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Utilization of covariation knowledge in source monitoring: no evidence for implicit processes

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Abstract

In three experiments, a “hidden covariation” (Lewicki, in Journal of Experimental Psychology: Learning, Memory, and Cognition, 12, 135–146, 1986) of nonsalient stimulus attributes and the source of stimulus information was established to test whether implicit knowledge about this correlation influences source memory judgments. The source monitoring framework (Johnson, Hashtroudi, and Lindsay, in Psychological Bulletin, 114, 3–28, 1993) postulates heuristic and strategic judgment processes in source attributions. A multinomial model analysis disentangled memory and guessing processes. While there were large strategic guessing biases involving explicit knowledge in all experiments, there was no evidence for the use of implicit covariation knowledge. Only participants who were later able to verbalize the covariation had shown corresponding biases during the source memory test, suggesting that implicit covariation knowledge plays no prominent role in the reconstruction processes in source monitoring.

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Notes

  1. There is a debate whether threshold models or signal detection models are more adequate to analyze source memory and recognition data (e.g., Banks, 2000; Hilford, Glanzer, Kim, and DeCarlo, 2002; Slotnick, Klein, Dodson, and Shimamura, 2000). While this question concerning the adequate decision model is of great theoretical importance, we presently prefer multinomial models for pragmatic reasons of measurement: First, they often approximate signal detection models well (Snodgrass and Corwin, 1988). Second, the models of Bayen et al. (1996) and Meiser and Bröder (2002) have been validated empirically. Third, they are conceptually simple and open for extensions.

  2. In the original model, the d 2 parameters are conditionalized on the d 1, allowing for stochastic dependence of the memory processes. In the models used here, stochastic independence is assumed.

  3. The pattern of results is identical when the D parameters for items presented twice are eqated to D N.

  4. According to a conventional significance level, the fit is not overwhelmingly good (P < 0.05). We argue, however, that the conventional level is not sensible in this case. We can conclude with high confidence (α = 0.001; 1 - β > 0.999) that there is no model deviation as large as or larger than w = 0.1 which is denoted a “small” effect by Cohen (1988). If the model is fitted to both experimental counterbalancing conditions separately, a misfit results in Condition 1 [G 2(15) = 34.19, P = 0.003], but not in Condition 2 [G 2(15) = 17.00, P = 0.30]. All conclusions remain the same if the statistical tests are only based on Condition 2, ignoring the potentially problematic data set from Condition 1 (Table 1). All statistical tests reported in the text are based on the combined data.

  5. The χ2 difference between restricted and unrestricted models is centrally χ2 distributed only if the null hypothesis for the unrestricted model holds, so the α probability of the parameter test may be distorted, probably resulting in a progressive test. However, the conclusions drawn are robust: guessing biases can also be tested by comparing the source judgment frequency distributions of false alarms on distractors (see Table 6). χ2 tests on these frequencies show converging results: no signs of bias in the control condition, χ2(1, N = 600) = 0.03, P = 0.86, or for unaware participants, χ2(1, N = 970) = 0.00, P > 0.999, but an extreme bias for participants with explicit rule knowledge, χ2(1, N = 242) = 96.57, P < 0.001.

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Acknowledgements

Parts of the research reported were supported by the Deutsche Forschungsgemeinschaft (DFG, grant Br 2130/2–2). We thank Hernan-Leonardo Aceval, Mareike Adams, Arvid Herwig, Florentin Klein, Michael Kondzior, Florian Schmitz, Lore Thaler, and Daniel Wessel for their help in preparing and conducting Experiment 1.

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Author notes

  1. Arndt Bröder

    Present address: Max-Planck-Institute for Research on Collective Goods, Kurt-Schumacher-Strasse 10, 53113, Bonn, Germany

  2. Julia Schütz

    Present address: Department of Psychology, University of Hamburg, Hamburg, Germany

  3. Patrick Bay

    Present address: “Sprungbrett e.V.” educational counseling in Bonn, Bonn, Germany

Authors and Affiliations

  1. Department of Psychology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    Arndt Bröder, Daniela Noethen, Julia Schütz & Patrick Bay

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  1. Arndt Bröder
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  2. Daniela Noethen
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Correspondence to Arndt Bröder.

Appendix

Appendix

Table 4 Response frequencies in Experiment 1 (covariation letter sets–location)
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Table 5 Response frequencies in Experiment 2 (covariation word valence–location)
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Table 6 Response frequencies in Experiment 3 (covariation background color—line of sight)
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Bröder, A., Noethen, D., Schütz, J. et al. Utilization of covariation knowledge in source monitoring: no evidence for implicit processes. Psychological Research 71, 524–538 (2007). https://doi.org/10.1007/s00426-006-0047-5

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