Author(s) Moritz Ingendahl (Ruhr-Universität Bochum) - moritz.ingendahl@rub.de Franziska Schäfer (University of Mannheim) - franziska.schaefer@uni-mannheim.de
Pre-registered on 02/19/2024 12:03 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? We investigate the role of metamemory in evaluative conditioning (EC).
We expect that pairings with positive/neutral/negative USs lead to corresponding evaluations of CSs, such that positive > neutral > negative.
We expect that the previous effect is attenuated under low CS-US fit conditions.
We expect higher JOLs under high CS-US fit conditions.
We expect higher fit judgments under high CS-US fit conditions.
We expect that higher JOLs are associated with stronger EC effects.
We expect that this latter effect statistically mediates the attenuation described in the second hypothesis.
3) Describe the key dependent variable(s) specifying how they will be measured. We assess JOLs with the question: "How likely is it that you will remember this word if you are presented with this alien?", and a scale from 0 -100% for each of the 6 CS.
We assess processing ease with the question: "How well does this word fit to this alien?", and a scale from 0 to 100 representing a scale from "not at all" to "very much " for each of the 6 CS.
We assess CS Evaluations and CS-US memory with the same instructions and tasks as Ingendahl and Vogel (2023), except that our USs are words and the CSs aliens.
4) How many and which conditions will participants be assigned to? US valence (positive/neutral/negative, within-subjects) and CS-US fit (high vs. low, within-subjects). CS-US fit is operationalized by using either adjectives or nouns as USs. There is one CS-US pair per combination.
We counterbalance the order of the JOL and the CS-US fit between participants.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We will analyze each dependent variable with a multilevel regression in lme4, using the highest converging random effect structure. For CS-US memory, we will use a binomial multilevel regression. In each model, we will use two dummy variables (positive + negative valence, baseline = neutral) as predictors. Further, we will code CS-US fit with 1 (high) and low (-1) and have all interactions in the model, leading to five predictors.
For CS evaluations, we expect a positive effect of positive valence and a negative effect of negative valence, as well as two significant interaction terms such that the effects of positive and negative valence are more pronounced for higher CS-US fit.
For JOLs, we expect a main effect of CS-US fit.
For fit judgments, we expect a main effect of CS-US fit.
For CS-US memory, we do not expect any effects. Actually, we deem it likely that we get a ceiling effect here, with almost perfect memory performance.
Next, we will compute within-subjects gamma correlations between JOLs and CS-US memory. We will also compute a correlation between JOLs and fit judgments both on a person- and on a within-person level.
Next, we will do one multilevel model where we add the z-standardized (within-person-centered) JOLs as a moderator to the model predicting CS evaluations, without CS-US fit. The model will have a main effect of within-person JOL, the two dummies, and two interaction terms. We expect two significant interaction terms (positive for positive, negative for negative interaction term).
Next, we will do a multilevel moderated mediation analysis in the bruceR package. There, we will code valence only with -1, 0, and 1. We will compute indirect effects, such that the valence*CS-US fit interaction is mediated by the valence x JOL interaction.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We do not plan any exclusions.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We will collect 200 finished interviews.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) Nothing else to pre-register.
