Forgiveness is a core feature of human psychology in which a person makes a deliberate decision to cease negative emotions or attitudes toward an offender who has done them harm. The concept of interpersonal forgiveness is deeply embedded across societies, but much remains to be understood about how it actually works. What are its key ingredients and why does it occur in the first place? Research in social psychology has demonstrated a range of personal and social benefits of forgiveness, giving rise to two dominant mechanistic accounts – one that positions empathy as the driving factor and another that centres motivated reasoning (Donovan & Priester, 2017).

 

In the current study, Chan and Feldman (2023) seek to replicate a formative study by McCullough et al (1997) that led to the so-called Empathy Model of forgiveness. According to this theory, forgiving is a motivational change facilitated (crucially) by empathy, promoting constructive over destructive behaviour toward the offender. Chan and Feldman will replicate Study 1 from McCullough et al., measuring the correlational relationship between apology, forgiving, and empathy for offenders, and exploring whether forgiving is associated with increased conciliation and decreased avoidance motivation. As well as closely replicating the original study, the authors will extend it to test the more severe hypothesis that empathy causally influences forgiveness. To achieve this, they will experimentally manipulate empathy by adding two groups to the design: one in which participants are asked to recall hurtful past experiences in which they were not empathetic to the offender, and another in which they were highly empathetic.

 

The Stage 1 manuscript was evaluated over two rounds of in-depth review. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).

 
URL to the preregistered Stage 1 protocol: https://osf.io/q78fs

 

Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA. 
 
List of eligible PCI RR-friendly journals:

 

 

1. Donovan, L. A. N., & Priester, J. R. (2017). Exploring the psychological processes underlying interpersonal forgiveness: The superiority of motivated reasoning over empathy. Journal of Experimental Social Psychology, 71, 16-30. https://doi.org/10.1016/j.jesp.2017.02.005

 

2. Chan, C. F. & Feldman, G. (2023). The link between Empathy and Forgiveness: Replication and extensions of McCullough et al. (1997)'s Study 1, in principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/q78fs

Words have served as stimuli in memory experiments for over a century. What makes some words stand out in memory compared to others? One plausible answer is that semantically rich words are more distinctive and therefore exhibit a mirror effect in recognition memory experiments where they are likely to be correctly endorsed and also less likely to be confused with other words (Glanzer & Adams, 1985). Semantic richness can arise due to extensive prior experience with the word in multiple contexts but can also arise due to sensorimotor grounding, i.e., direct perceptual and action-based experience with the concepts represented by the words (e.g. pillow, cuddle). However, previous experiments have revealed inconsistent recognition memory performance patterns for words based on different types of sensorimotor grounding (Dymarska et al., 2023). Most surprisingly, body-related words such as cuddle and fitness exhibited greater false alarm rates. 

In the current study, Dymarska and Connell (2023) propose to test two competing theories that can explain the increased confusability of body-related words: 1) the adaptive account - contextual elaboration-based strategies activate other concepts related to body and survival, increasing confusability; and 2) the attentional account - somatic attentional mechanisms automatically induce similar tactile and interoceptive experiences upon seeing body-related words leading to less distinctive memory traces. The adaptive account leads to different predictions under intentional and incidental memory conditions. Specifically, contextual elaboration strategies are unlikely to be employed when participants do not expect a memory test and therefore in an incidental memory task, body-related words should not lead to inflated false alarm rates (see Hintzman (2011) for a discussion on incidental memory tasks and the importance of how material is processed during memory tasks). However, the attentional account is not dependent on the task instructions or the knowledge about an upcoming memory test. 

Here, Dymarska and Connell (2023) have designed an incidental recognition memory experiment with over 5000 words, disguised as a lexical decision task using carefully matched pseudowords during the encoding phase. The sample size will be determined by using a sequential hypothesis testing plan with Bayes Factors. To test the predictions of the adaptive and attentional accounts, the authors derive a set of lexical and sensorimotor variables (including a body-component) after dimensionality reduction of a comprehensive set of lexical and semantic word features. The analysis will involve running both Bayesian and frequentist hierarchical linear regression to explain four different measures of recognition memory performance based on the key sensorimotor variables and other baseline/confounding variables. While this analysis plan enables a comparison with the earlier results from an expected memory test (Dymarska et al., 2023), the current study is self-contained in that it is possible to distinguish the adaptive and attentional accounts based on the effect of body component scores on hit rate and false alarm rate.

The study plan was refined across two rounds of review, with input from two external reviewers after which the recommender judged that the study satisfied the Stage 1 criteria for in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/ck5bg
 
Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA.
 
List of eligible PCI RR-friendly journals:
 

 

Dymarska, A. & Connell, L. (2023). Sensorimotor Effects in Surprise Word Memory – a Registered Report. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/ck5bg

Dymarska, A., Connell, L. & Banks, B. (2023). More is Not Necessarily Better: How Different Aspects of Sensorimotor Experience Affect Recognition Memory for Words. Journal of Experimental Psychology: Language, Memory, Cognition. Advance online publication. https://dx.doi.org/10.1037/xlm0001265 

Glanzer, M., & Adams, J. K. (1985). The mirror effect in recognition memory. Memory & cognition, 13, 8-20.

Hintzman, D. L. (2011). Research strategy in the study of memory: Fads, fallacies, and the search for the “coordinates of truth”. Perspectives on Psychological Science, 6(3), 253-271.

Everyday decisions can often be biased by how different options are presented and which choices were made previously, over and above whichever option is rationally preferable. Two such major biases in judgment and decision-masking are status quo bias and the default effect. The status quo bias reflects a tendency for people to choose an option that has already been implemented or represents the current state of affairs (for instance, choosing to remain with a current energy supplier rather than switching to a different one, even when the current supplier provides less value for money), while the default effect is a bias toward taking a course of action that would occur automatically in the absence of an active choice (such as the pre-selected energy supplier in questionnaire). Status quo bias and default effects can overlap or diverge: the status quo option will often be the same as the default, while on other occasions the default option in a choice set will be determined by other factors and differ from the option that was already implemented.

 

In the current study, Yam and Feldman (2023) propose a replication and extension of an influential study by Dinner et al. (2011) that purported to examine default effects but, arguably, actually studied its nearby cousin, status quo bias. Using a large online sample, the authors will independently test for the existence of both status quo bias and the default effect, before then asking whether (and if so how) status quo bias and default effects interact and how they are related to several potential explanatory variables, including perceived effort (which may be lower for the status quo or default) and endorsements (whether through direct recommendations or indirectly through perceived norms). The authors also build in additional manipulation checks and comprehension checks, in addition to exploratory analyses examining (among other variables), the political orientation of participants.

 

The Stage 1 manuscript was evaluated over two rounds of in-depth review. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/ep3jh
 
Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA.
 
List of eligible PCI RR-friendly journals:
 

References
 

1. Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K. (2011). Partitioning default effects: Why people choose not to choose. Journal of Experimental Psychology: Applied, 17, 332– 341. https://doi.org/10.1037/a0024354

2. Yam, M. L. M.  & Feldman, G. (2023). Reference points and decision-making: Impact of status quo and defaults in a conceptual replication and extensions Registered Report of Dinner et al. (2011), in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/ep3jh

Emotion is a well-established mediator of decision-making, including prospective economic decisions, but does it affect the way we respond economically to loss? According to classic economic theories, when an object is lost and cannot be recovered, our emotional attachment to that object should be irrelevant for decisions such as choosing whether to claim insurance or compensation. Intriguingly, however, this does not appear to be the case: in a series of experiments, Hsee and Kunreuther (2000) found that when people have higher affection towards an object, they are more sensitive to its loss and are more willing to claim compensation or purchase insurance for the object. They explained these findings according to an influential “consolation hypothesis” in which people see insurance compensation as means to mitigate against the emotional distress associated with property loss.

 

Using a large online sample (N=1000), Law and Feldman (2023) propose to replicate four of six studies from Hsee and Kunreuther (2000), each asking (primarily) whether people with higher affection towards an object are more willing to claim compensation or purchase insurance for that object. In each experiment, participants are randomly assigned to either a high affection group or a low affection group and then given a scenario in which the level of affection to an object is correspondingly manipulated while the monetary value is held constant. For example, for high affection: “You liked the now-damaged painting very much and you fell in love with it at first sight. Although you paid only $100, it was worth a lot more to you”, and for low affection: “You were not particularly crazy about the now-damaged painting. You paid $100 for it, and that’s about how much you think it was worth.” A range of dependent measures are then collected, including the maximum hours participants would be willing to spend driving to claim compensation, the maximum amount participants would be willing to pay for insurance, and how likely participants would be to claim compensation or purchase insurance. As part of the replication, the authors have also built in manipulation checks to confirm that the scenarios influenced participants' (imagined) level of affection for the object, and a range of exploratory analyses.

 

The Stage 1 manuscript was evaluated over two rounds of in-depth review. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/b7y5z
 
Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA.
 
List of eligible PCI RR-friendly journals:
 

 

References
 

1. Hsee, C. K., & Kunreuther, H. C. (2000). The affection effect in insurance decisions. Journal of Risk and Uncertainty, 20, 141-159. https://doi.org/10.1023/A:1007876907268

2. Law, Y. Y. & Feldman, G. (2023). Revisiting the impact of affection on insurance purchase and claim decision-making: Replication and extensions Registered Report of Hsee and Kunreuther (2000), in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/b7y5z

Researchers are often interested in moderated mediation. A predictor variable, such as number of counselling sessions, may predict an outcome, such as approach to a feared object, by way of a mediator, for example number of times the object was described in counselling. The strength of mediation in turn may depend on a moderator, such as vividness of imagery: Counselling reduces fear by way of imaginative exposure, particularly in those with vivid imagery. There may be a number of mediators ("indirect" paths), and any or all of these mediators may be moderated. In testing moderated mediation, a statistical model is specified which may or may not match the data generating process; in particular, there may or may not be moderators in the model corresponding to moderators that may or may not exist in the real data generating process, resulting in overspecification (more moderators of the indirect paths in the model than reality), underspecification (less moderators of indirect paths in the model than reality) or complete misspecification (where the moderated indirect paths in the model are not moderated in reality, and vice versa).

Researchers rely on the validity of tests (correct type I error rates), if they use frequentist statistics. Model misspecification may impact the validity of inferential tests for moderated mediation. Similarly, researchers need to be able to assess power for any analysis. In simulating power for mediated moderation, it may be important to know the possible extent to which the model is misspecified, and take this into account in planning numbers of participants. Fossum et al. (2024) will address this important problem with a series of simulations to determine if power is reduced meaningfully with over or under specification, or type I error and parameter estimates are biased for complete misspecification.

 

The Stage 1 manuscript was evaluated over two rounds of in-depth review. Based on ​detailed responses to reviewers’ and the recommender’s comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance.​​​

 

Level of bias control achieved: Level 6. No part of the data or evidence that will be used to answer the research question yet exists and no part will be generated until after IPA.
 
List of eligible PCI-RR-friendly journals:

 

 
References

 

Fossum, J. L., Montoya, A. K., & Anderson, S. F. (2024). How Does Model (Mis)Specification Impact Statistical Power, Type I Error Rate, and Parameter Bias in Moderated Mediation? A Registered Report. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/8gwfu