Sven Hoffmann

A perspective on neural and cognitive mechanisms of error commission

Behavioral adaptation and cognitive control are crucial for goal-reaching behaviors. Every creature is ubiquitously faced with choices between behavioral alternatives. Common sense suggests that errors are an important source of information in the regulation of such processes. Several theories exist regarding cognitive control and the processing of undesired outcomes. However, most of these models focus on the consequences of an error, and less attention has been paid to the mechanisms that underlie the commissioning of an error. In this article, we present an integrative review of neuro-cognitive models that detail the determinants of the occurrence of response errors. The factors that may determine the likelihood of committing errors are likely related to the stability of task-representations in prefrontal networks, attentional selection mechanisms and mechanisms of action selection in basal ganglia circuits. An important conclusion is that the likelihood of committing an error is not stable over time but rather changes depending on the interplay of different functional neuro-anatomical and neuro-biological systems. We describe factors that might determine the time-course of cognitive control and the need to adapt behavior following response errors. Finally, we outline the mechanisms that may proof useful for predicting the outcomes of cognitive control and the emergence of response errors in future research.

Effects of binge drinking and hangover on response selection sub-processes—a study using EEG and drift diffusion modeling

Effects of binge drinking on cognitive control and response selection are increasingly recognized in research on alcohol (ethanol) effects. Yet, little is known about how those processes are modulated by hangover effects. Given that acute intoxication and hangover seem to be characterized by partly divergent effects and mechanisms, further research on this topic is needed. In the current study, we hence investigated this with a special focus on potentially differential effects of alcohol intoxication and subsequent hangover on sub‐processes involved in the decision to select a response. We do so combining drift diffusion modeling of behavioral data with neurophysiological (EEG) data. Opposed to common sense, the results do not show an impairment of all assessed measures. Instead, they show specific effects of high dose alcohol intoxication and hangover on selective drift diffusion model and EEG parameters (as compared to a sober state). While the acute intoxication induced by binge‐drinking decreased the drift rate, it was increased by the subsequent hangover, indicating more efficient information accumulation during hangover. Further, the non‐decisional processes of information encoding decreased with intoxication, but not during hangover. These effects were reflected in modulations of the N2, P1 and N1 event‐related potentials, which reflect conflict monitoring, perceptual gating and attentional selection processes, respectively. As regards the functional neuroanatomical architecture, the anterior cingulate cortex (ACC) as well as occipital networks seem to be modulated. Even though alcohol is known to have broad neurobiological effects, its effects on cognitive processes are rather specific.

Classifying Response Correctness across Different Task Sets: A Machine Learning Approach

Erroneous behavior usually elicits a distinct pattern in neural waveforms. In particular, inspection of the concurrent recorded electroencephalograms (EEG) typically reveals a negative potential at fronto-central electrodes shortly following a response error (Ne or ERN) as well as an error-awareness-related positivity (Pe). Seemingly, the brain signal contains information about the occurrence of an error. Assuming a general error evaluation system, the question arises whether this information can be utilized in order to classify behavioral performance within or even across different cognitive tasks. In the present study, a machine learning approach was employed to investigate the outlined issue. Ne as well as Pe were extracted from the single-trial EEG signals of participants conducting a flanker and a mental rotation task and subjected to a machine learning classification scheme (via a support vector machine, SVM). Overall, individual performance in the flanker task was classified more accurately, with accuracy rates of above 85%. Most importantly, it was even feasible to classify responses across both tasks. In particular, an SVM trained on the flanker task could identify erroneous behavior with almost 70% accuracy in the EEG data recorded during the rotation task, and vice versa. Summed up, we replicate that the response-related EEG signal can be used to identify erroneous behavior within a particular task. Going beyond this, it was possible to classify response types across functionally different tasks. Therefore, the outlined methodological approach appears promising with respect to future applications.

Does response selection contribute to inhibition of return

Inhibition of return (IOR) means delayed responses for targets at a cued compared to targets at an uncued location. It is assumed to reflect delayed reallocation of attention toward a previously attended location. Besides an attentional mechanism, IOR could also be due to a cue-evoked inhibition to respond toward a cued target. In the present study, IOR with simple, compatible, and incompatible choice responses was compared and tracked by means of event-related EEG activity. IOR was amplified with simple responses but did not differ between compatible and incompatible responses. Attention-related ERP correlates were constant across cue target onset asynchronies as were, in part, behavioral effects. Early, rather sensory ERP components varied with time, reflecting sensory or attentional interaction of cue and target processing. None of these effects varied with response requirements, indicating that response selection does not contribute to IOR in manual choice response tasks.

The Psychophysiology of Action: A Multidisciplinary Endeavor for Integrating Action and Cognition

There is a vast amount of literature concerning the integration of action and cognition. Although this broad research area is of great interest for many disciplines like sports, psychology and cognitive neuroscience, only a few attempts tried to bring together different perspectives so far. Our goal is to provide a perspective to spark a debate across theoretical borders and integration of different disciplines via psychophysiology. In order to boost advances in this research field it is not only necessary to become aware of the different areas that are relevant but also to consider methodological aspects and challenges. We briefly describe the most relevant theoretical accounts to the question of how internal and external information processes or factors interact and, based on this, argue that research programs should consider the three dimensions: (a) dynamics of movements; (b) multivariate measures and; (c) dynamic statistical parameters. Only with an extended perspective on theoretical and methodological accounts, one would be able to integrate the dynamics of actions into theoretical advances.

Performance and Error Monitoring: Causes and Consequences

The adjustment of our behavior is made possible by a permanent monitoring of our actions. In this regard, the detection, monitoring, and compensation of errors are crucial because response errors are important events that often require immediate behavioral adjustments. A neurophysiological substrate of this error monitoring system can be measured in the electroencephalogram: The error negativity is a negative potential that can be observed shortly following response errors at fronto-central electrode positions. I will provide a brief overview about response or error monitoring, followed by some methodological advice on how to measure response-related EEG signals. The chapter will close with an outlook about the practical value of this central correlate of response monitoring and individual differences with respect to the efficiency of the response monitoring system. I will argue that the key correlate, the Ne, can be utilized in a concrete practical application to predict inter- and intra-individual differing behavioral adaptation.Abstract The adjustment of our behavior is made possible by a permanent monitoring of our actions. In this regard, the detection, monitoring, and compensation of errors are crucial because response errors are important events that often require immediate behavioral adjustments. A neurophysiological substrate of this error monitoring system can be measured in the electroencephalogram: The error negativity is a negative potential that can be observed shortly following response errors at fronto-central electrode positions. I will provide a brief overview about response or error monitoring, followed by some methodological advice on how to measure response-related EEG signals. The chapter will close with an outlook about the practical value of this central correlate of response monitoring and individual differences with respect to the efficiency of the response monitoring system. I will argue that the key correlate, the Ne, can be utilized in a concrete practical application to predict inter- and intra-individual differing behavioral adaptation.

Bridging the Gap between Perception and Cognition: An Overview

People evaluate their own actions continually. These evaluations are crucial for goal-directed behavior and mark the endpoint of solving one of lifes main problems: the choice between behavioral alternatives or between competing information-processing systems. Such action selection is obviously error prone—people do not always select the appropriate action in a given situation. However, people use these errors to adapt their behavior to changes not only in the environment but also in internal demands. This short-term behavioral adaption is closely related to learning and requires constant monitoring, evaluation, and adaptation of a persons own actions. Response monitoring is a key function of behavioral performance, especially in speeded tasks, where performance is reflected in a trade-off between speed and accuracy. However, a key preprocessing stage of response execution includes action selection and an adequate perceptual and cognitive integration of relevant information, which is covered in this chapter.Abstract People evaluate their own actions continually. These evaluations are crucial for goal-directed behavior and mark the endpoint of solving one of lifes main problems: the choice between behavioral alternatives or between competing information-processing systems. Such action selection is obviously error prone—people do not always select the appropriate action in a given situation. However, people use these errors to adapt their behavior to changes not only in the environment but also in internal demands. This short-term behavioral adaption is closely related to learning and requires constant monitoring, evaluation, and adaptation of a persons own actions. Response monitoring is a key function of behavioral performance, especially in speeded tasks, where performance is reflected in a trade-off between speed and accuracy. However, a key preprocessing stage of response execution includes action selection and an adequate perceptual and cognitive integration of relevant information, which is covered in this chapter.

Preface in Performance psychology: perception, action, cognition, and emotion

The scope of this book is to present a unique collective volume written by experts, with the aims of providing a scientific guide to the field of performance psychology with a focus on research from multiple disciplines and domains and synthesizing these perspectives to form a foundation for future theoretical, empirical, and applied developments.