Modeling Distraction: How Stimulus-driven Attention Capture Influences Goal-directed Behavior.

The importance of paying attention to a task at hand is emphasized from an early age and extends throughout life. The costs of attentional focus, however, include the potential to miss important changes in the environment, so some process for monitoring nontask information is essential. In this study, a model of latent cognitive variables was applied to data obtained from a two-alternative forced-choice task where participants identified the longer of two sounds. Using an adaptive procedure task, accuracy was maintained at a higher or lower level creating two difficulties, and the sounds were heard either where frequency changes in the sound were rare or common (oddball and multistandard conditions, respectively). Frequency changes created stimulus-driven "distraction" effects in the oddball sequence only, and cognitive modeling (using the linear ballistic accumulator) attributed these effects to slowed accumulation of evidence about tone length on these trials. Concurrent recording of auditory ERPs revealed these delays in evidence accumulation to be related to the amplitude of N2 or mismatch negativity period and P300 response components. In contrast, the response time on trials after a rare frequency change was associated with increased caution in decision-making. Results support the utility of mapping behavioral and ERP measures of performance to latent cognitive processes that contribute to performance and are consistent with a momentary diversion of resources to evaluate the deviant sound feature and remodel predictions about sound.

A Broader Application of the Detection Response Task to Cognitive Tasks and Online Environments.

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.

The Effects of Increased Visual Information on Cognitive Workload in a Helicopter Simulator.

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.

Using group level factor models to resolve high dimensionality in model-based sampling.

Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling 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 that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

FluTracking: Weekly online community-based surveillance of influenza-like illness in Australia, 2019 Annual Report.

Abstract: FluTracking provided evidence for an early, long, but moderate influenza season in the Australian community compared to prior years. Influenza-like illness (ILI) activity in 2019 peaked earlier (week ending 16 June) than any season on record in FluTracking data. ILI attack rates were above average early in the 2019 season (peak of 2.2%), and the duration of peak activity was longer than most prior years. However, ILI attack rates were lower than the five-year average in the latter half of the season. FluTracking participants reported higher vaccination coverage in 2019 (73.3%) compared with 2018 (65.7%), with the most notable increase in children aged less than five years (69.3% in 2019, compared to 55.6% in 2018). The total 2019 count of laboratory notifications (312,945) was higher than prior years (2007 onwards), and the peak weekly count of 18,429 notifications in 2019 was also higher than all prior years, except 2017. FluTracking makes a comparison to another surveillance system each year. The peak weekly percentage of calls to HealthDirect that were influenza-related was higher in 2019 (12.8%) than for 2014-2018 (range of 8.2-11.4% for peak week of activity each year). FluTracking participants reported a 2.5 times increase in influenza testing from 2018 to 2019 and a 1.5 times increase from 2017. Although 2019 was of higher activity and severity than 2018, Flutracking data indicates that 2019 was a lower activity and severity season than 2017, and notifications and influenza-related calls were heightened by increased community concern and testing.

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance.

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.

Structure-function similarities in deep brain stimulation targets cross-species.

Deep Brain Stimulation (DBS) is an effective neurosurgical treatment to alleviate motor symptoms of advanced Parkinson's disease. Due to its potential, DBS usage is rapidly expanding to target a large number of brain regions to treat a wide range of diseases and neuropsychiatric disorders. The identification and validation of new target regions heavily rely on the insights gained from rodent and primate models. Here we present a large-scale automatic meta-analysis in which the structure-function associations within and between species are compared for 21 DBS targets in humans. The results indicate that the structure-function association for the majority of the 21 included subcortical areas were conserved cross-species. A subset of structures showed overlapping functional association. This can potentially be attributed to shared brain networks and might explain why multiple brain areas are targeted for the same disease or neuropsychiatric disorder.

How is multi-tasking different from increased difficulty?

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 multi-tasking and difficulty-based workload impairments, particularly when relying on measures of central tendency.