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More evidence against the Spinozan model: Cognitive load diminishes memory for “true” feedback

  • Lena NadarevicEmail author
  • Edgar Erdfelder
Article
  • 44 Downloads

Abstract

We tested two competing models on the memory representation of truth-value information: the Spinozan model and the Cartesian model. Both models assume that truth-value information is represented with memory “tags,” but the models differ in their coding scheme. According to the Cartesian model, true information is stored with a “true” tag, and false information is stored with a “false” tag. In contrast, the Spinozan model proposes that only false information receives “false” tags. All other (i.e., untagged) information is considered as true by default. Hence, in case of cognitive load during feedback encoding, the latter model predicts a load effect on memory for “false” feedback, but not on memory for “true” feedback. To test this prediction, participants studied trivia statements (Experiment 1) or nonsense statements that allegedly represented foreign-language translations (Experiment 2). After each statement, participants received feedback on the (alleged) truth value of the statement. Importantly, half of the participants experienced cognitive load during feedback processing. For the trivia statements of Experiment 1, we observed a load effect on memory for both “false” and “true” feedback. In contrast, for the nonsense statements of Experiment 2, we found a load effect on memory for “true” feedback only. Both findings clearly contradict the Spinozan model. However, our results are also only partially in line with the predictions of the Cartesian model. For this reason, we suggest a more flexible model that allows for an optional and context-dependent encoding of “true” tags and “false” tags.

Keywords

Truth bias Spinoza Descartes Feedback memory Multinomial model 

Notes

Acknowledgments

This work was supported by a University of Mannheim autonomy grant. We thank Johanna Heckeroth, Denise Meyer, Jana Ritschel, and Vera Vogel for collecting data of Experiment 1. Moreover, we thank Vanessa Gottschall for her dedicated help with setting up and collecting data for Experiment 2.

Open practices statement

All materials and data sets for the two experiments are available via the Open Science Framework (OSF): https://osf.io/ac4rt/

References

  1. Bayen, U. J., Murnane, K., & Erdfelder, E. (1996). Source discrimination, item detection, and multinomial models of source monitoring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 197–215.  https://doi.org/10.1037/0278-7393.22.1.197 Google Scholar
  2. Bell, R., Buchner, A., & Musch, J. (2010). Enhanced old–new recognition and source memory for faces of cooperators and defectors in a social-dilemma game. Cognition, 117, 261–275.  https://doi.org/10.1016/j.cognition.2010.08.020 CrossRefGoogle Scholar
  3. Bennett, J. (1984). A study of Spinoza’s ethics. Indianapolis, IN: Hackett.Google Scholar
  4. Bröder, A., & Meiser, T. (2007). Measuring source memory. Zeitschrift fur Psychologie/Journal of Psychology, 215, 52–60.  https://doi.org/10.1027/0044-3409.215.1.52 CrossRefGoogle Scholar
  5. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Hillsdale, NJ: Erlbaum.Google Scholar
  6. Descartes, R. (1985). Principles of philosophy. In J. Cottingham, R. Stoothoff, & D. Murdoch (Eds.), The philosophical writings of Descartes (Vol. 1, pp. 177–291). Cambridge, UK: Cambridge University Press. (Original work published 1644)CrossRefGoogle Scholar
  7. Erdfelder, E., Auer, T.-S., Hilbig, B. E., Aßfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift für Psychologie / Journal of Psychology, 217, 108–124.  https://doi.org/10.1027/0044-3409.217.3.108 CrossRefGoogle Scholar
  8. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160.  https://doi.org/10.3758/BRM.41.4.1149 CrossRefGoogle Scholar
  9. Gilbert, D. T., Krull, D. S., & Malone, P. S. (1990). Unbelieving the unbelievable: Some problems in the rejection of false information. Journal of Personality and Social Psychology, 59, 601–613.  https://doi.org/10.1037/0022-3514.59.4.601 CrossRefGoogle Scholar
  10. Gilbert, D. T., Tafarodi, R. W., & Malone, P. S. (1993). You can’t not believe everything you read. Journal of Personality and Social Psychology, 65, 221–233.  https://doi.org/10.1037/0022-3514.65.2.221 CrossRefGoogle Scholar
  11. Glanzer, M., Adams, J. K., Iverson, G. J., & Kim, K. (1993). The regularities of recognition memory. Psychological Review, 100, 546–567.  https://doi.org/10.1037/0033-295X.100.3.546 CrossRefGoogle Scholar
  12. Glenberg, A. M., Robertson, D. A., Jansen, J. L., & Johnson-Glenberg, M. C. (1999). Not propositions. Journal of Cognitive Systems Research, 1, 19–33.  https://doi.org/10.1016/S1389-0417(99)00004-2 CrossRefGoogle Scholar
  13. Grice, H. P. (1989). Logic and conversation. In H. P. Grice (Ed.), Studies in the way of words (pp. 22–40). Cambridge, MA: Harvard University Press.Google Scholar
  14. Hasson, U., Simmons, J. P., & Todorov, A. (2005). Believe it or not: On the possibility of suspending belief. Psychological Science, 16, 566–571.  https://doi.org/10.1111/j.0956-7976.2005.01576.x CrossRefGoogle Scholar
  15. Hu, X., & Batchelder, W. H. (1994). The statistical analysis of general processing tree models with the EM algorithm. Psychometrika, 59, 21–47.  https://doi.org/10.1007/BF02294263 CrossRefGoogle Scholar
  16. Hunt, E. (2017). ‘Disputed by multiple fact-checkers’: Facebook rolls out new alert to combat fake news. The Guardian. Retrieved from https://www.theguardian.com/technology/2017/mar/22/facebook-fact-checking-tool-fake-news.
  17. Isberner, M.-B., & Richter, T. (2013). Does validation during language comprehension depend on an evaluative mindset? Discourse Processes, 51, 7–25.  https://doi.org/10.1080/0163853X.2013.855867 CrossRefGoogle Scholar
  18. Klauer, K. C., & Wegener, I. (1998). Unraveling social categorization in the “Who said what?” paradigm. Journal of Personality and Social Psychology, 75, 1155–1178.  https://doi.org/10.1037/0022-3514.75.5.1155 CrossRefGoogle Scholar
  19. Moshagen, M. (2010). multiTree: A computer program for the analysis of multinomial processing tree models. Behavior Research Methods, 42, 42–54.  https://doi.org/10.3758/BRM.42.1.42 CrossRefGoogle Scholar
  20. Murnane, K. B., & Bayen, U. J. (1996). An evaluation of empirical measures of source identification. Memory & Cognition, 24, 417–428.  https://doi.org/10.3758/BF03200931 CrossRefGoogle Scholar
  21. Nadarevic, L., & Erdfelder, E. (2013). Spinoza’s error: Memory for truth and falsity. Memory & Cognition, 41, 176–186.  https://doi.org/10.3758/s13421-012-0251-z CrossRefGoogle Scholar
  22. Pantazi, M., Kissine, M., & Klein, O. (2018). The power of the truth bias: False information affects memory and judgment even in the absence of distraction. Social Cognition, 36, 167–198.  https://doi.org/10.1521/soco.2018.36.2.167 CrossRefGoogle Scholar
  23. Piest, B. A., Isberner, M.-B., & Richter, T. (2018). Don’t believe everything you hear: Routine validation of audiovisual information in children and adults. Memory & Cognition, 46, 849–863.  https://doi.org/10.3758/s13421-018-0807-7 CrossRefGoogle Scholar
  24. Read, T. R. C., & Cressie, N. A. C. (1988). Goodness-of-fit statistics for discrete multivariate data. New York, NY: Springer-Verlag.CrossRefGoogle Scholar
  25. Richter, T., Schroeder, S., & Wöhrmann, B. (2009). You don’t have to believe everything you read: Background knowledge permits fast and efficient validation of information. Journal of Personality and Social Psychology, 96, 538–558.  https://doi.org/10.1037/a0014038 CrossRefGoogle Scholar
  26. Riefer, D. M., Hu, X., & Batchelder, W. H. (1994). Response strategies in source monitoring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 680–693.  https://doi.org/10.1037/0278-7393.20.3.680 Google Scholar
  27. Skurnik, I. W. (1998). Metacognition and the illusion of truth (Unpublished doctoral dissertation). Princeton University, Princeton, NJ.Google Scholar
  28. Spinoza, B. (2006). The ethics. Middlesex, UK: Echo Library (Original work published 1677).Google Scholar
  29. Street, C. N. H., Bischof, W. F., Vadillo, M. A., & Kingstone, A. (2016). Inferring others’ hidden thoughts: Smart guesses in a low diagnostic world. Journal of Behavioral Decision Making, 29, 539–549.  https://doi.org/10.1002/bdm.1904 CrossRefGoogle Scholar
  30. Street, C. N. H., & Kingstone, A. (2016). Aligning Spinoza with Descartes: An informed Cartesian account of the truth bias. British Journal of Psychology, 108, 453–466.  https://doi.org/10.1111/bjop.12210 CrossRefGoogle Scholar
  31. Street, C. N. H., & Richardson, D. C. (2014). Lies, damn lies, and expectations: How base rates inform lie–truth judgments. Applied Cognitive Psychology, 29, 149–155.  https://doi.org/10.1002/acp.3085 CrossRefGoogle Scholar
  32. Unkelbach, C., & Rom, S. C. (2017). A referential theory of the repetition-induced truth effect. Cognition, 160, 110–126.  https://doi.org/10.1016/j.cognition.2016.12.016 CrossRefGoogle Scholar
  33. Vogt, V., & Bröder, A. (2007). Independent retrieval of source dimensions: An extension of results by Starns and Hicks (2005) and a comment on the ACSIM measure. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 443–450.  https://doi.org/10.1037/0278-7393.33.2.443 Google Scholar
  34. Wiswede, D., Koranyi, N., Müller, F., Langner, O., & Rothermund, K. (2013). Validating the truth of propositions: Behavioral and ERP indicators of truth evaluation processes. Social Cognitive and Affective Neuroscience, 8, 647–653.  https://doi.org/10.1093/scan/nss042 CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of Psychology, School of Social SciencesUniversity of MannheimMannheimGermany

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