Although developmental coordination disorder (DCD) is a common neurodevelopmental disorder with an estimated prevalence of up to 6% in children (APA, 2013), many DCD diagnoses are not made before late adulthood. Receiving a neurodevelopmental disorder diagnosis has been found adding to people’s self-concepts, for instance, with autism spectrum disorder (Tan, 2018), but it is not well known if and how such events unfold in late DCD diagnoses. In this Stage 1 Registered Report, Topor et al. (2022) present a careful plan to qualitatively investigate the lived experiences of individuals with a late DCD diagnosis in order to map out the variety of emotional responses to diagnoses and their effects on self-concepts.

Topor et al. (2022) will carry out 10–15 semi-structured interviews with participants who received a DCD diagnosis at the age of 30 or after. They commit to realist epistemology when utilizing thematic analysis; namely, instructions have been preregistered for two separate analysts who will code the transcript data independently. At the same time, the methodology involves reflexive components. The authors have prepared strong positionality statements through which their analyses will be carried out with post-analysis reflections to be written at Stage 2. The coding process will explicitly involve a data analysis log that pursues interpretive transparency. The data and materials will be shared, which adds to the work's value in the context of open qualitative psychology in general.   
 
The study will help us better understand the process of receiving (late) DCD diagnoses and, specifically, how the emotional aftermath is potentially related to one’s evolving self-concept. In addition to making a clear contribution to cumulative scientific knowledge, the findings can be useful for professionals working with DCD-diagnosed individuals as well as for the development of related support services. The Registered Report format allowed the research design to be reviewed in three rounds before data collection. Initially, three experts representing developmental psychology and DCD reviewed the Stage 1 manuscript, after which the recommender carried out two iterations with further requested revisions. This was followed by in-principle acceptance.

 

URL to the preregistered Stage 1 protocol: https://osf.io/67h3f
 
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. APA (2013) Diagnostic and statistical manual of mental disorders, 5th ed, American Psychiatric Association, Washington, DC.

2. Tan, C. D. (2018). “I'm a normal autistic person, not an abnormal neurotypical”: Autism Spectrum Disorder diagnosis as biographical illumination.” Social Science & Medicine, 197, 161-167.

 

3. Topor, M., Armstrong, G., Gentle, J. (2022). Through the lens of Developmental Coordination Disorder (DCD): experiences of a late diagnosis, in principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/67h3f

Screening instruments that aim to provide diagnostic classifications of gaming-related health problems derive from different ontologies and it is not known whether they identify equivalent prevalence rates of ‘gaming disorder’ or even the same individuals. Underpinned by this, Karhulahti et al. (2022) assessed how screening instruments that derive from different ontologies differ in identifying associated problem groups. A nationally representative sample of 8217 Finnish participants completed four screening measures to assess the degree of overlap between identified prevalence (how many?), who they identify (what characteristics?) and the health of their identified groups (how healthy?).

 

The results indicate that measures based on the ICD-11, DSM-5, DSM-IV, and self-assessment appear to be associated with lower mental health. However, these measures of gaming-related health problems differed significantly in terms of prevalence and/or overlap, suggesting that they identify different groups of people and that different problems or constructs are being measured by different instruments. These findings are important because they contribute to the rapidly growing literature on the ‘fuzziness’ of  constructs and measures relating to technology use. The authors recommend that researchers working with these measures should: (a) define their construct of interest; and (b) evaluate the construct validity of their instruments. Being able to answer these questions will enhance research quality and contribute to strengthened meta-analyses. Importantly, this will prevent hype around gaming-related disorders, allowing researchers to communicate clearly and appropriately without risk of confusing related yet different constructs.

 

The Stage 2 manuscript was evaluated by two of the reviewers who assessed it at Stage 1. Following revision, the recommender judged that the manuscript met the Stage 2 criteria and awarded a  positive recommendation. To ensure that the manuscript met the requirements of the PCI RR TOP guidelines, prior to this acceptance an email communication was sent to the authors by the recommender to ensure that study data were openly available on a temporary OSF link before the final data archive is full validated by the Finnish Social Sciences Data Archive (FSD). This is noted in the recommended preprint.

 

 

Level of bias control achieved: Level 6. No part of the data or evidence that was used to answer the research question existed prior to Stage 1 in-principle acceptance.

 

 

 

 

1. Karhulahti V.-M., Vahlo J., Martončik M., Munukka M., Koskimaa R. and Bonsdorff M. (2022). Ontological Diversity in Gaming Disorder Measurement: A Nationally Representative Registered Report. Peer-reviewed and recommended at Stage 2 by Peer Community in Registered Reports https://psyarxiv.com/qytrs

One of the major challenges in studies of language learning is understanding productive generalisation – the ability to use words and linguistic structures in novel settings that the learner has never encountered previously. According to discriminative learning theory, this skill arises from an iterative process of prediction and error-correction that gradually reduces uncertainty, allowing learners to discriminate linguistic outcomes and to identify informative, invariant cues for generalisation to novel cases. In the current study, Viviani et al (2022) use computational modelling to propose a central hypothesis stemming from this theory that children will learn the meaning and use of spatial adpositions (words such as “above” and “below” that describe relative positions) more effectively when there is more variability in the use of the nouns within the spatial sentences. They will also test a range of additional hypotheses, including that learning and generalisation to novel contexts will be enhanced when children learn from skewed distributions that are similar to those found in natural languages.

 

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/37dxr

 

Level of bias control achieved: Level 6. Some of the data that will be used in the preregistered analyses was obtained in the second of two pilot experiments. However, since no further revisions to the analysis plan were made after this pilot, the risk of bias due to prior data observation remains zero, and the manuscript therefore qualifies for Level 6.
 
List of eligible PCI RR-friendly journals:

 

 

1. Viviani, E., Ramscar, M. & Wonnacott, E. (2022). Go above and beyond: Does input variability affect children’s ability to learn spatial adpositions in a novel language? In principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/37dxr

Stigmas are prejudices or discrimination against people based on qualities that vary from the norm, such as a physical or mental illness, disability, sexuality, race, or one of many other personal characteristics. The harm caused by stigmatisation has made understanding the causes and potential solutions a vital area of study in psychology and public health. One of the major focuses of ongoing research is understanding the factors that determine whether a particular characteristic becomes stigmatised, and if so how the stigma might be eliminated. Previous research has found that for disease-based stigmas, the contagiousness, course, and disruptiveness of a disease can be influential. Another key determinant is the perceived cause of the stigmatised condition or characteristic. In a landmark study, Weiner et al. (1988) reported that attributes based on physical health were more likely to be perceived as being uncontrollable, stable and irreversible, prompting feelings of sympathy without anger or judgment. On the other hand, attributes related to mental health and behaviour were more likely to be regarded as controllable and reversible, prompting lack of sympathy coupled with feelings of anger and negative judgement. In a second experiment, they also found that manipulating the perception of controllability can modify emotional responses and judgments – for some stigmas (but not others), providing participants with information that a particular characteristic was controllable vs. uncontrollable was found to increase or decrease negative attributions, respectively.

 

In the current study, Yeung and Feldman (2022) propose to replicate Experiment 2 from Weiner et al. (1988) in a large online sample. In particular, they plan to ask how the source of a stigma is related to perceived controllability and stability, emotional reactions, and willingness to help. They also propose a range of extensions, including the inclusion of additional stigmas that have become relevant since the original study was published over 30 years ago.

 

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/k957f

 

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. Weiner, B., Perry, R. P., & Magnusson, J. (1988). An attributional analysis of reactions to stigmas. Journal of Personality and Social Psychology, 55, 738–748. https://doi.org/10.1037/0022-3514.55.5.738

 

2. Yeung, K. Y. & Feldman, G. (2022). Revisiting stigma attributions and reactions to stigma: Replication and extensions of Weiner et al. (1988), in principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/k957f

How do emotions interact with cognition? The last 40 years has witnessed the rise of cognitive-appraisal theories, which propose that emotions can be differentiated along an axis of cognitive dimensions such as certainty, pleasantness, attentional activity, control, anticipated effort, and responsibility (Smith and Ellsworth, 1985). Early tests of such theories focused especially on the impact of the valence – pleasantness/unpleasantness – of emotions on judgment and decision-making, finding, for instance, that negative mood induction can heighten pessimistic estimates of risk (Johnson & Tversky, 1983).

 

The Appraisal-Tendency Framework proposed by Lerner and Keltner (2000) refined cognitive-appraisal theory by proposing that specific emotions trigger a predisposition to appraise future (or hypothetical) events in line with the central appraisal dimensions that triggered the emotion, even when the emotion and the judgment are unrelated. For example, an individual who is triggered to become fearful of a heightened risk, such as nuclear war, may then exhibit heightened pessimism about risks unrelated to war. The Appraisal-Tendency Framework also predicts relationships between traits, such as between fear, anger and risk-taking/risk-seeking tendencies. In an influential paper, Lerner and Keltner (2001) reported direct empirical support for the Appraisal-Tendency Framework, which aside from its influence in cognitive/affective psychology has had considerable impact in behavioural economics, moral psychology, and studies of consumer behaviour.

 

In the current study, Lu and Feldman (2022) propose to replicate three key studies from Lerner and Keltner (2001) in a large online sample. Through a combination of replication and extension, the authors will probe the relationship between various trait emotions (including fear, anger, happiness, and hope) and trait characteristics of risk seeking and optimistic risk assessment. The authors also propose examining how the ambiguity of triggering events moderates the relationship between specific emotions and risk judgments. 

 

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/8yu2x

 

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. Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology, 48, 813-838. https://doi.org/10.1037/0022-3514.48.4.813

 

2. Johnson, E. J., & Tversky, A. (1983). Affect, generalization, and the perception of risk. Journal of Personality and Social Psychology, 45(1), 20–31. https://doi.org/10.1037/0022-3514.45.1.20

 

3. Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgment and choice. Cognition & Emotion, 14, 473-493. https://doi.org/10.1080/026999300402763 

 

4. Lerner, J. S., & Keltner, D. (2001). Fear, anger, and risk. Journal of Personality and Social Psychology, 81, 146–159. https://doi.org/10.1037/0022-3514.81.1.146

  
5. Lu, S. & Feldman, G. (2022). Associations of fear, anger, happiness, and hope with risk judgments: Revisiting appraisal-tendency framework with a replication and extensions of Lerner and Keltner (2001), in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/8yu2x