10.12: Key Takeaways and References
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Key Takeaways
- Many methods exist to quantify the timing or magnitude of an ERP component or experimental effect. The traditional methods—peak amplitude and peak latency—have many shortcomings, and better methods are available.
- In many cases, it is advantageous to obtain scores from a difference wave that isolates the component or experimental effect of interest.
- You should always visually verify that the scores are being calculated appropriately for each individual ERP waveform.
- Most scoring methods require specifying a measurement window, and this needs to be done in an unbiased manner. If you decide on the measurement window after seeing the waveforms, you may consciously or unconsciously choose a window that increases the probability of bogus-but-significant effects.
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When you have many conditions and/or channels, it’s easy to accidentally put the cells of the design into the wrong order in the statistical analysis. You should always check the table of means produced by your statistical package and make sure it matches what you are seeing in the grand average ERP waveforms.
- For mean amplitude, but not most other scoring methods, taking the score from the grand average waveforms gives you the same result as measuring from the single-participant waveforms and then averaging. This makes it easy to compare the table of means with the grand averages.
- For other measures, you can still make sure that the table of means shows the same pattern as the grand averages, even if the individual values are not identical.
- To reduce the number of p values and the likelihood of bogus-but-significant effects, you should use the smallest possible number of factors in your statistical analyses. This can often be achieved by collapsing across channels and obtaining scores from difference waves.
References
Donchin, E., & Heffley, E. F. (1978). Multivariate analysis of event-related potential data: A tutorial review. In D. Otto (Ed.), Multidisciplinary Perspectives in Event-Related Brain Potential Research (pp. 555–572). U.S. Government Printing Office.
Eriksen, C. W. (1995). The flankers task and response competition: A useful tool for investigating a variety of cognitive problems. Visual Cognition , 2 , 101–118.
Gehring, W. J., Liu, Y., Orr, J. M., & Carp, J. (2012). The error-related negativity (ERN/Ne). In S. J. Luck & E. S. Kappenman (Eds.), The Oxford Handbook of Event-Related Potential Components (pp. 231–292). Oxford University Press.
Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre- and post-stimulus activation of response channels: A psychophysiological analysis. Journal of Experimental Psychology: Human Perception and Performance , 14 , 331–344.
Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An Open Resource for Human Event-Related Potential Research. NeuroImage , 225 , 117465. https://doi.org/10.1016/j.neuroimage.2020.117465
Kiesel, A., Miller, J., Jolicoeur, P., & Brisson, B. (2008). Measurement of ERP latency differences: A comparison of single-participant and jackknife-based scoring methods. Psychophysiology , 45 , 250–274. https://doi.org/10.1111/j.1469-8986.2007.00618.x
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Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, Second Edition . MIT Press.
Luck, S. J., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn’t). Psychophysiology , 54 , 146–157. https://doi.org/10.1111/psyp.12639
Luck, S. J., Stewart, A. X., Simmons, A. M., & Rhemtulla, M. (2021). Standardized measurement error: A universal metric of data quality for averaged event-related potentials. Psychophysiology , 58 , e13793. https://doi.org/10.1111/psyp.13793
Smulders, F. T. Y., & Miller, J. O. (2012). The Lateralized Readiness Potential. In S. J. Luck & E. S. Kappenman (Eds.), The Oxford Handbook of Event-Related Potential Components (pp. 209–229). Oxford University Press.
Urbach, T. P., & Kutas, M. (2002). The intractability of scaling scalp distributions to infer neuroelectric sources. Psychophysiology , 39 , 791–808. https://doi.org/10.1017/S0048577202010648
Urbach, T. P., & Kutas, M. (2006). Interpreting event-related brain potential (ERP) distributions: Implications of baseline potentials and variability with application to amplitude normalization by vector scaling. Biological Psychology , 72 (3), 333–343. https://doi.org/10.1016/j.biopsycho.2005.11.012