task, which measures workload, and paired it with a Multiple Object Tracking Task to manipulate cognitive load. Load was manipulated either by changing within-task difficulty or by the addition of an extra task. Using quantitative cognitive modelling we showed that these manipulations cause similar cognitive impairments through diminished processing rates, but that the introduction of a second task tends to invoke more cautious response strategies that do not occur when only difficulty changes. We conclude that more prudence should be exercised when directly comparing multitasking and difficulty-based workload impairments, particularly when relying on measures of central tendency.

The accurate and objective measurement of cognitive workload is important in many aspects of psychological research. The Detection Response Task (DRT) is a well-validated method for measuring cognitive workload that has been used extensively in applied tasks, for example to investigate the effects of fatigue and phone usage on driving. Given its success in applied tasks, we investigated whether the DRT could be used to measure cognitive workload in cognitive tasks more commonly used in experimental cognitive psychology, and whether this application could be extended to online environments. We had participants perform a multiple object tracking task while simultaneously performing a DRT. We manipulated the cognitive load of the multiple object tracking task by changing the number of dots to be tracked. Measurements from the DRT were sensitive to changes in the cognitive load, establishing the efficacy of the DRT for experimental cognitive tasks in lab-based situations. This sensitivity continued when applied to an online environment (with our code for the online DRT implementation being freely available at \url{https://osf.io/dc39s/}), though to a reduced extent compared to the in-lab situation, opening up the potential use of the DRT to a much greater range of tasks and situations, but suggesting that in-lab applications are best when possible.

Cognitive workload is assumed to influence performance due to resource competition. However, there is a lack of evidence for a direct relationship between changes in workload within an individual over time and changes in that individual's performance. We collected performance data using a multiple object-tracking task in which we measured workload objectively in real-time using a modified detection response task. Using a multi-level Bayesian model controlling for task difficulty and past performance, we found strong evidence that workload both during and preceding a tracking trial was predictive of performance, such that higher workload led to poorer performance. These negative workload-performance relationships were remarkably consistent across individuals. Importantly, we demonstrate that fluctuations in workload independent from the task demands accounted for significant performance variation. The outcomes have implications for designing real-time adaptive systems to proactively mitigate human performance decrements, but also highlight the pervasive influence of cognitive workload more generally.

Joint modelling of behaviour and neural activation poses the potential to provide significant advances in linking brain and behaviour. However, methods of joint modelling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation which draws on current state-of-the-art Bayesian hierarchical modelling techniques and uses factor analysis as a means of dimensionality reduction to provide further information on which to make inference. The method uses a particle metropolis within Gibbs sampler (PMwG; Gunawan, Hawkins, Tran, Kohn, & Brown, 2020), where the factor structure is estimated within the Gibbs step for the group level. We show the significant dimensionality reduction gained by factor analysis in the Gibbs step of the PMwG, evidence for parameter recovery, a variety of factor loading constraints which can be used for different purposes and research questions, as well as two applications of the method to previously analysed data. This method represents a flexible and usable approach with interpretable outcomes, which relies on data driven analysis as opposed to hypothesis driven methods often used in joint modelling. Although we focus on joint modelling methods, this model based estimation approach could be used for any high dimensional modelling problem. We provide open source code and accompanying tutorial documentation to make the method accessible to any researchers.

Objective The present research applied a well-established measure of cognitive workload in driving literature to an in-lab paradigm. We then extended this by comparing the in-lab version of the task to an online version. Background The accurate and objective measurement of cognitive workload is important in many aspects of psychological research. The detection response task (DRT) is a well-validated method for measuring cognitive workload that has been used extensively in applied tasks, for example, to investigate the effects of phone usage or passenger conversation on driving, but has been used sparingly outside of this field. Method The study investigated whether the DRT could be used to measure cognitive workload in tasks more commonly used in experimental cognitive psychology and whether this application could be extended to online environments. We had participants perform a multiple object tracking (MOT) task while simultaneously performing a DRT. We manipulated the cognitive load of the MOT task by changing the number of dots to be tracked. Results Measurements from the DRT were sensitive to changes in the cognitive load, establishing the efficacy of the DRT for experimental cognitive tasks in lab-based situations. This sensitivity continued when applied to an online environment (our code for the online DRT implementation is freely available at https://osf.io/dc39s/ ), though to a reduced extent compared to the in-lab situation. Conclusion The MOT task provides an effective manipulation of cognitive workload. The DRT is sensitive to changes in workload across a range of settings and is suitable to use outside of driving scenarios, as well as via online delivery. Application Methodology shows how the DRT could be used to measure sources of cognitive workload in a range of human factors contexts.

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Objective To test the effects of enhanced display information (“symbology”) on cognitive workload in a simulated helicopter environment, using the detection response task (DRT). Background Workload in highly demanding environments can be influenced by the amount of information given to the operator and consequently it is important to limit potential overload. Methods Participants (highly trained military pilots) completed simulated helicopter flights, which varied in visual conditions and the amount of information given. During these flights, participants also completed a DRT as a measure of cognitive workload. Results With more visual information available, pilots’ landing accuracy was improved across environmental conditions. The DRT is sensitive to changes in cognitive workload, with workload differences shown between environmental conditions. Increasing symbology appeared to have a minor effect on workload, with an interaction effect of symbology and environmental condition showing that symbology appeared to moderate workload. Conclusion The DRT is a useful workload measure in simulated helicopter settings. The level of symbology-moderated pilot workload. The increased level of symbology appeared to assist pilots’ flight behavior and landing ability. Results indicate that increased symbology has benefits in more difficult scenarios. Applications The DRT is an easily implemented and effective measure of cognitive workload in a variety of settings. In the current experiment, the DRT captures the increased workload induced by varying the environmental conditions, and provides evidence for the use of increased symbology to assist pilots.

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