Flexibly shifting attention between stimulus dimensions (e.g., shape and color) is a central component of regulating cognition for goal-based behavior. In the present report, we examine the functional roles of different cortical regions by manipulating two demands on task switching that have been confounded in previous studies—shifting attention between visual dimensions and resolving conflict between stimulus–response representations. Dimensional shifting was manipulated by having participants shift attention between dimensions (either shape or color; dimension shift) or keeping the task-relevant dimension the same (dimension same). Conflict between stimulus–response representations was manipulated by creating conflict between response-driven associations from the previous set of trials and the stimulus–response mappings on the current set of trials (e.g., making a leftward response to a red stimulus during the previous task, but being required to make a rightward response to a red stimulus in the current task; stimulus–response conflict), or eliminating conflict by altering the features of the dimension relevant to the sorting rule (stimulus–response no-conflict). These manipulations revealed activation along a network of frontal, temporal, parietal, and occipital cortices. Specifically, dimensional shifting selectively activated frontal and parietal regions. Stimulus–response conflict, on the other hand, produced decreased activation in temporal and occipital cortices. Occipital regions demonstrated a complex pattern of activation that was sensitive to both stimulus–response conflict and dimensional attention switching. These results provide novel information regarding the distinct role that frontal cortex plays in shifting dimensional attention and posterior cortices play in resolving conflict at the stimulus level.

Visual working memory (VWM) is essential for executive function and is known to be compromised in older adults. Yet, the cognitive and neural processes associated with these age-related changes remain inconclusive. The purpose of this study was to explore such factors with a dynamic neural field (DNF) model that was manipulated to replicate the behavioral performances of younger and older adults in a change detection task. Although previous work has successfully modeled children and younger adult VWM performance, this study represents the first attempt to model older adult VWM performance within the DNF architecture. In the behavioral task, older adults performed worse than younger adults and exhibited a characteristic response bias that favored “same” over “different” responses. The DNF model was modified to capture the age group differences, with three parameter manipulations producing the best fit for the behavioral performances. The best-fitting model suggests that older adults operate through altered excitatory and inhibitory coupling and decreased inhibitory signals, resulting in wider and weaker neural signals. These results support a dedifferentiation account of brain aging, with older adults operating with wider and weaker neural signals because of decreased intracortical inhibition rather than increased stochastic neural noise.

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.