Skip to main content
SpringerLink
Log in

Physiological threat responses predict number processing

  • Original Article
  • Published:
Psychological Research Aims and scope Submit manuscript

Abstract

Being able to adequately process numbers is a key competency in everyday life. Yet, self-reported negative affective responses towards numbers are known to deteriorate numerical performance. Here, we investigated how physiological threat responses predict numerical performance. Physiological responses reflect whether individuals evaluate a task as exceeding or matching their resources and in turn experience either threat or challenge, which influences subsequent performance. We hypothesized that, the more individuals respond to a numerical task with physiological threat, the worse they would perform. Results of an experiment with cardiovascular indicators of threat/challenge corroborated this expectation. The findings thereby contribute to our understanding of the physiological mechanism underlying the influence of negative affective responses towards numbers on numerical performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The original sample included a total of sixty-one participants. However, due to technical problems, the complete set of physiological measures was only assessed for the reported fifty participants. Please note that data collection took place in two waves (i.e., during two separate weeks), as optimal sample size was not reached and gender distribution was biased in the first wave. Hence, we may have collected data from different populations. There was only a marginal difference in reaction times between the two samples for small range triplets, t(39) = 1.87, p = .070 (M first wave = 1937.73, SD = 1328.78; M second wave = 2603.65, SD = 949.32), but not for all other types of triplets. To control for potential differences between these two data sets, all measures included in the main analyses (i.e., correlations) were z-standardized. Unless otherwise reported, point in time of data collection (i.e., first vs. second wave) did not interact with the cardiovascular measures in predicting performance on the math task participants. Please note that data collection took place in two waves (i.e., during two separate weeks), as optimal sample size was not reached and gender distribution was biased in the first wave. Hence, we may have collected data from different populations. There was only a marginal difference in reaction times between the two samples for small range triplets, t(39) = 1.87, p = .070 (M first wave = 1937.73, SD = 1328.78; M second wave = 2603.65, SD = 949.32), but not for all other types of triplets. To control for potential differences between these two data sets, all measures included in the main analyses (i.e., correlations) were z-standardized. Unless otherwise reported, point in time of data collection (i.e., first vs. second wave) did not interact with the cardiovascular measures in predicting performance on the math task.

  2. Applying different outlier criteria (e.g., 2.5 or 3 SDs) as well as including the outliers with their original values did not change results substantially.

  3. Including this one outlier only changed the correlation between physiological indicators and IBSD, all other correlations between physiological indicators and performance remained.

  4. There were no interactions between threat-challenge index (TCI) and participant gender or point in time of data collection (i.e., first vs. second wave), respectively. The same is the case for total peripheral resistance (TPR). However, there was a gender x cardiac output (CO) interaction in predicting errors for incorrectly bisected triplets with a small distance (IBSD) and incorrectly bisected triplets with a large distance (IBLD). Further examination of this effect indicated that this interaction seemed to result from the point in data collection (wave 1 included almost exclusively females, wave 2 included males and females), rather than from gender itself: In the first wave (t1), female participants showed no significant correlations of CO with IBSD, r(15)female, t1 = .36, and CO with IBLD, r(15)female, t1 = −.07. However, in the second wave (t2), female participants’ correlations of CO with IBSD, r(7)female, t2 = −.45, and CO with IBLD, r(7)female, t2 = −.69, showed a pattern identical to that observed for male participants at t2, with correlations of CO with IBSD, r(27)male, t2 = −.34, and CO with IBLD, r(27) male, t2 = −.48. We assume the pattern of the second wave of data collection to be the more reliable measurement, given the bigger sample, and that evidence for task engagement (i.e. changes in PEP and HR) were clearest there. Future research, however, might examine potential gender effects—especially if gender and stereotypic expectations (e.g., with regard to worse math performance) are made salient before performing a task (see, e.g., Derks et al., 2011).

References

  • Alter, A. L., Aronson, J., Darley, J. M., Rodriguez, C., & Ruble, D. N. (2010). Rising to the threat: reducing stereotype threat by reframing the threat as a challenge. Journal of Experimental Social Psychology, 46, 166–171.

    Article  Google Scholar 

  • Ansari, T. L., & Derakshan, N. (2011). The neural correlates of cognitive effort in anxiety: effects on processing efficiency. Biological Psychology, 86, 337–348.

    Article  PubMed  Google Scholar 

  • Ashcraft, M. (2002). Math anxiety: personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11, 181–185.

    Article  Google Scholar 

  • Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130, 224–237.

    Article  Google Scholar 

  • Ashcraft, M. H., & Ridley, K. S. (2005). Math anxiety and its cognitive consequences: a tutorial review. In J. I. D. Campbell (Ed.), Handbook of mathematical cognition (pp. 315–327). New York: Psychology Press.

    Google Scholar 

  • Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.

    Google Scholar 

  • Beilock, S. L. (2008). Math performance in stressful situations. Current Directions in Psychological Science, 17, 339–343.

    Article  Google Scholar 

  • Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail: working memory and “choking under pressure” in math. Psychological Science, 16, 101–105.

    Article  PubMed  Google Scholar 

  • Blascovich, J. (2000). Psychophysiological methods. In H. T. Reis, H. Judd, & C. M. Judd (Eds.), Handbook of research methods in social psychology (pp. 117–137). Cambridge: Cambridge University Press.

    Google Scholar 

  • Blascovich, J. (2008). Challenge, threat, and health. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (pp. 481–493). New York: Guilford Press.

    Google Scholar 

  • Blascovich, J., & Mendes, W. B. (2000). Challenge and threat appraisals: the role of affective cues. In J. Forgas (Ed.), Feeling and thinking: The role of affect in social cognition (pp. 59–82). Cambridge: Cambridge University Press.

    Google Scholar 

  • Blascovich, J., & Mendes, W. B. (2010). Social psychophysiology and embodiment. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (5th ed.). New York: Wiley.

    Google Scholar 

  • Blascovich, J., Mendes, W. B., Hunter, S. B., & Salomon, K. (1999). Social “facilitation” as challenge and threat. Journal of Personality and Social Psychology, 31, 422–429.

    Article  Google Scholar 

  • Blascovich, J., Seery, M. D., Mugridge, C. A., Norris, R. K., & Weisbuch, M. (2004). Predicting athletic performance from cardiovascular indexes of challenge and threat. Journal of Experimental Social Psychology, 40, 683–688.

    Article  Google Scholar 

  • Blascovich, J., & Tomaka, J. (1996). The biopsychosocial model of arousal regulation. In M. Zanna (Ed.), Advances in experimental social psychology (Vol. 28, pp. 1–51). New York: Academic Press.

    Google Scholar 

  • Cates, G. L., & Rhymer, K. N. (2003). Examining the relationship between mathematics anxiety and mathematics performance: an instructional hierarchy perspective. Journal of Behavioral Education, 12, 23–34.

    Article  Google Scholar 

  • Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman & Hall.

    Google Scholar 

  • De Wit, F. C., Scheepers, D., & Jehn, K. A. (2012). Cardiovascular reactivity and resistance to opposing viewpoints during intragroup conflict. Psychophysiology, 49, 1691–1699.

    Article  Google Scholar 

  • Derakshan, N., & Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance: new developments from attentional control theory. European Psychologist, 14, 168–176.

    Article  Google Scholar 

  • Derks, B., Scheepers, D., Van Laar, C., & Ellemers, N. (2011). The threat vs. challenge of car parking for women: how self- and group affirmation affect cardiovascular responses. Journal of Experimental Social Psychology, 47, 178–183.

    Article  Google Scholar 

  • Eden, C., Heine, A., & Jacobs, A. M. (2013). Mathematics anxiety and its development in the course of formal schooling—a review. Psychology, 4, 27–35.

    Article  Google Scholar 

  • 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, 103–127.

    Article  PubMed  Google Scholar 

  • Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7, 336–353.

    Article  PubMed  Google Scholar 

  • Gratton, G., Coles, M. G., & Donchin, E. (1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General, 121, 480–506.

    Article  Google Scholar 

  • Gross, J., Hudson, C., & Price, D. (2009). The long term costs of numeracy difficulties. London: Every Child a Chance Trust and KPMG.

    Google Scholar 

  • Hopko, D. R., Ashcraft, M. H., Gute, J., Ruggiero, K. J., & Lewis, C. (1998). Mathematics anxiety and working memory: support for the existence of a deficient inhibition mechanism. Journal of Anxiety Disorders, 12, 343–355.

    Article  PubMed  Google Scholar 

  • Hopko, D. R., McNeil, D. W., Lejuez, C. W., Ashcraft, M. H., Eifert, G. H., & Riel, J. (2003). The effects of anxious responding on arithmetic and lexical decision task performance. Journal of Anxiety Disorders, 17, 647–665.

    Article  PubMed  Google Scholar 

  • Huber, S., Moeller, K., Nuerk, H.-C., Macizo, P., Herrera, A., & Willmes, K. (2013). Cognitive control in number processing: a computational model. In R. West & T. Stewart (Eds.), Proceedings of the 12th international conference on cognitive modeling (pp. 185–190). Ottawa: Carleton University.

    Google Scholar 

  • Jamieson, J. P., Mendes, W. B., & Nock, M. K. (2013). Improving acute stress responses: the power of reappraisal. Current Directions in Psychological Science, 22, 51–56.

    Article  Google Scholar 

  • Krinzinger, H., Kaufman, L., & Willmes, K. (2009). Math anxiety and math ability in early primary school years. Journal of Psycho-Educational Assessment, 27, 206–225.

    Article  Google Scholar 

  • Macizo, P., & Herrera, A. (2011). Cognitive control in number processing: evidence from the unit-decade compatibility effect. Acta Psychologica, 136, 112–118.

    Article  PubMed  Google Scholar 

  • Maloney, E. A., & Beilock, S. L. (2012). Math anxiety: who has it, why it develops, and how to guard against it. Trends in Cognitive Sciences, 16, 404–406. doi:10.1016/j.tics.2012.06.008.

    Article  PubMed  Google Scholar 

  • Moeller, K., Fischer, M. H., Nuerk, H., & Willmes, K. (2009). Eye fixation behaviour in the number bisection task: evidence for temporal specificity. Acta Psychologica, 131, 209–220.

    Article  PubMed  Google Scholar 

  • Moenikia, M., & Zahed-Babelan, A. (2010). A study of simple and multiple relations between mathematics attitude, academic motivation and intelligence quotient with mathematics achievement. Procedia Social and Behavioral Sciences, 2, 1537–1542.

    Article  Google Scholar 

  • Moore, L. J., Vine, S. J., Wilson, M. R., & Freeman, P. (2012). The effect of challenge and threat states on performance: an examination of potential mechanisms. Psychophysiology, 49, 1417–1425.

    Article  PubMed  PubMed Central  Google Scholar 

  • Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: verbal reports on mental processes. Psychological Review, 84, 231–259.

    Article  Google Scholar 

  • Nuerk, H.-C., Geppert, B. E., van Herten, M., & Willmes, K. (2002). On the impact of different number representations in the number bisection task. Cortex, 38, 691–715.

    Article  PubMed  Google Scholar 

  • Núñez Peña, M. I., & Colomé, À. (2014). Reactive recruitment of attentional control in math anxiety: an ERP study of numeric conflict monitoring and adaptation. PLoS ONE, 9, e99579.

    Article  PubMed  PubMed Central  Google Scholar 

  • Parsons, S., & Bynner, J. (2005) Does numeracy matter more? NRDC (National Research and Development Centre for adult literacy and numeracy).

  • Pecchinenda, A., & Smith, C. A. (1996). The motivational significance of skin conductance activity during a difficult problem-solving task. Cognition and Emotion, 10, 481–503.

    Article  Google Scholar 

  • Ramirez, G., Gunderson, E. A., Levine, S. C., & Beilock, S. L. (2013). Math anxiety, working memory and math achievement in early elementary school. Journal of Cognition and Development, 14, 187–202.

    Article  Google Scholar 

  • Scheepers, D., de Wit, F., Ellemers, N., & Sassenberg, K. (2012). Social power makes the heart work more efficiently: evidence from cardiovascular markers of challenge and threat. Journal of Experimental Social Psychology, 48, 371–374.

    Article  Google Scholar 

  • Seery, M. D., Weisbuch, M., Hetenyi, M. A., & Blascovich, J. (2010). Cardiovascular measures independently predict performance in a university course. Psychophysiology, 47, 535–539.

    Article  PubMed  Google Scholar 

  • Sherwood, A., Allen, M. T., Fahrenberg, J., Kelsey, R. M., Lovallo, W. R., & van Dooren, L. J. P. (1990). Methodological guidelines for impedance cardiography. Psychophysiology, 27, 1–23.

    Article  PubMed  Google Scholar 

  • Steele, C. M. (1988). The psychology of self-affirmation: Sustaining the integrity of the self. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 21, pp. 261–302). San Diego: Academic Press.

    Google Scholar 

  • Tomaka, J., Blascovich, J., Kelsey, R. M., & Leitten, C. L. (1993). Subjective, physiological, and behavioral effects of threat and challenge appraisal. Journal of Personality and Social Psychology, 18, 616–624.

    Article  Google Scholar 

  • Tzelgov, J., Henik, A., & Berger, J. (1992). Controlling Stroop effects by manipulating expectations for color words. Memory & Cognition, 20, 727–735.

    Article  Google Scholar 

  • Vrana, S. R., & Rollock, D. (1998). Physiological response to a minimal social encounter: effects of gender, ethnicity, and social context. Psychophysiology, 35, 462–469.

    Article  PubMed  Google Scholar 

  • Weisbuch-Remington, M., Mendes, W. B., Seery, M. D., & Blascovich, J. (2005). The influence of religious stimuli outside of subjective awareness during motivated performance situations. Personality and Social Psychology Bulletin, 31, 1203–1216.

    Article  PubMed  Google Scholar 

  • Wood, G., Nuerk, H.-C., Moeller, K., Geppert, B., Schnitker, R., Weber, J., et al. (2008). All for one but not one for all: how multiple number representations are recruited in one numerical task. Brain Research, 1187, 154–166.

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

    Authors and Affiliations

    1. Leibniz-Institut fuer Wissensmedien, Schleichstr. 6, D-72076, Tuebingen, Germany

      Annika Scholl, Korbinian Moeller & Kai Sassenberg

    2. Department of Psychology and LEAD Graduate School, University of Tuebingen, Tuebingen, Germany

      Korbinian Moeller, Hans-Christoph Nuerk & Kai Sassenberg

    3. Leiden University, Leiden, The Netherlands

      Daan Scheepers

    Authors
    1. Annika Scholl
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Korbinian Moeller
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Daan Scheepers
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Hans-Christoph Nuerk
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Kai Sassenberg
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Annika Scholl.

    Additional information

    A. Scholl and K. Moeller contributed equally to this work.

    Appendix

    Appendix

    Glossary of cardiovascular indicators:

    • Cardiac Output (CO) = cardiac performance (the higher = the more challenged)

    • Total Peripheral Resistance (TPR) = efficiency of mobilizing and transporting energy (the lower = the more challenged)

    • Threat-challenge index (TCI )= zCO—zTPR (the higher = the more challenged)

    • Heart rate (HR) = indicator for task engagement (higher than baseline = engaged)

    • Pre-ejection period (PEP) = ventricular contractility; indicator for task engagement (lower than baseline = engaged)

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Scholl, A., Moeller, K., Scheepers, D. et al. Physiological threat responses predict number processing. Psychological Research 81, 278–288 (2017). https://doi.org/10.1007/s00426-015-0719-0

    Download citation

    • Received:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s00426-015-0719-0

    Keywords

    Search

    Navigation