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Re-analysis of picture-based cognitive reappraisal experiments: Power simulations and analyses of trial-level factors

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Abstract

Cognitive reappraisal is widely recognized as an effective emotion regulation strategy. In laboratory research, negative ratings from image-based reappraisal tasks are typically aggregated within trial condition, and the average difference in reported negative affect between conditions serves as a measure of reappraisal success. We aggregated and re-analyzed trial-level data from 27 picture-based reappraisal experiments to which we had access in order to examine the robustness of the reappraisal effect and estimate the power to detect this effect within different sample sizes. Then, we leveraged the database to conduct novel tests of potential trial-level factors (time and previous trial type) that may impact reappraisal success. These allowed us to test among competing hypotheses about how negative affect may change based on these factors: habituation, sensitization, practice, and fatigue. We observed a small but significant linear decrease in negative emotion over time in look and regulation conditions (habituation). We also observed a small but significant interaction between previous trial and current trial, such that seeing a negative image on a previous trial resulted in slightly higher ratings of negative emotion, but this effect was relatively diminished if the previous trial also involved reappraisal. The database is an open, novel resource for those designing and conducting picture-based reappraisal tasks.

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Notes

  1. We use the word “Change” here to refer to any reappraisal trial condition, however experiments may vary on the specific instruction word used for these trials (see Table 1).

  2. It should be noted that despite numerous experiments, the reproducibility of the data supporting ego depletion models remains contested (Friese et al., 2019).

  3. Demographic data were not captured or retained for a portion of the participants included in the database. Depending upon the study and when it was conducted, the demographics questionnaire may have asked for the participant’s gender, sex assigned at birth, or both. Non-binary and “other” options were unfortunately included only in some studies. All said, gender data was available for 83% of participants in the sample and age data for 75% of participants.

  4. The specific instruction words (“Look”, “Change” etc.) varied across studies but conveyed essentially the same meaning (See Table 1).

  5. This study employed a between-subjects design in which each participant only executed one type of trial instruction. Likewise, a second study did not include a Look Neutral baseline condition. Data from these studies were not included in the trial-level analyses below.

  6. The small number of DAPS images were normed on a 5-point scale but were converted to the same scale as the other images using a linear transformation.

  7. Outputs of the ANOVAs were also validated using SPSS.

  8. There were 12 bins of trial sizes and 28 sample sizes in total. Trial bins increased by 1 from 1:4 and in steps of 4 from 4:36. Sample sizes increased in steps of 5 from 5:20 (i.e., 5, 10, 15, 20 in steps of 20 from 20:500 (e.g., 20, 40… 500).

  9. The transformation matches the size of the scaling but not the range of the two scales as the two poles of the normative valence scale represent positive and negative affect respectively, with neutral being approximately in the middle. By contrast, the Negative Affect outcome variable does not contain values for positive valence.

  10. Notably, we opted not to combine this analysis with the repeated measures ANOVA described in the Main Effect of Trial Condition section above, even though both analyses test the main effect of current trial condition. By limiting this analysis to only participants with a sufficient number of qualifying trials in order to assess the interaction effect and conduct post hoc tests, the sample size was greatly reduced in the present analysis. As the aim of the previously described ANOVA is to test the robustness of the main effect of reappraisal it is more appropriate to run the first test on the largest available sample.

  11. The simulation also indicated highly significant main effects for current trial in all tests (all p’s < .0001).

  12. In our experience, the proportion of non-complaint participants tends to be quite small. However, the overall conscientiousness of participants can vary across sample populations. For example, we’ve seen higher levels of non-compliance as well as bots within MTurk workers compared with other online studies that utilize university students.

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Acknowledgements

We thank Allison Grossberg, Hannah Friedman, Emma Young, Alexander Assila, and Sasha Zabelski for their assistance in assembling data for this study. We would also like to thank James J. Gross and Kevin N. Ochsner for their patience, support, and guidance on many of the included studies, which were conducted while K.M. was a trainee under their mentorship.

Funding

This work was supported by NSF CAREER Award #1554683 to K.M. Component studies conducted at the University of Denver were supported by the National Science Foundation, the Templeton Foundation, and the National Institute for Mental Health as credited in previously published reports.

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Authors and Affiliations

  1. Department of Psychology, University of Denver, 2155 S. Race St., Denver, CO, 80210, USA

    Damon Abraham & Kateri McRae

  2. Department of Psychology, University of Arizona, Tucson, USA

    Eric S. Andrews

  3. Department of Psychology, University of Southern California, Los Angeles, USA

    Chelsey X. Pan

Authors
  1. Damon Abraham
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  2. Eric S. Andrews
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  3. Chelsey X. Pan
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  4. Kateri McRae
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Correspondence to Kateri McRae.

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Abraham, D., Andrews, E.S., Pan, C.X. et al. Re-analysis of picture-based cognitive reappraisal experiments: Power simulations and analyses of trial-level factors. Motiv Emot (2024). https://doi.org/10.1007/s11031-024-10064-3

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  • DOI: https://doi.org/10.1007/s11031-024-10064-3

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