Using response time modeling to understand the sources of dual-task interference in a dynamic environment

This article examines the causes of dual-task interference in a time pressured dynamic environment. Resource sharing theories are often used as a theoretical framework to understand dual-task interference. These frameworks propose that resources from a limited pool of information-processing capacity are reallocated toward the primary task as task load increases and, as a result, secondary-task performance declines if the total demand exceeds capacity limit. However, tests of resource models have relied on behavioral results that could be because of a number of different cognitive processes, including changes in response caution, rate of information processing, nondecision processes, and response biases. We applied evidence-accumulation models to quantify the cognitive processes underlying performance in a dual-task paradigm to examine the causes underlying dual-task interference. We fit performance in time-pressured environment on both a primary classification task and a secondary detection task using evidence-accumulation models. Under greater time pressure, the rate of information processing increased for the primary task while response caution decreased, whereas the rate of information processing for the secondary task declined with greater time pressure. Assuming the rate of evidence accumulation is proportional to available capacity these results are consistent with resource theory and highlight the value of evidence-accumulation models for understanding the complex set of processes underlying dual-task interference

An Integrated Approach to Testing Dynamic, Multilevel Theory: Using Computational Models to Connect Theory, Model, and Data

Some of the most influential theories in organizational sciences explicitly describe a dynamic, multilevel process. Yet the inherent complexity of such theories makes them difficult to test. These theories often describe multiple subprocesses that interact reciprocally over time at different levels of analysis and over different time scales. Computational (i.e., mathematical) modeling is increasingly advocated as a method for developing and testing theories of this type. In organizational sciences, however, efforts that have been made to test models empirically are often indirect. We argue that the full potential of computational modeling as a tool for testing dynamic, multilevel theory is yet to be realized. In this article, we demonstrate an approach to testing dynamic, multilevel theory using computational modeling. The approach uses simulations to generate model predictions and Bayesian parameter estimation to fit models to empirical data and facilitate model comparisons. This approach enables a direct integration between theory, model, and data that we believe enables a more rigorous test of theory

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving 2-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if 2 different prints belong to the same finger or different fingers. Here, we apply a dynamic decision-making model—the linear ballistic accumulator (LBA)—to fingerprint discrimination decisions to gain insight into the cognitive processes underlying these complex perceptual judgments. Across 3 experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli

Evidence accumulation in a complex visual domain: applying the linear ballistic accumulator to fingerprint discrimination

59th Annual Meeting Psychonomic SocietyHector Palada, Rachel A. Searston, Annabel Persson, Matthew B. Thompson, Timothy Ballar

An evidence accumulation model of perceptual discrimination with naturalistic stimuli

Online First Publication, May 7, 2020Evidence accumulation models have been used to describe the cognitive processes underlying performance in tasks involving 2-choice decisions about unidimensional stimuli, such as motion or orientation. Given the multidimensionality of natural stimuli, however, we might expect qualitatively different patterns of evidence accumulation in more applied perceptual tasks. One domain that relies heavily on human decisions about complex natural stimuli is fingerprint discrimination. We know little about the ability of evidence accumulation models to account for the dynamic decision process of a fingerprint examiner resolving if 2 different prints belong to the same finger or different fingers. Here, we apply a dynamic decision-making model-the linear ballistic accumulator (LBA)-to fingerprint discrimination decisions to gain insight into the cognitive processes underlying these complex perceptual judgments. Across 3 experiments, we show that the LBA provides an accurate description of the fingerprint discrimination decision process with manipulations in visual noise, speed-accuracy emphasis, and training. Our results demonstrate that the LBA is a promising model for furthering our understanding of applied decision-making with naturally varying visual stimuli. (PsycInfo Database Record (c) 2020 APA, all rights reserved).Hector Palada, Rachel A. Searston, Annabel Persson, Timothy Ballard, and Matthew B. Thompso

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