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When errors count: an EEG study on numerical error monitoring under performance pressure

An Erratum to this article was published on 01 April 2016

Abstract

In high-stake tests, students often display lower achievements than expected based on their skill level—a phenomenon known as choking under pressure. This imposes a serious problem for many students, especially for test-anxious individuals. Among school subjects, mathematics has been shown to be particularly vulnerable to choking. To succeed in a mathematics test, it is important to monitor ongoing responses, and to dynamically adapt to errors. However, it is largely unknown how academic pressure changes response monitoring and whether this depends on individual differences in test anxiety. In the present study, we aimed to start answering these questions by combining behavioral performance measurements with electroencephalography (EEG) indices of response monitoring. Eighteen participants performed a numerical Stroop task in two pressure scenarios: a high pressure condition modeling a real-life test situation and a low pressure control condition. While behavioral performance data provided mixed evidence, EEG indices suggested changed response monitoring in the high pressure condition as well as in relatively test-anxious participants. These findings highlight the role of response monitoring under academic performance pressure.

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Notes

  1. 1.

    Only female participants were recruited since gender differences have been reported for both mathematics (Else-Quest et al. 2010) and test anxiety (Hembree 1988). To avoid possible confutations with stereotype threat effects (Nguyen and Ryan 2008) all experiments were conducted by a team of a female and a male examiner.

  2. 2.

    The high pressure scenario used in the present study differed in some points from the scenario reported by Beilock and colleagues (e.g. Beilock et al. 2004). Since the participating psychology students in the present study were likely to be familiar with psychological experiments it seemed implausible to us to involve a cover story with confederates.

  3. 3.

    In the notation of the linear mixed models used in the present manuscript, “+” indicates a main effect and “*” indicates an interaction term.

References

  1. Ansari, D., Grabner, R. H., Koschutnig, K., Reishofer, G., & Ebner, F. (2011). Individual differences in mathematical competence modulate brain responses to arithmetic errors: an fMRI study. Learning and Individual Differences, 21(6), 636–643. doi:10.1016/j.lindif.2011.07.013.

    Article  Google Scholar 

  2. Ashcraft, M. H. (2002). Math anxiety: personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11(5), 181–185. doi:10.1111/1467-8721.00196.

    Article  Google Scholar 

  3. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: linear mixed-effects models using S4 classes. R package version 1.1-6. R. doi:http://CRAN.R-project.org/package=lme4

  4. Baumeister, R. F. (1984). Choking under pressure: self-consciousness and paradoxical effects of incentives on skillful performance. Journal of Personality and Social Psychology, 46, 610–620. doi:10.1037/0022-3514.46.3.610.

    Article  Google Scholar 

  5. Beilock, S. L. (2008). Math performance in stressful situations. Current Directions in Psychological Science, 17(5), 339–343. doi:10.1111/j.1467-8721.2008.00602.x.

    Article  Google Scholar 

  6. Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail: working memory and “choking under pressure” in math. Psychological Science, 16(2), 101–105. doi:10.1111/j.0956-7976.2005.00789.x.

    Article  Google Scholar 

  7. Beilock, S. L., & DeCaro, M. S. (2007). From poor performance to success under stress: working memory, strategy selection, and mathematical problem solving under pressure. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33(6), 983–998. doi:10.1037/0278-7393.33.6.983.

    Article  Google Scholar 

  8. Beilock, S. L., Kulp, C. A., Holt, L. E., & Carr, T. H. (2004). More on the fragility of performance: choking under pressure in mathematical problem solving. Journal of Experimental Psychology: General, 133(4), 584–600. doi:10.1037/0096-3445.133.4.584.

    Article  Google Scholar 

  9. Besner, D., & Coltheart, M. (1979). Ideographic and alphabetic processing in skilled reading of English. Neuropsychologia, 17(5), 467–472. doi:10.1016/0028-3932(79)90053-8.

    Article  Google Scholar 

  10. Bugden, S., & Ansari, D. (2011). Individual differences in children’s mathematical competence are related to the intentional but not automatic processing of Arabic numerals. Cognition, 118(1), 35–47. doi:10.1016/j.cognition.2010.09.005.

    Article  Google Scholar 

  11. Calvo, M. G., Ramos, P. M., & Estevez, A. (1992). Test anxiety and comprehension efficiency: the role of prior knowledge and working memory deficits. Anxiety Stress Coping, 5, 125–138. doi:10.1080/10615809208250492.

    Article  Google Scholar 

  12. Carter, C. S. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science, 280(5364), 747–749. doi:10.1126/science.280.5364.747.

    Article  Google Scholar 

  13. Coles, M. G., Scheffers, M. K., & Holroyd, C. B. (2001). Why is there an ERN/Ne on correct trials? Response representations, stimulus-related components, and the theory of error-processing. Biological Psychology, 56(3), 173–189. doi:10.1016/S0301-0511(01)00076-X.

    Article  Google Scholar 

  14. De Smedt, B., Ansari, D., Grabner, R. H., Hannula-Sormunen, M., Schneider, M., & Verschaffel, L. (2011). Cognitive neuroscience meets mathematics education: it takes two to Tango. Educational Research Review, 6(3), 232–237. doi:10.1016/j.edurev.2011.10.003.

    Article  Google Scholar 

  15. De Smedt, B., Verschaffel, L., & Ghesquière, P. (2009). The predictive value of numerical magnitude comparison for individual differences in mathematics achievement. Journal of Experimental Child Psychology, 103(4), 469–479. doi:10.1016/j.jecp.2009.01.010.

    Article  Google Scholar 

  16. DeCaro, M. S., Thomas, R. D., Albert, N. B., & Beilock, S. L. (2011). Choking under pressure: multiple routes to skill failure. Journal of Experimental Psychology: General, 140(3), 390–406. doi:10.1037/a0023466.

    Article  Google Scholar 

  17. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. doi:10.1016/j.jneumeth.2003.10.009.

    Article  Google Scholar 

  18. Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: a meta-analysis. Psychological Bulletin, 136(1), 103–127. doi:10.1037/a0018053.

    Article  Google Scholar 

  19. Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2), 336–353. doi:10.1037/1528-3542.7.2.336.

    Article  Google Scholar 

  20. Falkenstein, M., Hohnsbein, J., Hoormann, J., & Blanke, L. (1991). Effects of crossmodal divided attention on late ERP components. II. Error processing in choice reaction tasks. Electroencephalography and Clinical Neurophysiology, 78(6), 447–455. doi:10.1016/0013-4694(91)90062-9.

    Article  Google Scholar 

  21. Ganushchak, L. Y., & Schiller, N. O. (2008). Motivation and semantic context affect brain error-monitoring activity: an event-related brain potentials study. NeuroImage, 39(1), 395–405. doi:10.1016/j.neuroimage.2007.09.001.

    Article  Google Scholar 

  22. Gehring, W. J., Goss, B., Coles, M. G. H., Meyer, D. E., & Donchin, E. (1993). A Neural system for error detection and compensation. Psychological Science, 4(6), 385–390. doi:10.1111/j.1467-9280.1993.tb00586.x.

    Article  Google Scholar 

  23. Gehring, W. J., Liu, Y., Orr, J. M., & Carp, J. (2012). The error-related negativity (ERN/Ne). In The Oxford Handbook of Event-related Potential Components.

  24. Grabner, R. H., & Ansari, D. (2010). Promises and potential pitfalls of a “cognitive neuroscience of mathematics learning”. ZDM The International Journal on Mathematics Education, 42(6), 655–660. doi:10.1007/s11858-010-0283-4.

    Article  Google Scholar 

  25. Hajcak, G., McDonald, N., & Simons, R. F. (2003). Anxiety and error-related brain activity. Biological Psychology, 64(1–2), 77–90. doi:10.1016/S0301-0511(03)00103-0.

    Article  Google Scholar 

  26. Hajcak, G., McDonald, N., & Simons, R. F. (2004). Error-related psychophysiology and negative affect. Brain and Cognition, 56(2), 189–197. doi:10.1016/j.bandc.2003.11.001.

    Article  Google Scholar 

  27. Hajcak, G., Moser, J. S., Yeung, N., & Simons, R. F. (2005a). On the ERN and the significance of errors. Psychophysiology, 42(2), 151–160. doi:10.1111/j.1469-8986.2005.00270.x.

    Article  Google Scholar 

  28. Hajcak, G., Nieuwenhuis, S., Ridderinkhof, K. R., & Simons, R. F. (2005b). Error-preceding brain activity: robustness, temporal dynamics, and boundary conditions. Biological Psychology, 70(2), 67–78. doi:10.1016/j.biopsycho.2004.12.001.

    Article  Google Scholar 

  29. Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. Review of Educational Research (Vol. 58). doi:10.3102/00346543058001047

  30. Henik, A., & Tzelgov, J. (1982). Is three greater than five: the relation between physical and semantic size in comparison tasks. Memory & cognition, 10(4), 389–395. doi:10.3758/BF03202431.

    Article  Google Scholar 

  31. Hirsh, J. B., & Inzlicht, M. (2010). Error-related negativity predicts academic performance. Psychophysiology, 47(1), 192–196. doi:10.1111/j.1469-8986.2009.00877.x.

    Article  Google Scholar 

  32. Hodapp, V., Rohrmann, S., & Ringeisen, T. (2011). PAF-Prüfungsangstfragebogen. Hogrefe Göttingen.

  33. Holloway, I. D., & Ansari, D. (2009). Mapping numerical magnitudes onto symbols: the numerical distance effect and individual differences in children’s mathematics achievement. Journal of Experimental Child Psychology, 103(1), 17–29. doi:10.1016/j.jecp.2008.04.001.

    Article  Google Scholar 

  34. Kaufmann, L., Koppelstaetter, F., Delazer, M., Siedentopf, C., Rhomberg, P., Golaszewski, S., et al. (2005). Neural correlates of distance and congruity effects in a numerical Stroop task : an event-related fMRI study. NeuroImage, 25, 888–898. doi:10.1016/j.neuroimage.2004.12.041.

    Article  Google Scholar 

  35. Keith, N., Hodapp, V., Schermelleh-engel, K., & Moosbrugger, H. (2003). Cross-sectional and longitudinal confirmatory factor models for the german test anxiety inventory: a construct validation. Anxiety, Stress & Coping, 16(3), 251–270. doi:10.1080/1061580031000095416.

    Article  Google Scholar 

  36. Kim, E. Y., Iwaki, N., Imashioya, H., Uno, H., & Fujita, T. (2007). Error-related negativity in a visual go/no-go task: children vs adults. Developmental Neuropsychologyeuropsychology, 31(2), 181–191. doi:10.1080/87565640701190775.

    Article  Google Scholar 

  37. Kim, E. Y., Iwaki, N., Uno, H., & Fujita, T. (2005). Error-related negativity in children: effect of an observer. Developmental Neuropsychology, 28(3), 871–883. doi:10.1207/s15326942dn2803_7.

    Article  Google Scholar 

  38. Kliegl, R., Wei, P., Dambacher, M., Yan, M., & Zhou, X. (2011). Experimental effects and individual differences in linear mixed models: estimating the relationship between spatial, object, and attraction effects in visual attention. Frontiers in Psychology, 1, 1–12. doi:10.3389/fpsyg.2010.00238.

    Google Scholar 

  39. Landerl, K., Bevan, A., & Butterworth, B. (2004). Developmental dyscalculia and basic numerical capacities: a study of 8-9-year-old students. Cognition, 93(2), 99–125. doi:10.1016/j.cognition.2003.11.004.

    Article  Google Scholar 

  40. Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience, 8, 213. doi:10.3389/fnhum.2014.00213.

    Article  Google Scholar 

  41. Luck, S. J. (2014). An introduction to the event-related potential technique (2nd ed.). New York: MIT Press.

    Google Scholar 

  42. Moser, J. S., Moran, T. P., Schroder, H. S., Donnellan, M. B., & Yeung, N. (2013). On the relationship between anxiety and error monitoring: a meta-analysis and conceptual framework. Frontiers in Human Neuroscience, 7, 466. doi:10.3389/fnhum.2013.00466.

    Article  Google Scholar 

  43. Nguyen, H.-H. D., & Ryan, A. M. (2008). Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence. The Journal of Applied Psychology, 93(6), 1314–1334. doi:10.1037/a0012702.

    Article  Google Scholar 

  44. OECD. (2013). PISA 2012 results: ready to learn – students’ engagement, drive and self-beliefs (Volume III). PISA: OECD Publishing.

    Book  Google Scholar 

  45. Olvet, D., & Hajcak, G. (2009a). Reliability of error-related brain activity. Brain Research, 1284, 89–99. doi:10.1016/j.brainres.2009.05.079.

    Article  Google Scholar 

  46. Olvet, D., & Hajcak, G. (2009b). The stability of error related brain activity with increasing trials. Psychophysiology, 46, 957–961. doi:10.1111/j.1469-8986.2009.00848.x.

    Article  Google Scholar 

  47. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25–42. doi:10.1146/annurev.ne.13.030190.000325.

    Article  Google Scholar 

  48. R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/

  49. Raghubar, K. P., Barnes, M. A., & Hecht, S. A. (2010). Working memory and mathematics: a review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20, 110–122. doi:10.1016/j.lindif.2009.10.005.

    Article  Google Scholar 

  50. Ramirez, G., & Beilock, S. L. (2011). Writing about testing worries boosts exam performance in the classroom. Science, 331(6014), 211–213. doi:10.1126/science.1199427.

    Article  Google Scholar 

  51. Ridderinkhof, K. R., Van Den Wildenberg, W. P. M., Segalowitz, S. J., & Carter, C. S. (2004). Neurocognitive mechanisms of cognitive control: The role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain and Cognition, 56(2 SPEC. ISS.), 129–140. doi:10.1016/j.bandc.2004.09.016

  52. Riesel, A., Weinberg, A., Endrass, T., Meyer, A., & Hajcak, G. (2013). The ERN is the ERN is the ERN? Convergent validity of error-related brain activity across different tasks. Biological Psychology, 93(3), 377–385. doi:10.1016/j.biopsycho.2013.04.007.

    Article  Google Scholar 

  53. Rousselle, L., & Noël, M. P. (2007). Basic numerical skills in children with mathematics learning disabilities: a comparison of symbolic vs non-symbolic number magnitude processing. Cognition, 102(3), 361–395. doi:10.1016/j.cognition.2006.01.005.

    Article  Google Scholar 

  54. Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution, 1(2), 103–113. doi:10.1111/j.2041-210X.2010.00012.x.

    Article  Google Scholar 

  55. Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., & Pfurtscheller, G. (2007). A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology, 118(1), 98–104. doi:10.1016/j.clinph.2006.09.003.

    Article  Google Scholar 

  56. Schneider, M., Beeres, K., Coban, L., Simon, M., Schmidt, S. S., Stricker, J., & De Smedt, B. (2015). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: a meta-analysis. Developmental Science. doi:10.1111/desc.12372

  57. Simons, R. F. (2010). The way of our errors: theme and variations. Psychophysiology, 47(1), 1–14. doi:10.1111/j.1469-8986.2009.00929.x.

    Article  Google Scholar 

  58. Spielberger, C. D. (1980). Test Anxiety Inventory (“Test Attitude Inventory”). Preliminary professional manual. Palo Alto: Consulting Psychologists Press.

    Google Scholar 

  59. Suárez-Pellicioni, M., Núñez-Peña, M. I., & Colomé, A. (2013). Abnormal error monitoring in math-anxious individuals: evidence from error-related brain potentials. PLoS ONE, 8(11), e81143. doi:10.1371/journal.pone.0081143.

    Article  Google Scholar 

  60. Vogel, S. E., Remark, A., & Ansari, D. (2015). Differential processing of symbolic numerical magnitude and order in 1st grade children. Journal of Experimental Child Psychology, 129, 26–39. doi:10.1016/j.jecp.2014.07.010.

    Article  Google Scholar 

  61. Weinberg, A., & Hajcak, G. (2011). Longer term test-retest reliability of error-related brain activity. Psychophysiology, 48(10), 1420–1425. doi:10.1111/j.1469-8986.2011.01206.x.

    Article  Google Scholar 

  62. Weinberg, A., Riesel, A., & Hajcak, G. (2011). Integrating multiple perspectives on error-related brain activity: the ERN as a neural indicator of trait defensive reactivity. Motivation and Emotion, 36(1), 84–100. doi:10.1007/s11031-011-9269-y.

    Article  Google Scholar 

  63. Wine, J. (1971). Test anxiety and direction of attention. Psychological Bulletin, 76, 92–104. doi:10.1037/h0031332.

    Article  Google Scholar 

  64. Zeidner, M. (2007). Test anxiety in educational contexts. concepts, findings, and future directions. In Emotion in Education (pp. 165–184). doi:10.1016/B978-012372545-5/50011-3

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Acknowledgments

The authors want to thank Anna Hinze and Isabel Müller for their help with data collection.

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Correspondence to Frieder L. Schillinger.

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11858_2015_746_MOESM1_ESM.pdf

Figure S1. Scatter plot of mean response times (A) and accuracy (B) as a function ofperformance pressure and test anxiety (raw values). Numbers indicate individual subjects.Linear regression lines were fitted for each pressure condition for illustrative purposesd. (PDF 853 kb)

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Schillinger, F.L., De Smedt, B. & Grabner, R.H. When errors count: an EEG study on numerical error monitoring under performance pressure. ZDM Mathematics Education 48, 351–363 (2016). https://doi.org/10.1007/s11858-015-0746-8

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Keywords

  • Choking under pressure
  • Test anxiety
  • Response monitoring
  • Event-related negativity (ERN)