The amount and degree of conflict of competing task demands affects one’s multi-tasking performance. Although efficiency multi-tasking can sometimes be improved over time, in many contexts, people attempt to simultaneously complete more task demands than their attentional resources allow for, and performance suffers in a degraded fashion. Some decision or processing strategies may shift, depending on the overall task demands and the types of tasks that are competing for common resources. Much is now known about tasks with common perceptual (auditory, visual, tactile), cognitive (spatial, verbal), or response (manual, verbal) demands conflict and subsequent multiple task performance suffers but the theoretical understanding of the underlying cognitive and neural mechanisms has lagged behind. Understanding the mechanisms of multi-task deficits should lead to improved technology design, decision support, and training to facilitate effective multi-task management in complex and multi-faceted scenarios. Moreover, such understanding should be particularly valuable as we strive for enhanced supervisory control of multiple unmanned aircraft vehicles, integrative visualizations, and human-machine teaming (HMT) for decision-superiority.
Mathematical modeling frameworks provide the opportunity to investigate the underlying cognitive processing of perception and decision-making using performance and physiological measurement. For instance, the demand of an auditory task on verbal working memory may alter whether one simultaneously processes visual objects in an image in a parallel (all at once) or serial (one at a time) fashion, and/or the processing rate of each object may vary depending on the cross-modal demands. Further, the load of an audio n-back may alter the rate of evidence accumulation to make multiple decisions simultaneously (was there a dog and/or a car in the image?) and/or the amount of evidence necessary before a decision is made. This project will focus on further developing mathematical models of performance and neural activity to investigate the influence of cross-modal load on information cognitive processing and decision-making. This will involve further developing the theoretical underpinnings and statistical framework of systems factorial technology (Townsend & Nozawa, 1995; Fox et al., 2021; Fox et al., 2023; Fox & Houpt, 2021) to investigate capacity, architecture, and stopping rule, and extending and applying neural and behavioral models (van Vugt et al., 2019; Ratcliff & Rouder, 1998) to make neurobehavioral predictions of drift rate and threshold of evidence accumulation in decision-making over time. The project will provide a holistic model of how cross-modal demands necessarily influences the processing of, and decision-making about, multiple visual objects, and vice versa, how a difficult visual search task influence one’s processing of, and efficiency to maintain, multiple auditory objects in working memory. The project will combine multiple mathematical frameworks to serve as a comprehensive prediction of one’s underlying processing mechanisms and decision-making strategies in complex and dynamic contexts.
Fox, E.L., Cook, A., Yang, C., Fu, H., Latthirun, K., & Howard, Z. L. (2023). Influence of dual-task load on redundant signal processing. The Quantitative Methods for Psychology, 19, 84–99.
Fox, E. L., & Houpt, J. W. (2021). A Bayesian model of capacity across trials. Journal of Mathematical Psychology, 105, 102604.
Fox, E.L., Houpt, J., & Tsang, P. (2021). Derivation and demonstration of a new metric for multi-tasking performance. Human Factors, 63, 833–853. https://doi.org/10.1177/0018720820951089.
Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9, 347-356.
Townsend, J. T., & Nozawa, G. (1995). Spatio-temporal properties of elementary perception: An investigation of parallel, serial, and coactive theories. Journal of Mathematical Psychology, 39, 321-359.
van Vugt, M. K., Beulen, M. A., & Taatgen, N. A. (2019). Relation between centro-parietal positivity and diffusion model parameters in both perceptual and memory-based decision making. Brain Research, 1715, 1-12.
Mathematical models of performance, models of neural activity, multitask management, attention and performance, systems factorial technology, Bayesian models, evidence accumulation models, decision-making