Motivation and Emotion

, Volume 42, Issue 3, pp 386–402 | Cite as

Effects of implicit fear of failure on cognitive processing: A diffusion model analysis

  • Veronika Lerche
  • Andreas B. Neubauer
  • Andreas Voss
Original Paper


Whereas previous studies suggest that individuals with high implicit fear of failure (FF) perform worse on various indicators of general performance, the underlying mechanisms of this effect have not yet been understood. In our experimental study, 280 participants worked on a binary color discrimination task. Half of the participants were frustrated by means of negative performance feedback, while the control group received mainly positive feedback. We employed a diffusion model analysis (Ratcliff in Psychol Rev 85(2):59–108, 1978) to disentangle the different components involved in the execution of the task. Results revealed that participants in the frustration condition adopted more conservative decision settings (threshold separation parameter of the diffusion model). Besides, high implicit FF was related to slow information accumulation (drift), and this relation was stronger in the frustration condition. Participants with higher FF further showed reduced learning rates during the task. Task related intrusive thoughts are discussed as mechanism for reduced performance of high FF individuals. We conclude that diffusion model analyses can contribute to a better understanding of the mechanisms underlying the effects of psychological motives.


Diffusion model Achievement motive Fear of failure 


Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.


  1. Abele, A. E., Andrä, M. S., & Schute, M. (1999). Wer hat nach dem Hochschulexamen schnell eine Stelle? Erste Ergebnisse der Erlanger Längsschnittstudie (BELA-E) [Who is able to find employment after having finished a university exam?] Zeitschrift für Arbeits-und Organisationspsychologie, 43(2), 95–101.CrossRefGoogle Scholar
  2. Arnold, N. R., Bröder, A., & Bayen, U. J. (2015). Empirical validation of the diffusion model for recognition memory and a comparison of parameter-estimation methods. Psychological Research Psychologische Forschung, 79(5), 882–898. Scholar
  3. Atkinson, J. W. (1981). Studying personality in the context of an advanced motivational psychology. American Psychologist, 36(2), 117–128. Scholar
  4. Baumann, N., Kazén, M., & Kuhl, J. (2010). Implicit motives: A look from personality systems interaction theory. In O. C. Schultheiss & J. C. Brunstein (Eds.), Implicit Motives (pp. 375–403). Oxford: Oxford University Press.CrossRefGoogle Scholar
  5. Bowen, H. J., Spaniol, J., Patel, R., & Voss, A. (2016). A diffusion model analysis of decision biases affecting delayed recognition of emotional stimuli. PLoS ONE, 11(1), 1–20. Scholar
  6. Brunstein, J. C., & Hoyer, S. (2002). Implizites und explizites Leistungsstreben: Befunde zur Unabhängigkeit zweier Motivationssysteme [Implicit versus explicit achievement strivings: Empirical evidence of the independence of two motivational systems]. Zeitschrift für Pädagogische Psychologie, 16, 51–62.CrossRefGoogle Scholar
  7. Brunstein, J. C., & Maier, G. W. (2005). Implicit and self-attributed motives to achieve: Two separate but interacting needs. Journal of Personality & Social Psychology, 89(2), 205–222. Scholar
  8. Dutilh, G., Vandekerckhove, J., Tuerlinckx, F., & Wagenmakers, E.-J. (2009). A diffusion model decomposition of the practice effect. Psychonomic Bulletin & Review, 16(6), 1026–1036. Scholar
  9. Ehring, T., & Watkins, E. R. (2008). Repetitive negative thinking as a transdiagnostic process. International Journal of Cognitive Therapy, 1(3), 192–205. Scholar
  10. Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality & Social Psychology, 72(1), 218–232.CrossRefGoogle Scholar
  11. Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition and Emotion, 6(6), 409–434. Scholar
  12. Gable, S. L. (2006). Approach and avoidance social motives and goals. Journal of Personality, 74(1), 175–222. Scholar
  13. Germar, M., Schlemmer, A., Krug, K., Voss, A., & Mojzisch, A. (2014). Social influence and perceptual decision making: A diffusion model analysis. Personality and Social Psychology Bulletin, 40(2), 217–231. Scholar
  14. Heckhausen, H. (1963). Hoffnung und Furcht in der Leistungsmotivation [Hope and fear components of achievement motivation]. Meisenheim am Glam: Anton Hain.Google Scholar
  15. Jackson, D. (1984). Personality research form manual. Port Huron, MI: Research Psychologists Press.Google Scholar
  16. Kuhl, J., & Scheffer, D. (1999). Der operante multi-motiv-test (OMT): Manual [The operant multi-motive-test (OMT): Manual]. Osnabrück: University of Osnabrück.Google Scholar
  17. Lang, J. W. B. (2014). A dynamic Thurstonian item response theory of motive expression in the picture story exercise: Solving the internal consistency paradox of the PSE. Psychological Review, 121(3), 481–500. Scholar
  18. Lang, J. W. B., & Fries, S. (2006). A revised 10-item version of the Achievement Motives Scale. Psychometric properties in German-speaking samples. [Eine revidierte 10-Item-Version der Achievement Motives Scale (Skala zu Leistungsmotiven)]. European Journal of Psychological Assessment, 22(3), 216–224. Scholar
  19. Langens, T. A., & Schmalt, H.-D. (2002). Emotional consequences of positive daydreaming: The moderating role of fear of failure. Personality and Social Psychology Bulletin, 28(12), 1725–1735. Scholar
  20. Langens, T. A., & Schmalt, H.-D. (2008). Motivational traits: New directions and measuring motives with the multi-motive grid (MMG). In G. J. Boyle, G. Matthews, D. H. Saklofske, G. J. Boyle, G. Matthews & D. H. Saklofske (Eds.), The SAGE handbook of personality theory and assessment, Vol 1: Personality theories and models (pp. 523–544). Thousand Oaks, CA: Sage Publications, Inc.CrossRefGoogle Scholar
  21. Leite, F. P., & Ratcliff, R. (2011). What cognitive processes drive response biases? A diffusion model analysis. Judgment and Decision Making, 6(7), 651–687.Google Scholar
  22. Lerche, V., & Voss, A. (2016). Model complexity in diffusion modeling: Benefits of making the model more parsimonious. Frontiers in Psychology, 7, 1324. Scholar
  23. Lerche, V., & Voss, A. (2017a). Experimental validation of the diffusion model based on a slow response time paradigm. Psychological Research. Scholar
  24. Lerche, V., & Voss, A. (2017b). Retest reliability of the parameters of the Ratcliff diffusion model. Psychological Research, 81(3), 629–652. Scholar
  25. Lerche, V., Voss, A., & Nagler, M. (2017). How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria. Behavior Research Methods, 49(2), 513–537. Scholar
  26. Lowell, E. L. (1952). The effect of need for achievement on learning and speed of performance. The Journal of Psychology: Interdisciplinary and Applied, 33, 31–40. Scholar
  27. McClelland, D. C. (1985). Human motivation. Glenview, IL: Scott, Foresman.Google Scholar
  28. McClelland, D. C., Atkinson, J. W., Clark, R. A., & Lowell, E. L. (1953). The achievement motive. East Norwalk, CT: Appleton-Century-Crofts.CrossRefGoogle Scholar
  29. McClelland, D. C., Koestner, R., & Weinberger, J. (1989). How do self-attributed and implicit motives differ? Psychological Review, 96(4), 690–702. Scholar
  30. McKoon, G., & Ratcliff, R. (2012). Aging and IQ effects on associative recognition and priming in item recognition. Journal of Memory and Language, 66(3), 416–437. Scholar
  31. Metzger, R. L., Miller, M. L., Cohen, M., Sofka, M., & Borkovec, T. D. (1990). Worry changes decision making: The effect of negative thoughts on cognitive processing. Journal of Clinical Psychology, 46(1), 78–88.;2-R.CrossRefPubMedGoogle Scholar
  32. Neubauer, A. B., Lerche, V., & Voss, A. (2017). Inter-individual differences in the intra-individual association of competence and well-being: Combining experimental and intensive longitudinal designs. Journal of Personality. Scholar
  33. Pang, J. S. (2010). The achievement motive: A review of theory and assessment of n achievement, hope of success, and fear of failure. In O. C. Schultheiss & J. C. Brunstein (Eds.), Implicit motives (pp. 30–70). New York, NY: Oxford University Press.CrossRefGoogle Scholar
  34. Pang, J. S. (2006). A revised content-coding measure for hope of success and fear of failure. Unpublished dissertation, University of Michigan, Ann Arbor, MI.Google Scholar
  35. Pang, J. S., Villacorta, M. A., Chin, Y. S., & Morrison, F. J. (2009). Achievement motivation in the social context: Implicit and explicit hope of success and fear of failure predict memory for and liking of successful and unsuccessful peers. Journal of Research in Personality, 43(6), 1040–1052. Scholar
  36. Puca, R. M., & Schmalt, H.-D. (1999). Task enjoyment: A mediator between achievement motives and performance. Motivation and Emotion, 23(1), 15–29. Scholar
  37. Ramsay, J. E., & Pang, J. S. (2013). Set ambiguity: A key determinant of reliability and validity in the picture story exercise. Motivation and Emotion, 37(4), 661–674. Scholar
  38. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. Scholar
  39. Ratcliff, R. (2014). Measuring psychometric functions with the diffusion model. Journal of Experimental Psychology: Human Perception and Performance, 40(2), 870–888. Scholar
  40. Ratcliff, R., Gomez, P., & McKoon, G. (2004). A diffusion model account of the lexical decision task. Psychological Review, 111(1), 159–182. Scholar
  41. Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356. Scholar
  42. Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Sciences, 20(4), 260–281. Scholar
  43. Ratcliff, R., Thapar, A., & McKoon, G. (2010). Individual differences, aging, and IQ in two-choice tasks. Cognitive Psychology, 60(3), 127–157. Scholar
  44. Ratcliff, R., & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin & Review, 9(3), 438–481. Scholar
  45. Reuman, D. A. (1982). Ipsative behavioral variability and the quality of thematic apperceptive measurement of the achievement motive. Journal of Personality and Social Psychology, 43(5), 1098–1110. Scholar
  46. Runge, J. M., Lang, J. W. B., Engeser, S., Schüler, J., den Hartog, S. C., & Zettler, I. (2016). Modeling motive activation in the operant motive test: A psychometric analysis using dynamic Thurstonian item response theory. Motivation Science, 2(4), 268–286. Scholar
  47. Schmalt, H.-D. (1976). Die Messung des Leistungsmotivs. Göttingen [u.a.]: Verl. f. Psychologie.Google Scholar
  48. Schmalt, H.-D. (1982). Two concepts of fear of failure motivation. In R. Schwarzer, H. M. van der Ploeg & C. D. Spielberger (Eds.), Advances in test anxiety research (Vol I, pp. 45–52). Lisse, NJ: Swets & Zeitlinger.Google Scholar
  49. Schmalt, H.-D. (1999). Assessing the achievement motive using the grid technique. Journal of Research in Personality, 33(2), 109–130. Scholar
  50. Schmalt, H.-D. (2005). Validity of a short form of the achievement-motive grid (AMG-S): Evidence for the three-factor structure emphasizing active and passive forms of fear of failure. Journal of Personality Assessment, 84(2), 172–184. Scholar
  51. Schmalt, H.-D., Sokolowski, K., & Langens, T. (2000). Das Multi-Motiv-Gitter für Anschluß, Leistung und Macht (MMG). Frankfurt: Swets Test ServicesGoogle Scholar
  52. Schmalt, H.-D., Sokolowski, K., & Langens, T. A. (2010). Das Multi-Motiv-Gitter für Anschluß, Leistung und Macht (MMG): Manual [The Multi-Motive Grid for affiliation, achievement, and power] (2nd ed.). Frankfurt: Pearson.Google Scholar
  53. Schmiedek, F., Oberauer, K., Wilhelm, O., Süß, H.-M., & Wittmann, W. W. (2007). Individual differences in components of reaction time distributions and their relations to working memory and intelligence. Journal of Experimental Psychology—General, 136(3), 414–429. Scholar
  54. Schubert, A.-L., Hagemann, D., Voss, A., Schankin, A., & Bergmann, K. (2015). Decomposing the relationship between mental speed and mental abilities. Intelligence, 51, 28–46. Scholar
  55. Schüler, J., Brandstätter, V., Wegner, M., & Baumann, N. (2015). Testing the convergent and discriminant validity of three implicit motive measures: PSE, OMT, and MMG. Motivation and Emotion, 39(6), 839–857. Scholar
  56. Schultheiss, O. C., & Brunstein, J. C. (2005). An implicit motive perspective on competence. In A. J. Elliot, C. S. Dweck, A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 31–51). New York, NY: Guilford Publications.Google Scholar
  57. Schultheiss, O. C., Liening, S. H., & Schad, D. (2008). The reliability of a picture story exercise measure of implicit motives: Estimates of internal consistency, retest reliability, and ipsative stability. Journal of Research in Personality, 42(6), 1560–1571. Scholar
  58. Schultheiss, O. C., & Pang, J. S. (2007). Measuring implicit motives. In R. W. Robins, R. C. Fraley, R. F. Krueger, R. W. Robins, R. C. Fraley & R. F. Krueger (Eds.), Handbook of research methods in personality psychology (pp. 322–344). New York, NY: Guilford Press.Google Scholar
  59. Sokolowski, K., Schmalt, H.-D., Langens, T. A., & Puca, R. M. (2000). Assessing achievement, affiliation, and power motives all at once: The multi-motive grid (MMG). Journal of Personality Assessment, 74(1), 126–145. Scholar
  60. Spangler, W. D. (1992). Validity of questionnaire and TAT measures of need for achievement: Two meta-analyses. Psychological Bulletin, 112(1), 140–154. Scholar
  61. Thrash, T. M., & Elliot, A. J. (2002). Implicit and self-attributed achievement motives: Concordance and predictive validity. Journal of Personality, 70(5), 729–755. Scholar
  62. Tukey, J. W. (1977). Exploratory data analysis. Reading: Addison-Wesley.Google Scholar
  63. van Ravenzwaaij, D., Donkin, C., & Vandekerckhove, J. (2017). The EZ diffusion model provides a powerful test of simple empirical effects. Psychonomic Bulletin & Review, 24(2), 547–556. Scholar
  64. van Ravenzwaaij, D., Dutilh, G., & Wagenmakers, E.-J. (2012). A diffusion model decomposition of the effects of alcohol on perceptual decision making. Psychopharmacology (Berl), 219(4), 1017–1025. Scholar
  65. Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion models in experimental psychology: A practical introduction. Experimental Psychology, 60(6), 385–402. Scholar
  66. Voss, A., Rothermund, K., & Brandtstädter, J. (2008). Interpreting ambiguous stimuli: Separating perceptual and judgmental biases. Journal of Experimental Social Psychology, 44(4), 1048–1056. Scholar
  67. Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: An empirical validation. Memory & Cognition, 32(7), 1206–1220. Scholar
  68. Voss, A., & Schwieren, C. (2015). The dynamics of motivated perception: Effects of control and status on the perception of ambivalent stimuli. Cognition and Emotion, 29(8), 1411–1423. Scholar
  69. Voss, A., & Voss, J. (2007). Fast-dm: A free program for efficient diffusion model analysis. Behavior Research Methods, 39(4), 767–775. Scholar
  70. Voss, A., & Voss, J. (2008). A fast numerical algorithm for the estimation of diffusion model parameters. Journal of Mathematical Psychology, 52(1), 1–9. Scholar
  71. Voss, A., Voss, J., & Lerche, V. (2015). Assessing cognitive processes with diffusion model analyses: A tutorial based on fast-dm-30. Frontiers in Psychology. Scholar
  72. Wagenmakers, E.-J. (2009). Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy. European Journal of Cognitive Psychology, 21(5), 641–671. Scholar
  73. Wagenmakers, E.-J., Ratcliff, R., Gomez, P., & McKoon, G. (2008). A diffusion model account of criterion shifts in the lexical decision task. Journal of Memory and Language, 58(1), 140–159. Scholar
  74. Weiss, P., Wertheimer, M., & Groesbeck, B. (1959). Achievement motivation, academic aptitude, and college grades. Educational and Psychological Measurement, 19, 663–666. Scholar
  75. Winter, D. G. (1991). Manual for scoring motive imagery in running text. Ann Arbor: University of Michigan, Unpublished instrumentGoogle Scholar
  76. Winter, D. G. (1994). Manual for scoring motive imagery in running text. Ann Arbor: University of Michigan, Unpublished instrumentGoogle Scholar
  77. Yang, Y., Cao, S., Shields, G. S., Teng, Z., & Liu, Y. (2016). The relationships between rumination and core executive functions: A meta-analysis. Depression and Anxiety. Scholar
  78. Yap, M. J., Balota, D. A., Sibley, D. E., & Ratcliff, R. (2012). Individual differences in visual word recognition: Insights from the English Lexicon Project. Journal of Experimental Psychology: Human Perception and Performance, 38(1), 53–79. Scholar

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Authors and Affiliations

  1. 1.Ruprecht-Karls-Universität HeidelbergHeidelbergGermany
  2. 2.German Institute for International Educational Research (DIPF)Frankfurt am MainGermany
  3. 3.Center for Research on Individual Development and Adaptive Education of Children at Risk (IDeA)Frankfurt am MainGermany

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