Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the
In a Dutch auction, an item is offered for sale at a set maximum price. The price is then gradually lowered over a fixed interval of time until a bid is made, securing the item for the bidder at the current price. Bidders must trade-off between certainty and price: bid early to secure the item and you pay a premium; bid later at a lower price but risk losing to another bidder. These properties of Dutch auctions provide new opportunities to study competitive decision-making in a group setting. We developed a novel computerised Dutch auction platform and conducted a set of experiments manipulating volatility (fixed vs varied number of items for sale) and price reduction interval rate (step-rate). Triplets of participants ($$N=66$$ N = 66 ) competed with hypothetical funds against each other. We report null effects of step-rate and volatility on bidding behaviour. We developed a novel adaptation of prospect theory to account for group bidding behaviour by balancing certainty and subjective expected utility. We show the model is sensitive to variation in auction starting price and can predict the associated changes in group bid prices that were observed in our data.
Discrete choice (DCE) and rating scale experiments (RSE) are commonly applied procedures for eliciting preference judgments in a plethora of applied settings such as consumer choice and health care. Despite their common use, differences and similarities of DCE and RSE preference judgments are not yet fully understood. In typical studies using these elicitation methods, response options vary on multiple attributes and there is no objectively correct response which exacerbates comparison between the approaches. To facilitate a comparison of DCE and RSE, we conducted a perceptual discrimination experiment where response options varied on a single attribute -- stimulus saturation level -- with a known objectively correct response. Each participant completed both a DCE and RSE version of the experiment. Results indicate that there was no difference in accuracy across DCE and RSE. Furthermore, we developed and applied a cognitive model with a response mechanism for both DCE and RSE based on latent Gaussian stimulus representations. This enabled us to compare a model version that featured one shared latent stimulus representation across DCE and RSE versus a model version which featured a separate latent representation for DCE and RSE. Bayes factor model comparison favored a shared representation across DCE and RSE for 71% of participants. Parameter inference indicated that, if the representations differ across tasks, the DCE yielded a higher stimulus discrimination.
In the modern world, there are important tasks that have become too complex for a single unaided individual to manage. Some safety-critical tasks are conducted by teams to improve task performance and minimize risk of error. These teams have traditionally consisted of human operators, yet nowadays AI and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task, modeled after a classic arcade game, to investigate the performance of human-machine and human-human teams. We manipulated the group conditions between team members; sometimes they were incentivised to collaborate, sometimes compete, and sometimes to work separately. We evaluated players’ performance in the main task (game play) and also measured the cognitive workload they experienced. We compared workload and game performance between different team types (human-human vs. human-machine) and different group conditions (competitive, collaborate, independent). Adapting workload capacity analysis to human-machine teams, we found performance under both team types and all group conditions suffered a performance efficiency cost. However, we observed a reduced cost in collaborative over competitive teams within human-human pairings but this effect was diminished when playing with a machine partner. The implications of workload capacity analysis as a powerful tool for human-machine team performance measurement are discussed.
The surge in air traffic increases the workload experienced by air traffic controllers (ATC) while they organise traffic-flow and prevent conflicts between aircraft. Even though several factors influence the complexity of ATC tasks, keeping track of the aircraft and preventing collision are the most crucial. We have designed tracking and collision prediction tasks to elucidate the differences in the physiological response to the workload variations in these basic ATC tasks to untangle the impact of workload variations experienced by operators working in a complex ATC environment. Physiological measures, such as electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data, were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. The mental workload in the tracking task was found to be positively correlated with the frontal theta power and negatively correlated with the occipital alpha power. In contrast, for the collision prediction task, the frontal theta, parietal theta, occipital delta, and theta power were positively correlated, and parietal alpha power was negatively correlated with the increases in mental workload. The pupil size, number of blinks and HRV metric, root mean square of successive difference (RMSSD), also varied significantly with the mental workload in both these tasks in a similar manner. Our findings indicate that variations in task load are sensitively reflected in physiological signals, such as EEG, eye activity and HRV, in these basic ATC-related tasks. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just 'when' but also 'what' to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in ATC and beyond.
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