Metacognitive expectancy effects in source monitoring: Beliefs, in-the-moment experiences, or both?

Schaper, Marie Luisa; Kuhlmann, Beatrice G.; Bayen, Ute J.

doi:10.1016/j.jml.2019.03.009

Cited by 17 publications

(120 citation statements)

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“…The Belief ij is the belief-based prediction of memory performance for each trial. When beliefs are measured with global predictions, each participant only gives one belief-based prediction of memory performance for each level of the cue (Hu et al, 2015;Frank and Kuhlmann, 2017;Su et al, 2018;Schaper et al, 2019). Thus, for each participant, all of the items within the same level of the cue have the same value of belief-based prediction.…”

Section: The Multilevel Mediation Modelmentioning

confidence: 99%

“…Here we fix the slope a j as the same value for all participants [i.e., a in Equation 3] and does not add the random slope. This is because, in many previous studies (and in the hypothetical dataset), the predictor variable Cue only contains two levels (e.g., large and small font size) and the mediator variable Belief is the same within each level of the cue for a participant (Hu et al, 2015;Frank and Kuhlmann, 2017;Su et al, 2018;Schaper et al, 2019). In this case, the random effect for slope a j is perfectly confounded with the residual error, which can make the model fail to converge (Singmann and Kellen, 2020) (see section "Further Explanation About the Random Slope of Path a When We Regress Beliefs on the Cue in Multilevel Mediation Model" in the Supplementary Material for further explanation on this topic).…”

Section: The Multilevel Mediation Modelmentioning

confidence: 99%

“…Then we show examples of how to use this multilevel mediation model to investigate the relationship between beliefs and JOLs in hypothetical and empirical datasets, and how to quantitatively compare the effect of beliefs and fluency on JOLs in one model. Next, we discuss the similarities and differences between the current multilevel mediation model and the multilevel moderation model used in previous studies (Frank and Kuhlmann, 2017;Schaper et al, 2019). Finally, we show the inflation of Type I error in the multilevel mediation model when global belief-based judgments FIGURE 1 | Schematic illustration of the mediation model for quantifying the contribution of beliefs to the cue effect on JOLs.…”

Section: Introductionmentioning

confidence: 98%

“…To further examine the effect of beliefs on JOLs within the same participants, some previous studies have asked one group of participants to first make global belief-based predictions about their memory and then to provide JOLs to study items (Ariel et al, 2014;Hu et al, 2015;Frank and Kuhlmann, 2017;Su et al, 2018;Schaper et al, 2019). In these studies, participants predict how many items they will recall in a future test before they start learning the items.…”

Section: Introductionmentioning

confidence: 99%

“…To quantify the contribution of processing fluency to JOLs at the item level, previous studies often employed a multilevel linear model to delineate the relationship between fluency and JOLs (Undorf and Erdfelder, 2015;Undorf et al, 2017;Yang et al, 2018). Thus, it should be better to also explore the role of beliefs in the JOL process at the item level if we want to compare the contribution of beliefs and processing fluency to JOLs (Frank and Kuhlmann, 2017;Su et al, 2018;Schaper et al, 2019). Researchers should use appropriate statistical methods to examine to what extent cues affect item-by-item JOLs through global beliefs about memory performance, and quantitatively compare the effect of beliefs and fluency on JOLs at the item level.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning

Zheng

et al. 2020

Front. Psychol.

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Recent studies on judgments of learning (JOLs) suggest that beliefs play an important role in the formation of JOLs. The current article introduces a multilevel mediation model to quantify the contribution of beliefs to JOL formation when both JOLs and global belief-based predictions are measured from the same group of participants. Our examples of fitting the multilevel mediation model to hypothetical and published datasets revealed that it is feasible to use the multilevel mediation model to examine the mediation effect of beliefs on the relationship between a cue and JOLs, and quantitatively compare the effects of beliefs and processing fluency on JOLs in one model. Then we compared the current multilevel mediation model and the multilevel moderation model implemented in previous studies, and discussed their similarities and differences. Finally, a data simulation was performed to explain the inflation of Type I error for the multilevel mediation model when we regress global belief-based predictions on the cue, and suggestions about appropriate steps for conducting multilevel mediation analysis are proposed.

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Section: The Multilevel Mediation Modelmentioning

confidence: 99%

Section: The Multilevel Mediation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning

Zheng

et al. 2020

Front. Psychol.

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Remedying the Metamemory Expectancy Illusion in Source Monitoring: Are there Effects on Restudy Choices and Source Memory?

Schaper

Bayen

Hey

2022

Metacognition Learning

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Metamemory monitoring, study behavior, and memory are presumably causally connected. When people misjudge their memory, their study behavior should be biased accordingly. Remedying metamemory illusions should debias study behavior and improve memory. One metamemory illusion concerns source memory, a critical aspect of episodic memory. People predict better source memory for items that originated from an expected source (e.g., toothbrush in a bathroom) rather than an unexpected source (e.g., shampoo in a kitchen), whereas actual source memory shows the opposite: an inconsistency effect. This expectancy illusion biases restudy choices: Participants restudy more unexpected than expected source–item pairs. The authors tested the causal relationships between metamemory and source memory with a delay and a source-retrieval attempt between study and metamemory judgment to remedy the expectancy illusion and debias restudy choices. Debiased restudy choices should enhance source memory for expected items, thereby reducing the inconsistency effect. Two groups studied expected and unexpected source–item pairs. They made metamemory judgments and restudy choices immediately at study or after delay, restudied the selected pairs, and completed a source-monitoring test. After immediate judgments, participants predicted better source memory for expected pairs and selected more unexpected pairs for restudy. After delayed judgments, participants predicted a null effect of expectancy on source memory and selected equal numbers of expected and unexpected pairs. Thus, the expectancy illusion was partially remedied and restudy choices were debiased. Nevertheless, source memory was only weakly affected. The results challenge the presumed causal relationships between metamemory monitoring, study behavior, and source memory.

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Metacognitive effects of instructional visuals: the role of cue use and judgment type

Jaeger,

Fiorella

2023

Metacognition Learning

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Prior research suggests most students do not glean valid cues from provided visuals, resulting in reduced metacomprehension accuracy. Across 4 experiments, we explored how the presence of instructional visuals affects students’ metacomprehension accuracy and cue-use for different types of metacognitive judgments. Undergraduates read texts on biology (Study 1a and b) or chemistry (Study 2 and 3) topics, made various judgments (test, explain, and draw) for each text, and completed comprehension tests. Students were randomly assigned to receive only texts (text-only condition) or texts with instructional visualizations (text-and-image condition). In Studies 1b, 2 and 3, students also reported the cues they used to make each judgment. Across the set of studies, instructional visualizations harmed relative metacomprehension accuracy. In Studies 1a and 2, this was especially the case when students were asked to judge how well they felt they could draw the processes described in the text. But in Study 3, this was especially the case when students were asked to judge how well they would do on a set of comprehension tests. In Studies 2 and 3, students who reported basing their judgments on representation-based cues demonstrated more accurate relative accuracy than students who reported using heuristic based cues. Further, across these studies, students reported using visual cues to make their draw judgments, but not their test or explain judgments. Taken together, these results indicate that instructional visualizations can hinder metacognitive judgment accuracy, particularly by influencing the types of cues students use to make judgments of their ability to draw key concepts.

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Metacognitive expectancy effects in source monitoring: Beliefs, in-the-moment experiences, or both?

Cited by 17 publications

References 52 publications

Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning

Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning

Remedying the Metamemory Expectancy Illusion in Source Monitoring: Are there Effects on Restudy Choices and Source Memory?

Metacognitive effects of instructional visuals: the role of cue use and judgment type

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