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Effects of response-set size on error-related brain activity

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Abstract

To study the effect of response-set size on action monitoring processes, the error-related negativity (Ne/ERN), the correct-related negativity (Nc/CRN), and behavioral indicators of action monitoring were compared across three groups of participants performing a two-choice, a four-choice, or an eight-choice version of the flanker task. After controlling for differential contribution of stimulus-related activity to response-locked averages resulting from large differences in response times across conditions, response-set size had strong effects on Ne/ERN and Nc/CRN. With increasing response-set size, the Ne/ERN amplitude decreased, but the Nc/CRN amplitude increased. Moreover, post-error behavioral adjustments were impaired with an increasing response-set size. These results suggest that action monitoring severely suffers when response-set size is increased. Implications of these findings for present theories of Ne/ERN and Nc/CRN are discussed.

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Notes

  1. The data from the four-choice task were already published elsewhere (Maier et al. 2008). However, the data were re-analyzed in a way that was appropriate for all three response-set size conditions. Accordingly, trimming of the behavioral data, filtering, baseline correction, and quantification of ERP components have changed.

  2. This baseline window was chosen, because it aligned the waveforms for correct and error responses with respect to the positive peaks preceding error-related brain activity. This best illustrates differences in Nc/CRN and Ne/ERN across conditions. Note, however, that the choice of baseline did not affect statistical analyses, because baseline-independent base-to-peak measures were used for component quantification.

  3. We also analyzed the data using the mean voltage in a time window of −25 to 100 ms relative to the response for the Ne/ERN and −25 to 50 ms relative to the response for the Nc/CRN. This did not change the results qualitatively.

  4. An alternative method for removing slow potentials from EEG data is to calculate so-called Surface Laplacians (see, Vidal et al. 2003).

  5. Although the error rate was the same in our three conditions, the absolute number of errors differed because of different trial numbers. To examine whether this can account for our results, we re-analyzed the data using only the first 1,280 trials from each condition. This did not change the results. Accordingly, our findings are not related to the different absolute numbers of errors in the three conditions. Another possibility is that our results reflect the different number of trials used for computing the waveforms. To control for this effect, a bootstrapping technique was applied. The smallest number of error trials in a single participant was 38. Therefore, we recomputed waveforms for each participant by randomly drawing 38 trials from each condition (correct responses and errors), and repeated this for 1000 times. For each repetition, components were quantified, and component measures were averaged across repetitions. The results obtained in this way were the same as in the main analysis, suggesting that different trial numbers were not responsible for our results.

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  1. Fachbereich Psychologie, Universität Konstanz, Fach D29, 78457, Constance, Germany

    Martin E. Maier, Marco Steinhauser & Ronald Hübner

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  1. Martin E. Maier
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  2. Marco Steinhauser
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  3. Ronald Hübner
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Correspondence to Martin E. Maier.

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Maier, M.E., Steinhauser, M. & Hübner, R. Effects of response-set size on error-related brain activity. Exp Brain Res 202, 571–581 (2010). https://doi.org/10.1007/s00221-010-2160-3

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