Inhibitory cognitive control allows automated advice to improve accuracy while minimizing misuse

Humans increasingly use automated decision aids. However, environmental uncertainty means that automated advice can be incorrect, creating the potential for humans to action incorrect advice or to disregard correct advice. We present a quantitative model of the cognitive process by which humans use automation when deciding whether aircraft would violate minimum separation. The model closely fitted the performance of twenty-four participants, whom each made 2400 conflict detection decisions (conflict vs non-conflict), either manually (with no assistance) or with the assistance of 90% reliable automation. When the decision aid was correct, conflict detection accuracy improved, but when the decision aid was incorrect, accuracy and response time were impaired. The model indicated that participants integrated advice into their decision process by inhibiting evidence accumulation toward the task response incongruent with that advice, thereby ensuring that decisions could not be made solely on automated advice without first sampling information from the task environment

Modelling how humans use decision aids in simulated air traffic control

Air traffic controllers must often decide whether pairs of aircraft will violate safe standards of separation in the future, a task known as conflict detection. Recent research has applied evidence accumulation models (e.g., the linear ballistic accumulator; Brown & Heathcote, 2008) to simulated conflict detection tasks, to examine how the cognitive processes underlying conflict detection are affected by workplace factors such as time pressure and multiple task demands (e.g., Boag, Strickland, Loft & Heathcote, 2019). To meet increasing air traffic demands in future, controllers will increasingly require assistance from automation. Although automation can increase efficiency and overall performance, it may also decrease operator engagement, leading to potentially dire consequences in the event of an automation failure. In the current study, we applied the linear ballistic accumulator model to examine how humans adapt to automated decision aids when performing simulated conflict detection. Participants performed manual conditions, in which they made conflict detection decisions with no assistance. They also performed automated conditions, in which they were provided an (accurate but not perfect) decision aid that recommended a decision on each trial. We found that decision aids improved performance, primarily by inhibiting evidence accumulation towards the incorrect decision. Similarly, incorrect decision aids (i.e., automation failures) impaired performance because accumulation to the correct decision was inhibited. To account for these findings, we develop a framework for understanding human information integration with potentially broad applications. Future research should investigate how cognitive processes are affected by differing levels of automation reliability, and test whether our model applies to other important task contexts

Cognitive control and capacity for prospective memory in complex dynamic environments

Performing deferred actions in the future relies upon Prospective Memory (PM). Often, PM demands arise in complex dynamic tasks. Not only can PM be challenging in such environments, the processes required for PM may affect the performance of other tasks. To adapt to PM demands in such environments, humans may use a range of strategies, including flexible allocation of cognitive resources and cognitive control mechanisms. We sought to understand such mechanisms by using the Prospective Memory Decision Control (Strickland, Loft, Remington, & Heathcote, 2018) model to provide a comprehensive, quantitative account of dual task performance in a complex dynamic environment (a simulated air traffic control conflict detection task). We found that PM demands encouraged proactive control over ongoing task decisions, but that this control was reduced at high time pressure to facilitate fast responding. We found reactive inhibitory control over ongoing task processes when PM targets were encountered, and that time pressure and PM demand both affect the attentional system, increasing the amount of cognitive resources available. However, as demands exceeded the capacity limit of the cognitive system, resources were reallocated (shared) between the ongoing and PM tasks. As the ongoing task used more resources to compensate for additional time pressure demands, it drained resources that would have otherwise been available for PM task processing. This study provides the first detailed quantitative understanding of how attentional resources and cognitive control mechanisms support PM and ongoing task performance in complex dynamic environments

Evidence accumulation modelling in the wild: Understanding safety-critical decisions

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research

Combining error-driven models of associative learning with evidence accumulation models of decision-making

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings

Fadenwürmer - fade Würmer?

Hohberg K, Traunspurger W. Fadenwürmer - fade Würmer? Biologie in unserer Zeit. 2018;48(1):54-61