Neural correlates of error detection during complex response selection: Introduction of a novel eight-alternative response task.

Error processing in complex decision tasks should be more difficult compared to a simple and commonly used two-choice task. We developed an eight-alternative response task (8ART), which allowed us to investigate different aspects of error detection. We analysed event-related potentials (ERP; N = 30). Interestingly, the response time moderated several findings. For example, only for fast responses, we observed the well-known effect of larger error negativity (Ne) in signalled and non-signalled errors compared to correct responses, but not for slow responses. We identified at least two different error sources due to post-experimental reports and certainty ratings: impulsive (fast) errors and (slow) memory errors. Interestingly, the participants were able to perform the task and to identify both, impulsive and memory errors successfully. Preliminary evidence indicated that early (Ne-related) error processing was not sensitive to memory errors but to impulsive errors, whereas the error positivity seemed to be sensitive to both error types.

Performance monitoring beyond choice tasks: The time course of force execution monitoring investigated by event-related potentials and multivariate pattern analysis.

Accurate force production is an essential motor function which, in most cases, requires continuous performance monitoring. Unlike choice-response tasks with two response alternatives, the accuracy in a force production paradigm is defined as an area between an upper and lower limit on the force continuum. In the present study, we investigated the neural mechanisms underlying force production. We used a force production task in which the participants (n = 48) were asked to exert a brief force pulse within a specific force range. This allowed: (1) investigation of action monitoring activity during force execution using response-locked and feedback-locked event-related potential (ERP) components known to be involved in error monitoring; (2) multivariate pattern analysis (MVPA) for ERPs. We found that the different force production ranges (characterised as too low, correct, and too high with respect to the target force range) showed no clear error-specific variations in the ERP components of interest. MVPA, on the other hand, allowed for successful classification, not only between the correct and the incorrect outcome conditions, but also between the two incorrect outcome conditions. This suggests that the classifier identified neural patterns reflecting the force magnitude rather than the correctness of a response. Moreover, additional support-vector regression (SVR) analyses showed that single-trial response parameters (i.e. peak force and time-to-peak) could be decoded from the brain activity pattern starting from 140 ms (for peak force) and 270 ms (for time-to-peak) before the response onset. These results indicate that the motor program defined the magnitude and timing of the force pulse before response execution, while the correctness of that response (in relation to the "default force" required) was not yet foreshadowed in neural signals. Finally, this study presents the first evidence of a post-error force adjustment mechanism, for which participants produced a higher force in trials after under-producing the required force, and a lower force in trials after over-producing the required force.

Correction to: Eye movements and brain oscillations to symbolic safety signs with different comprehensibility.

After the publication of the original article [1] it was highlighted that there was an omission regarding the online resources for the traffic signs in the section of "Experimental stimuli".

Eye movements and brain oscillations to symbolic safety signs with different comprehensibility.

BACKGROUND: The aim of this study was to investigate eye movements and brain oscillations to symbolic safety signs with different comprehensibility. METHODS: Forty-two young adults participated in this study, and ten traffic symbols consisting of easy-to-comprehend and hard-to-comprehend signs were used as stimuli. During the sign comprehension test, real-time eye movements and spontaneous brain activity [electroencephalogram (EEG) data] were simultaneously recorded. RESULTS: The comprehensibility level of symbolic traffic signs significantly affects eye movements and EEG spectral power. The harder to comprehend the sign is, the slower the blink rate, the larger the pupil diameter, and the longer the time to first fixation. Noticeable differences on EEG spectral power between easy-to-comprehend and hard-to-comprehend signs are observed in the prefrontal and visual cortex of the human brain. CONCLUSIONS: Sign comprehensibility has significant effects on real-time nonintrusive eye movements and brain oscillations. These findings demonstrate the potential to integrate physiological measures from eye movements and brain oscillations with existing evaluation methods in assessing the comprehensibility of symbolic safety signs.