The competitive play of digital games known as ‘esports’ has surged in popularity over the past few decades. Millions of people nowadays participate in esports as a hobby, and many consider becoming professional esports athletes as a potential career path. However, psychological factors that may predict one's long-term success in esports are not entirely clear.

 

The current Registered Report by Martončik and colleagues (2024) offered a comprehensive test of potential predictors of long-term success in the two currently most impactful PC esports games, namely League of Legends (LoL) and Counter Strike: Global Offensive (CSGO). A wide range of predictors were examined, including native and deliberate practice, attention, intelligence, reaction time, and persistence etc. In both LoL and CSGO, deliberate practice did not meaningfully predict players' highest rank in the past 12 months, as an indicator of long-term success. Younger age predicted better performance in both titles though. Lastly, two title-specific predictors emerged: in LoL, more non-deliberate practice hours predicted better performance, while in CSGO better attention predicted better performance.

 

To explain these findings, the authors proposed the information density theory. Different games differ in the amount of knowledge that is required for achieving long-term success. For information-heavy games such as LoL, naive practice hours may be more essential for players to acquire game-relevant information via playing, compared to information-light games such as CSGO. This might also explain why deliberative practice did not meaningfully predict performance in LoL and CSGO. While this theory still needs to be further tested, the current results will be useful to individuals who are considering pursuing a professional career in esports, as well as professional and semi-professional esports teams and coaches.

 

This Stage 2 manuscript was assessed over two rounds of in-depth review. The recommenders judged the responses to the reviewers' comments were satisfactory, and that the manuscript met the Stage 2 criteria for recommendation.

 

URL to the preregistered Stage 1 protocol: https://osf.io/84zbv
 
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: 
 

• Advances in Cognitive Psychology
• Collabra: Psychology
• F1000Research
• Journal of Cognition
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Studia Psychologica
• Swiss Psychology Open
   

 
Martončik, M., Karhulahti, V.-M., Jin, Y. & Adamkovič, M. (2023). Psychological predictors of long-term esports success: A Registered Report [Stage 2]. Acceptance of Version 1.7 by Peer Community in Registered Reports. https://osf.io/b6vdf

Traditional Likert-type items are commonly used but can elicit response bias. An alternative approach, the forced-choice question, required respondents to rank order all items. Forced-choice questions boast some advantages but required advanced item response theory analysis to generate scores which are comparable across individuals and to evaluate the properties of those scales. However, there has been limited discussion of how to test differential item functioning (DIF) in these scales. In a previous study, Lee et al. (2021) proposed a method for testing DIF.

 

Here, Plantz et al. (2024) explore the implications of incorrect specification of anchors in DIF detection for forced choice items. The study proposes to use a Monte Carlo simulation which manipulates sample size, equality of sample size across groups, effect size, percentage of differentially functioning items, analysis approach, anchor set size, and percent of DIF blocks in the anchor set. This study aims to answer research questions about the type I error and power of DIF detection strategies under a variety of circumstances, both evaluating whether the results from Lee et al. (2021) generalize to misspecified models and expanding to evaluate new research questions. Results of this study will provide practical implications for DIF testing with forced-choice questions. An important limitation of the study is that it does not explore non-uniform DIF, only uniform DIF. Additionally, as with all simulation studies not all results can only apply to conditions which are simulated and so rely on the realistic selection of simulation conditions. The authors have selected conditions to match reality in circumstances where data is available, but relied on previous simulations in cases when data is not available. 

 

This Stage 1 manuscript was evaluated over two rounds of review by two reviewers with expertise in psychometrics. An additional round of review was completed by the recommender only. Based on the merits of the original submission and responsiveness of the authors to requests from the reviewers, 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/p8awx
 
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. Lee, P., Joo, S.-H. & Stark, S. (2021). Detecting DIF in multidimensional forced choice measures using the Thurstonian Item Response Theory Model. Organizational Research Methods, 24, 739–771. https://doi.org/10.1177/1094428120959822

 

2. Plantz, J. W.,  Brown, A., Wright, K. & Flake, J. K. (2024). Detecting DIF in Forced-Choice Assessments: A Simulation Study Examining the Effect of Model Misspecification. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/p8awx

Comparative research is how evidence is generated to support or refute broad hypotheses (e.g., Pagel 1999). However, the foundations of such research must be solid if one is to arrive at the correct conclusions. Determining the external validity (the generalizability across situations/individuals/populations) of the building blocks of comparative data sets allows one to place appropriate caveats around the robustness of their conclusions (Steckler & McLeroy 2008).

In the current study, Alley and colleagues (2023) tackled the external validity of comparative research that relies on subjects who are either university students or participating in experiments via an online platform. They determined whether data from these two types of subjects have measurement equivalence - whether the same trait is measured in the same way across groups.

Although they use data from studies involved in the Many Labs replication project to evaluate this question, their results are of crucial importance to other comparative researchers whose data are generated from these two sources (students and online crowdsourcing). The authors show that these two types of subjects do not often have measurement equivalence, which is a warning to others to evaluate their experimental design to improve validity. They provide useful recommendations for researchers on how to to implement equivalence testing in their studies, and they facilitate the process by providing well annotated code that is openly available for others to use.

After one round of review and revision, the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation.

 

Level of bias control achieved: Level 2. At least some data/evidence that was used to answer the research question had been accessed and partially observed by the authors prior to Stage 1 IPA, but the authors certify that they had not yet observed the key variables within the data that were used to answer the research question AND they took additional steps to maximise bias control and rigour.

 

 

• Advances in Methods and Practices in Psychological Science
• Collabra: Psychology
• F1000Research
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Studia Psychologica
• Swiss Psychology Open

 

 

1. Pagel, M. (1999). Inferring the historical patterns of biological evolution. Nature, 401, 877-884. https://doi.org/10.1038/44766

 

2. Steckler, A. & McLeroy, K. R. (2008). The importance of external validity. American Journal of Public Health 98, 9-10. https://doi.org/10.2105/AJPH.2007.126847

 

3. Alley L. J., Axt, J., & Flake J. K. (2023). Convenience Samples and Measurement Equivalence in Replication Research [Stage 2 Registered Report] Acceptance of Version 2 by Peer Community in Registered Reports​. https://osf.io/s5t3v

COVID-19 has been suspected to have long-lasting effects on cognitive function. The SARS-CoV-2 virus may enter the central nervous system (Frontera et al., 2020; Miners, Kehoe, & Love, 2020), explaining the observed detrimental effects of COVID-19 on verbal planning and reasoning (Hampshire et al., 2021; Wild et al., 2021), executive function (Hadad et al., 2022), and long-term memory (Guo et al., 2022). In particular, Guo et al. (2022) used verbal item recognition and non-verbal associative memory tasks. Weinerova et al. (2024), in the current study, propose to conduct a replication of Guo et al. (2022), but specifically, to disentangle the effect of COVID-19 infection status on both memory type (item vs. associative) and stimulus modality (verbal vs. non-verbal). Furthermore, Weinerova et al. (2024) propose to analyze cognitive function based on vaccination status before infection to provide a critical test of the potential protective effects of vaccination on cognitive function.

Data collection has been completed with 325 participants after exclusion criteria were applied (COVID group N = 232, No COVID group N = 93). Simulations assuming an effect size observed in Guo et al. (2022), a Bayesian t-test comparing the groups, and a Bayes Factor of 6 indicated that N = 320 is sufficient to detect an effect on 79% of simulations. The main analyses will be conducted using a Bayesian ANCOVA that allows for the inclusion of control variables such as age, sex, country, and education level. Both accuracy and reaction times from the item and associative recognition tasks will be analyzed as the dependent variables. In one analysis, vaccination status will be included as a between-subjects factor, to understand whether vaccination status at the time of infection influences subsequent cognitive function. 

It is important to note that participants were recruited through long-COVID Facebook groups and clinics. Therefore, the results must be interpreted carefully to avoid generalizing to all COVID-19 infections. The data are part of a larger longitudinal study, and the current pre-registration applies only to the baseline timepoint for a cross-sectional analysis. The remaining longitudinal data collection is ongoing and is not part of the current pre-registration.  

The study plan was refined after one round of review, with input from three external reviewers who all agreed that the proposed study was well-designed and scientifically valid. The recommender then reviewed the revised manuscript and judged that the study met the Stage 1 criteria for in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/tjs5u (under temporary private embargo)
 
Level of bias control achieved: Level 3. At least some data/evidence that will be used to the answer the research question has been previously accessed by the authors (e.g. downloaded or otherwise received), but the authors certify that they have not yet observed ANY part of the data/evidence.
 
List of eligible PCI RR-friendly journals:

 

 

 

1. Frontera, J., Mainali, S., Fink, E.L. et al. Global Consortium Study of Neurological Dysfunction in COVID-19 (GCS-NeuroCOVID): Study Design and Rationale. Neurocrit Care 33, 25–34 (2020). https://doi.org/10.1007/s12028-020-00995-3

2. Guo, P., Benito Ballesteros, A., Yeung, S. P., Liu, R., Saha, A., Curtis, L., Kaser, M., Haggard, M. P. & Cheke, L. G. (2022). COVCOG 2: Cognitive and Memory Deficits in Long COVID: A Second Publication From the COVID and Cognition Study. Frontiers in Aging Neuroscience. https://doi.org/10.3389/fnagi.2022.804937  

3. Hadad, R., Khoury, J., Stanger, C., Fisher, T., Schneer, S., Ben-Hayun, R., Possin, K., Valcour, V., Aharon-Peretz, J. & Adir, Y. (2022). Cognitive dysfunction following COVID-19 infection. Journal of NeuroVirology, 28(3), 430–437. https://doi.org/10.1007/s13365-022-01079-y  

4. Hampshire, A., Trender, W., Chamberlain, S. R., Jolly, A. E., Grant, J. E., Patrick, F., Mazibuko, N., Williams, S. C., Barnby, J. M., Hellyer, P. & Mehta, M. A. (2021). Cognitive deficits in people who have recovered from COVID-19. EClinicalMedicine, 39, 101044. https://doi.org/10.1016/j.eclinm.2021.101044

5. Miners, S., Kehoe, P. G., & Love, S. (2020). Cognitive impact of COVID-19: looking beyond the short term. Alzheimer's research & therapy, 12, 1-16. https://doi.org/10.1186/s13195-020-00744-w 

 

6. Weinerova, J., Yeung, S., Guo, P., Yau, A., Horne, C., Ghinn, M., Curtis, L., Adlard, F., Bhagat, V., Zhang, S., Kaser, M., Bozic, M., Schluppeck, D., Reid, A., Tibon, R. & Cheke, L. G. (2024). Changes in memory function in adults following SARS-CoV-2 infection: findings from the Covid and Cognition online study. In principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/tjs5u

7. Wild, C. J., Norton, L., Menon, D. K., Ripsman, D. A., Swartz, R. H. & Owen, A. M. (2022). Disentangling the cognitive, physical, and mental health sequelae of COVID-19. Cell Reports Medicine, 3, 100750. https://doi.org/10.1016/j.xcrm.2022.100750 

For nearly a century, rhythmic patterns in electrical brain activity have been of major interest in neuroscience and electrophysiology, but much remains to be discovered about their causal contribution to cognition and behaviour. Low-frequency oscillations in the alpha band (~8-13 Hz) have been suggested to facilitate the organisation and delivery of visual information to higher-level systems, including those involved in perception and decision-making. If so, visual perception should also operate in cycles that are synchronous with – and determined by – the phase of ongoing low-frequency oscillatory activity.

 

In this #EEGManyLabs study, Ruzzoli et al. (2023) propose a large-scale, multi-lab investigation (9 labs; N=315 human participants) of the relationship between the phase of alpha oscillations and visual perception. The authors focus in particular on replicating a formative study by Mathewson et al. (2009) which reported that during high-amplitude alpha fluctuations, stimulus visibility depended on the time the stimulus was presented relative to the phase of the pre-stimulus alpha activity. In addition, the amplitude of visual evoked potentials recorded with EEG was larger when the target was presented at peaks in pre-stimulus alpha. To explain their findings, Mathewson proposed an influential pulsed inhibition hypothesis in which low alpha power boosts both cortical excitability and stimulus processing (and hence perception), while high alpha power makes stimulus processing dependent on the phase during the alpha cycle at which the stimulus is presented.

 

In the first of (up to) two studies, the authors will seek to directly replicate the key finding of Mathewson et al: that when alpha power is high, the oscillatory phase determines perceptual performance and event-related electrophysiological correlates in a masked visual detection task. Specifically, (a) alpha oscillations are predicted to modulate the probability of perceiving a target stimulus within a single oscillatory cycle, with detection rate associated with separated (and potentially opposite) phase angles, and (b) alpha phase at the onset of the stimulus should drive electrophysiological correlates of stimulus processing (including the amplitude and/or latency of the N1 ERP component).

 

Provided the results of this first study do not conclusively disconfirm these hypotheses, the authors will then conduct a follow-up study in which the temporal predictability of the target onset (in relation to a fixation stimulus) is reduced to test the more severe hypothesis that the observed correlations between alpha phase and perception are linked directly to ongoing oscillations, independent of temporal expectations.

 

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/scqj8​ (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. 

 

 
References

 

1. Ruzzoli, M., Cuello, M. T., Molinaro, N., Benwell, C. S. Y., Berkowitz, D., Brignani, D., Falciati, L., Harris, A. M., Keitel, C., Kopčanová, M., Madan, C. R., Mathewson, K., Mishra, S., Morucci, P., Myers, N., Nannetti, F., Nara, S., Pérez-Navarro, J., Ro, T., Schaworonkow, N., Snyder, J. S., Soto-Faraco, S., Srinivasan, N., Trübutschek, D., Zazio, A., Mushtaq, F., Pavlov, Y. G., & Veniero, D. (2023). In principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/scqj8​

 

2. Mathewson, K. E., Gratton, G., Fabiani, M., Beck, D. M., & Ro, T. (2009). To see or not to see: prestimulus α phase predicts visual awareness. Journal of Neuroscience, 29, 2725-2732. https://doi.org/10.1523/JNEUROSCI.3963-08.2009