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Future research on cognitive control must precisely characterize the supervisory functions of executive mental processes. The achievement of this objective will be facilitated by formal concepts and algorithms from contemporary computer operating systems. In particular, operating-system fundamentals can help to advance work with the Executive-Process Interactive Control (EPIC) architecture, a theoretical framework for computational modeling of human multiple-task performance. EPIC models that incorporate general executive processes like those of operating systems provide insights about how people schedule tasks, allocate perceptual-motor resources, and coordinate task processes during multiple-task performance under both laboratory and real-world conditions. Such insights may lead to discoveries about the acquisition of procedural task knowledge and efficient multitasking skills.

According to a recent theory, anterior cingulate cortex is sensitive to response conflict, the coactivation of mutually incompatible responses. The present research develops this theory to provide a new account of the error-related negativity (ERN), a scalp potential observed following errors. Connectionist simulations of response conflict in an attentional task demonstrated that the ERN—its timing and sensitivity to task parameters—can be explained in terms of the conflict theory. A new experiment confirmed predictions of this theory regarding the ERN and a second scalp potential, the N2, that is proposed to reflect conflict monitoring on correct response trials. Further analysis of the simulation data indicated that errors can be detected reliably on the basis of post-error conflict. It is concluded that the ERN can be explained in terms of response conflict and that monitoring for conflict may provide a simple mechanism for detecting errors. Errors are an important source of information in the regulation of cognitive processes. The mechanism by which people detect and correct their errors has been the object of study for many years, but research interest has increased in recent years following the discovery of neural correlates of performance monitoring. In particular,

The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking—that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual and motor resources). The theory specifies a parsimonious mechanism that allows for concurrent execution, resource acquisition, and resolution of resource conflicts, without the need for specialized executive processes. By instantiating this mechanism as a computational model, threaded cognition provides explicit predictions of how multitasking behavior can result in interference, or lack thereof, for a given set of tasks. The authors illustrate the theory in model simulations of several representative domains ranging from simple laboratory tasks such as dual-choice tasks to complex real-world domains such as driving and driver distraction.

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When subjects switch between a pair of stimulus–response tasks, reaction time is slower on trial N if a different task was performed on trial N � 1. We present a parallel distributed processing (PDP) model that simulates this effect when subjects switch between word reading and color naming in response to Stroop stimuli. Reaction time on ‘‘switch trials’ ’ can be slowed by an extended response selection process which results from (a) persisting, inappropriate states of activation and inhibition of task-controlling representations; and (b) associative learning, which allows stimuli to evoke tasks sets with which they have recently been associated (as proposed by Allport & Wylie, 2000). The model provides a good fit to a large body of empirical data, including findings which have been seen as problematic for this explanation of switch costs, and shows similar behavior when the parameters are set to random values, supporting Allport and Wylie’s proposal. © 2001 Elsevier Science Key Words: task switching; task set; Stroop effect; parallel distributed processing; executive functions. Atkinson and Shiffrin (1968) proposed a distinction between relatively permanent cognitive structures, such as short- and long-term memory, and control processes which harness those fixed structures in order to attain specific goals. This distinction was elaborated in the following years (e.g.,

WhH subjectsswitch between two tasks, performance is slower after a taskswitch tht after a task repetition. We report five experimentsshperi thp a large part ofth)I "task-sh5S) costs" cannot be attributed to a control operation, needed to configureth cognitive system for th upcoming task (e.g., Rogers & Monsell, 1995). In all experiments subjects switchs between picture-naming and word-reading. We presented di#erent stimulieithl in just one of th two tasks, or inboth ofth9S ShSI#qH(93 were larger for stimuli presented inboth tasks ths forthH3 presented in only one task, even after more the 100 intervening trials between prime and probe events. We suggest (as proposed by Allport & Wylie, 2000)th0 stimuli acquire associationswith th tasks inwh9E th9 occur.Whu th current task activation is weak, as on aswitch of tasks, stimuli can trigger retrieval of th associated, competing task, provoking larger time costs.

Participants switched between two randomly ordered, two-choice reaction-time (RT) tasks, where an instructional cue preceded the target stimulus and indicated which task to execute. Task-switching cost dissipated passively while the participants waited for the instructional cue in order to know which task to execute (during the Response–Cue Interval). Switching cost was sharply reduced, but not abolished, when the participants actively prepared for the task switch in response to the instructional cue (during the Cue–Target Interval). The preparation for a task switch has shown not to be a by-product of general preparation by phasic alertness or predicting target onset. It is suggested that task-switching cost has at least three components reflecting (1) the passive dissipation of the previous task set, (2) the preparation of the new task set, and (3) a residual component. © 2000 Academic Press Compared to the wealth of empirical evidence regarding elementary cognitive process, relatively little is known on how these processes are controlled (Logan, 1985; Monsell, 1996). One paradigm to study cognitive control is task switching, in which participants rapidly switch between two or more choice reaction-time (RT) tasks. In most circumstances, switching tasks is associated with a sizable decrement in performance (called switching cost)

be used to assess interindividual differences in the strength of associative links between representational structures such as attitude objects and evaluations. Four experiments are reported that explore the extent of method-specific variance in the IAT. The most important findings are that conventionally scored IAT effects contain reliable interindividual differences that are method specific but independent of the measures ’ content, and that IAT effects can be obtained in the absence of a preexisting association between the response categories. Several techniques to decrease the impact of method-specific variance are evaluated. The best results were obtained with the D measures recently proposed by A. G. Greenwald,

This article provides a first look at an extensible philosophy for studying other instances of multitasking or collaborative performance. We argue that the models and framework presented here will improve the HCI community's ability to classify and evaluate existing and emerging notification systems, as well as to catalog information and interaction design guidelines and lessons learned in a cohesive, collective manner. In the next section, we present a more thorough overview of notification systems appearing in recent literature and itemize general user goals, providing motivation and background material for the model we present in Section 3