A cognitive model of response omissions in distraction paradigms

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

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.

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.