Dataset Information

Independent component analysis of erroneous and correct responses suggests online response control.

ABSTRACT: After errors in reaction tasks, a sharp negative wave emerges in the event-related potential (ERP), the error (related) negativity (Ne or ERN). However, also after correct trials, an Ne-like wave is seen, called CRN or Nc, which is much smaller than the Ne. This study tested the hypothesis whether Ne and Nc reflect the same functional process, and whether this process is linked to online response control. For this purpose, independent component analysis (ICA) was utilized with the EEG data of two types of reaction tasks: a flanker task and a mental rotation task. To control for speed-accuracy effects, speed and accuracy instructions were balanced in a between subjects design. For both tasks ICA and dipole analysis revealed one component (Ne-IC) explaining most of the variance for the difference between correct and erroneous trials. The Ne-IC showed virtually the same features as the raw postresponse ERP, being larger for erroneous compared to correct trials and for the flanker than for the rotation task. In addition, it peaked earlier for corrected than for uncorrected errors. The results favor the hypothesis that Ne and Nc reflect the same process, which is modulated by response correctness and type of task. On the basis of the literature and the present results, we assume that this process induces online response control, which is much stronger in error than correct trials and with direct rather than indirect stimulus response mapping.

SUBMITTER: Hoffmann S

PROVIDER: S-EPMC6870805 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Independent component analysis of erroneous and correct responses suggests online response control.

Hoffmann Sven S Falkenstein Michael M

Human brain mapping 20100901 9

After errors in reaction tasks, a sharp negative wave emerges in the event-related potential (ERP), the error (related) negativity (Ne or ERN). However, also after correct trials, an Ne-like wave is seen, called CRN or Nc, which is much smaller than the Ne. This study tested the hypothesis whether Ne and Nc reflect the same functional process, and whether this process is linked to online response control. For this purpose, independent component analysis (ICA) was utilized with the EEG data of tw ...[more]

PMID: 20127872

Similar Datasets

Project description:BackgroundRecent approaches for predicting the three-dimensional (3D) structure of proteins such as de novo or fold recognition methods mostly rely on simplified energy potential functions and a reduced representation of the polypeptide chain. These simplifications facilitate the exploration of the protein conformational space but do not permit to capture entirely the subtle relationship that exists between the amino acid sequence and its native structure. It has been proposed that physics-based energy functions together with techniques for sampling the conformational space, e.g., Monte Carlo or molecular dynamics (MD) simulations, are better suited to the task of modelling proteins at higher resolutions than those of models obtained with the former type of methods. In this study we monitor different protein structural properties along MD trajectories to discriminate correct from erroneous models. These models are based on the sequence-structure alignments provided by our fold recognition method, FROST. We define correct models as being built from alignments of sequences with structures similar to their native structures and erroneous models from alignments of sequences with structures unrelated to their native structures.ResultsFor three test sequences whose native structures belong to the all-alpha, all-beta and alphabeta classes we built a set of models intended to cover the whole spectrum: from a perfect model, i.e., the native structure, to a very poor model, i.e., a random alignment of the test sequence with a structure belonging to another structural class, including several intermediate models based on fold recognition alignments. We submitted these models to 11 ns of MD simulations at three different temperatures. We monitored along the corresponding trajectories the mean of the Root-Mean-Square deviations (RMSd) with respect to the initial conformation, the RMSd fluctuations, the number of conformation clusters, the evolution of secondary structures and the surface area of residues. None of these criteria alone is 100% efficient in discriminating correct from erroneous models. The mean RMSd, RMSd fluctuations, secondary structure and clustering of conformations show some false positives whereas the residue surface area criterion shows false negatives. However if we consider these criteria in combination it is straightforward to discriminate the two types of models.ConclusionThe ability of discriminating correct from erroneous models allows us to improve the specificity and sensitivity of our fold recognition method for a number of ambiguous cases.

Dataset Information

Independent component analysis of erroneous and correct responses suggests online response control.

Publications

Independent component analysis of erroneous and correct responses suggests online response control.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets