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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,

Abstract—Brain–computer interfaces (BCIs) are prone to errors in the recognition of subject’s intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: “Are ErrP also elicited when the error is made by the interface during the recognition of the subject’s intent? ” We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human–robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5 % and 79.2%, respectively, using a classifier built with data recorded up to three months earlier. Index Terms—Anterior cingulate cortex (ACC), brain–computer interface (BCI), electroencephalogram (EEG), error-related potentials (ErrP), inverse models, presupplementary motor area (pre-SMA), single-trial classification. I.

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)

& Studies of a range of higher cognitive functions consistently activate a region of anterior cingulate cortex (ACC), typically posterior to the genu and superior to the corpus collosum. In particular, this ACC region appears to be active in task situations where there is a need to override a prepotent response tendency, when responding is underdetermined, and when errors are made. We have hypothesized that the function of this ACC region is to monitor for the presence of ``crosstalk'' or competition between incompatible responses. In prior work, we provided initial support for this hypothesis, demonstrating ACC activity in the same region both during error trials and during correct trials in task conditions designed to elicit greater response competition. In the present study, we extend our testing of this hypothesis to task situations involving underdetermined responding. Specifically, 14 healthy control subjects performed a verb-generation task during

In reinforcement learning, the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance between exploitation and exploration. Our learning scheme is based on model-based reinforcement learning, in which the Bayes inference with forgetting effect estimates the state-transition probability of the environment. The balance parameter, which corresponds to the randomness in action selection, is controlled based on variation of action results and perception of environmental change. When applied to maze tasks, our method successfully obtains good controls by adapting to environmental changes. Recently, Usher et al. [60] has suggested that noradrenergic neurons in the locus coeruleus may control the exploitation-exploration balance in a real brain and that the balance may correspond to the level of animal's selective attention. According to this scenario, we also discuss a possible implementation in the brain.

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 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