In the past two decades, most academic fields have witnessed an open science revolution that has led to significant increases in open access publishing, reproducibility efforts, and scientific transparency in general (e.g., Spellman et al. 2018). One of the key areas in this ongoing change is data sharing. Although some evidence already points at progress in data sharing practices, many new datasets remain unshared (see Tedersoo et al. 2021).
 
In the present registered report, Henderson et al. (2023) empirically explore the factors that either hinder or facilitate data sharing in the UK. By means of semi-structured interviews, the team will chart researchers’ experiences of sharing and non-sharing. Thematic template analysis will be applied to organise the data into a hierarchical map of capabilities, opportunities, and motivations in a theoretical domains framework (COM-B-TDF). The research plan itself meets the highest open science standards and reflects on the authors own positions, from which the current qualitative interview data sharing efforts will be made.
 
The Stage 1 manuscript was reviewed over three rounds by three experts with familiary of the UK cultural context and specializations in open science practices, qualitative research, and data infrastructures. Based on careful revisions and detailed responses to the reviewers’ comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance.
 
URL to the preregistered Stage 1 protocol: https://osf.io/2gm5s (under temporary private embargo)
 
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. Henderson, E., Marcu, A., Atkins, L. & Farran, E.K. (2023). Investigating the barriers and enablers to data sharing behaviours: A qualitative Registered Report. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/2gm5s

 

2. Spellman, B. A., Gilbert, E. A. & Corker, K. S. (2018). Open Science. Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, 5, 1-47. https://doi.org/10.1002/9781119170174.epcn519

 

3. Tedersoo, L., Küngas, R., Oras, E., Köster, K., Eenmaa, H., Leijen, Ä., ... & Sepp, T. (2021). Data sharing practices and data availability upon request differ across scientific disciplines. Scientific data, 8, 192. https://doi.org/10.1038/s41597-021-00981-0

Previous research has demonstrated that experimental participants – both adults and infants – can track syllable co-occurrences in an artificial speech stream. This statistical learning (SL) is thought to support word segmentation and to be a core component of language acquisition. There is also some evidence that SL, as measured through artificial language tasks, is related to individual differences in language learning. What is not well understood is the underpinnings of individual differences in SL. In the current study, van der Wulp et al. (2023) will investigate the relationship between auditory SL and musical – specifically rhythmic – abilities. This work takes advantage of recent methodological innovations which allow online assessment of SL via electroencephalography (EEG) measures of neural entrainment. Up to N=105 participants will be recruited (using a well specified optional stopping procedure) and will be exposed to an artificial language using this EEG method to measure their response to the language through the exposure phase. In addition, post-tests will assess participants’ learning of the artificial language using behavioural tasks, as well as their abilities in a battery of tasks measuring rhythmic, musical, and cognitive abilities, and their vocabulary size. Core analyses will investigate whether the neural signature of statistical learning is found at a group level (i.e. replicating previous work) and whether at the individual level this is related to rhythm perception. Bayes factors will be used to assess the extent of evidence for these hypothesized relationships over the null. Exploratory analyses will explore other relationships, including with working memory and vocabulary size. 

 

The topic of individual differences in language learning and is important, but work in this area is often underpowered, and the correlational nature of the research makes it vulnerable to HARKing. In this context, the current RR looks set to make an important contribution. The Stage 1 manuscript was evaluated over two rounds of in-depth review by the recommender and two expert reviewers, and one additional round of review by the recommender, before issuing 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:
 

• Advances in Cognitive Psychology
• Brain and Neuroscience Advances
• Collabra: Psychology
• Cortex
• Experimental Psychology
  
• Imaging Neuroscience
• In&Vertebrates
• Journal of Cognition
• NeuroImage: Reports
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Studia Psychologica

 

References
 
1. van der Wulp, I. M., Struiksma, M. E., Batterink, L. J., & Wijnen, F. N. K. (2023). Investigating individual differences in linguistic statistical learning and their relation to rhythmic and cognitive abilities: A speech segmentation experiment with online neural tracking. In principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/2y6sx

 

The reward value of a stimulus is based on an error in prediction: Things going better than predicted. Could this learning principle, often tested on short acting stimuli, also apply to a long lasting episode, like going for a run? Could how rewarding a run is be based on the run going better than predicted?

 

Understanding the conditions under which exercise is pleasurable could of course be relevant to tempting people to do more of it! Brevers et al. (2023) will ask people before a daily run to predict the amount of perceived exertion they will experience; then just after the run, to rate the retrospective amount of perceived exertion actually experienced. The difference between the two ratings is the prediction error.

 

Participants will also rate their remembered pleasure in running and the authors will investigate whether running pleasure depends on prediction error.
 
The study plan was refined across four rounds of review, with input from two external reviewers and the recommender, after which it was judged to satisfy the Stage 1 criteria for in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/xh724
 
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. Brevers, D., Martinent, G., Oz, I. T., Desmedt, O. & de Geus, B. (2023). Do error predictions of perceived exertion inform the level of running pleasure? In principle acceptance of Version 5 by Peer Community in Registered Reports. https://osf.io/xh724

Visual symmetry is critical to our interaction with our environment so that when detected, symmetry automatically produces a neural marker in the form of an Event Related Potential (ERP) called Sustained Posterior Negativity (SPN). However, when symmetry is presented to the visual system slanted away from the viewer, there is a reduction in SPN, termed a perspective cost. 

 

Considering ​objects are rarely presented front-on (or frontoparallel) in our natural environment, Karakashevska et al., (2023) plan to examine the extent of the perspective cost with the addition of visual cues to facilitate extraretinal representation of the visual symmetry. The authors will record electroencephalography (EEG) from 120 participants while they perform a luminance task on symmetrical and asymmetrical stimuli. The authors hypothesize perspective cost will be reduced by three perspective cues: 1) monocular viewing, when cue conflict caused by binocular viewing is eliminated, 2) a static frame surrounding the symmetrical stimulus, adding a depth cue, and 3) a moving frame, assisting 3D perception prior to the symmetry onset. If the SPN is equivalent during frontoparallel and slanted presentation in a cue condition, the authors will conclude extraretinal representation can be automatic when sufficient visual cues are available. The proposed experiment is powered to detect a relatively small difference between perspective cue conditions. This will solidify fundamental research on visual symmetry processing and will further our understanding of object perception and recognition. 

 

The Stage 1 manuscript was evaluated over three rounds by four expert reviewers. Following in-depth review and responses from the authors, the recommenders have determined that Stage 1 criteria was met and have awarded in-principle acceptance (IPA). 

URL to the preregistered Stage 1 protocol: https://osf.io/yzsq5
 
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:
 

• Advances in Cognitive Psychology
• Brain and Neuroscience Advances
• Cortex
• F1000Research
• Imaging Neuroscience
• In&Vertebrates
• NeuroImage: Reports
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Studia Psychologica
• Swiss Psychology Open

 
References
 
1. Karakashevska, E., Bertamini, M. & Makin, A. D. J. (2023). Putting things into perspective: Which visual cues facilitate automatic extraretinal symmetry representation? [Stage 1 Registered Report]. In principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/yzsq5

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) sought 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 replicated 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 extended it to test the more severe hypothesis that empathy causally influences forgiveness. To achieve this, they experimentally manipulated empathy by adding two groups to the design: one in which participants were asked to recall hurtful past experiences in which they were not empathetic to the offender, and another in which they were highly empathetic.

 

The outcomes constitute a successful replication. Affective empathy was positively associated with perceived apology and forgiveness, and forgiveness was positively associated with conciliation motivation and negatively associated with both avoidance motivation and revenge motivation. In addition, the results of the experimental extension revealed a reliable causal effect of empathy on forgiveness and perceived apology. Overall, the findings provide robust support for the Empathy Model of forgiveness.

 

The Stage 2 manuscript was evaluated over one round of in-depth review. Based on responses to the comments, the recommender judged that the manuscript met the Stage 2 criteria and therefore awarded a positive recommendation.

 

 

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

 

• Collabra: Psychology
• F1000Research
• Meta-Psychology
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Swiss Psychology Open
   

 

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. McCullough, M. E., Worthington, E. L., & Rachal, K. C. (1997). Interpersonal Forgiving in Close Relationships. Journal of Personality and Social Psychology, 73, 321–336. https://doi.org/10.1037/0022-3514.73.2.321 

 

3. Chan, C. F. & Feldman, G. (2023). The link between Empathy and Forgiveness: Replication and extensions of McCullough et al. (1997)'s Study 1, acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/956fa