Abstract not found

This article describes the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT–R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.

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 present study employed event-related fMRI and EEG to investigate the biological basis of the cognitive control of behavior. Using a GO/NOGO task optimized to produce response inhibitions, frequent commission errors, and the opportunity for subsequent behavioral correction, we identified distinct cortical areas associated with each of these specific executive processes. Two cortical systems, one involving right prefrontal and parietal areas and the second regions of the cingulate, underlay inhibitory control. The involvement of these two systems was predicated upon the difficulty or urgency of the inhibition and each was employed to different extents by high- and low-absentminded subjects. Errors were associated with medial activation incorporating the anterior cingulate and pre-SMA while behavioral alteration subsequent to errors was associated with both the anterior cingulate and the left prefrontal cortex. Furthermore, the EEG data demonstrated that successful response inhibition depended upon the timely activation of cortical areas as predicted by race models of response selection. The results highlight how higher cognitive functions responsible for behavioral control can result from the dynamic interplay of distinct cortical systems. © 2002 Elsevier Science (USA)

Abstract not found

Adaptive automation refers to technology that can change its mode of operation dynamically. Further, both the technology and the operator can initiate changes in the level or mode of automation. The present paper reviews research on adaptive technology. The paper is intended as a guide and review for those seeking to use psychophysiological measures in design and assessing adaptively automated systems. It is divided into four primary sections. In the first section, issues surrounding the development and implementation of adaptive automation are presented. Because physiological-based measures show much promise for implementing adaptive automation, the second section is devoted to examining candidate indices and reviews some of the current research on these measures as they relate to workload. In the third section, detailed discussion is devoted to electroencephalogram (EEG) and eventrelated potentials (ERPs) measures of workload. The final section provides an example of how p...

This research uses fMRI to understand the role of eight cortical regions in a relatively complex information-processing task. Modality of input (visual versus auditory) and modality of output (manual versus vocal) are manipulated. Two perceptual regions (auditory cortex and fusiform gyrus) only reflected perceptual encoding. Two motor regions were involved in information rehearsal as well as programming of overt actions. Two cortical regions (parietal and prefrontal) performed processing (retrieval and representational change) independent of input and output modality. The final two regions (anterior cingulate and caudate) were involved in control of cognition independent of modality of input or output and content of the material. An information-processing model, based on the ACT-R theory, is described that predicts the BOLD response in these regions. Different modules in the theory vary in the degree to which they are modality-specific and the degree to which they are involved in central versus peripheral cognitive processes.

this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single -trial discrimination performance of A z # 0.80 and fraction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensory-- motor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy. 2002 Elsevier Science (USA)

The concept of error detection plays a central role in theories of executive control. In this article, the authors present a mechanism that can rapidly detect errors in speeded response time tasks. This error monitor assigns values to the output of cognitive processes involved in stimulus categorization and response generation and detects errors by identifying states of the system associated with negative value. The mechanism is formalized in a computational model based on a recent theoretical framework for understanding error processing in humans (C. B. Holroyd & M. G. H. Coles, 2002). The model is used to simulate behavioral and event-related brain potential data in a speeded response time task, and the results of the simulation are compared with empirical data. Frontal parts of the brain, including the prefrontal cortex (Luria, 1973; Stuss & Knight, 2002), the anterior cingulate cortex (Devinsky, Morrell, & Vogt, 1995; Posner & DiGirolamo, 1998), and their connections with the basal ganglia (L. L. Brown, Schneider, & Lidsky, 1997; Cummings, 1993), are thought to compose an executive system for cognitive control. The functions of this system are thought to include setting high-level goals, directing other

The concepts of “attention to action ” (Norman & Shallice, 1986) and “selection for action ” (Allport, 1987) refer to how particular cognitive intentions and sensory inputs are selected and coupled with the effector