Neuroscience
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This study investigates the error processing components in the EEG signal of Performers and Observers using an auditory lexical decision task, in which participants heard spoken items and decided for each item if it was a real word or not. Pairs of participants were tested in both the role of the Performer and the Observer. In the literature, an Error Related Negativity (ERN)-Error Positivity (Pe) complex has been identified for performed (ERN-Pe) and observed (oERN-oPe) errors. ⋯ Our hypothesis regarding the ERN was not supported, however a Pe-like effect, as well as a P300 were present. Analyses to disentangle lexical and error processing similarly indicated a P300 for errors, and the results furthermore pointed to differences between responses before and after word offset. The findings are interpreted as marking attention during error processing during auditory word recognition.
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The prediction of the sensory consequences of physical movements is a fundamental feature of the human brain. This function is attributed to a forward model, which generates predictions based on sensory and efferent information. The neural processes underlying such predictions have been studied using the error-related negativity (ERN) as a fronto-central event-related potential in electroencephalogram (EEG) tracings. ⋯ The findings suggest that early in learning, the motor control system relies more on information from external feedback about terminal outcome. With increasing task performance, the forward model is able to generate more accurate outcome predictions, which, as a result, increasingly contributes to error processing. The data also suggests a complementary relationship between the ERN and the FRN over motor learning.
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Post-error slowing during instrumental learning is shaped by working memory-based choice strategies.
Post-error slowing (PES) - a relative increase in response time for a decision on trialtgiven an error on trialt - 1 - is a well-known effect in studies of human decision-making. Post-error processing is reflected in neural signatures such as reduced activity in sensorimotor regions and increased activity in medial prefrontal cortex. PES is thought to reflect the deployment of executive resources to get task performance back on track. ⋯ PES decreased withload, even when the progress of learning (i.e., reinforcement history) was accounted for. This result suggested that PES during learning is influenced by the recruitment of working memory. Indeed, observed PES effects were approximated by a computational model with parallel working memory and reinforcement learning systems that are differentially recruited according to cognitive load.
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To examine the neural processing of valence-dependent augmented feedback, 38 students learned a sequential arm movement task with 192 trials in each of five practice sessions. The degree of motor automatization was tested under dual-task-conditions. Electroencephalogram (EEG) was recorded in the first and last practice session. ⋯ It was found that more negative amplitudes of the feedback-related-negativity (FRN) after negative feedback were predictive for goal-independent changes of behavior in the early practice phase, whereas more positive amplitudes of the late fronto-central positivity (LFCP) after negative feedback were predictive for goal-directed behavioral adaptations (error reduction), independent from the practice phase. Unexpectedly, more positive amplitudes of the P300 after positive feedback were also predictive for goal-directed behavioral adaptations. Concerning long-term learning and motor automatization, a positive correlation was found for the reduction of dual-task costs (DTC) and LFCP-amplitudes after positive feedback in the early practice.