title: Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).
abstract: People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.
authors.affiliation: University of Iowa.
authors.forename: Aaron T
authors.initials: AT
authors.lastname: Buss
authors.forename: Tim
authors.initials: T
authors.lastname: Wifall
authors.forename: Eliot
authors.initials: E
authors.lastname: Hazeltine
authors.forename: John P
authors.initials: JP
authors.lastname: Spencer
mesh_terms: D001288:Attention / Q000502:physiology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D005625:Frontal Lobe / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology; D016571:Neural Networks, Computer; D010100:Oxygen / Q000097:blood; D011597:Psychomotor Performance / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D013003:Somatosensory Cortex / Q000502:physiology
chemical_list: D010100:Oxygen