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 

Unconscious priming effects have fascinated not just psychologists but also ad-makers and consumers alike. A related phenomenon in perception is illustrated by presenting participants with two-tone images, which are degraded versions of images of objects and scenes. These two-tone images look like and are indeed judged as meaningless dark and light patches. Upon presenting the actual template image, however, the two-tone image is accurately recognized. This perceptual learning is abrupt, robust, and long-lasting (Daoudi et al., 2017). Surprisingly, Chang et al. (2016) showed that such perceptual disambiguation of two-tone images can happen even in the absence of conscious awareness of having seen the template image. 

 

Halchin et al. (2023) in the current study propose to conduct a conceptual replication of Chang et al. (2016) with important modifications to the procedures to address limitations with the earlier work. Specifically, there was no explicit manipulation of levels of conscious awareness of the template images in the original study. Therefore, miscategorization of low-confidence awareness as unaware could have led to an erroneous conclusion about unconscious priors guiding perceptual learning. Such miscategorization errors and how to tackle them are of interest to the broader field of consciousness studies. Furthermore, a conceptual replication of Chang et al. (2016) is also timely given that prior related work suggests that masking impairs not only conscious awareness of visual features but also blocks processing of higher-level information about the images (e.g. object category). 

 

To address the issues identified above, Halchin et al. (2023) propose to experimentally manipulate conscious awareness by masking the template image very quickly (i.e., a short stimulus onset asynchrony; SOA) or by allowing some more time to induce weak and strong conscious awareness, respectively. The SOAs were validated through pilot studies. Furthermore, they include a four-point perceptual awareness scale instead of the original yes/no options to gauge participants’ subjective awareness of the template images. The authors also propose multiple experiments to include different ways of testing participants’ objective ability to identify the masked template images. Last but not least, the proposed design includes a stronger control condition than the original study by using masked images created from related images (e.g. belonging to the same semantic category). Depending on the results obtained in the main experiments, the inclusion of this control allows the authors to conduct a third experiment to investigate whether the results in the first two can be explained by semantic priming. The proposed study is sufficiently powered (as demonstrated through simulations), and Bayesian statistical procedures will be used to test the main hypotheses. In summary, the proposed work offers a significant improvement in terms of experimental procedures over the original study. If the Chang et al. (2016) results are replicated, the stronger design in the current study is likely to lead to a better understanding of the mechanisms underlying unconscious priors guiding perceptual learning. On the other hand, a failure to replicate not just Chang et al. (2016)’s results but also effects across the three experiments in the current study would raise legitimate questions about the reality of unconscious information guiding perceptual learning. 

 

The study plan was refined across two rounds of review, with input from two external reviewers who both agreed that the proposed study is well designed, timely, 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/juckg
 
Level of bias control achieved: Level 3. At least some of the data/evidence that will be used to answer the research question already exists AND is accessible in principle to the authors BUT the authors certify that they have not yet accessed any part of that data/evidence.
 
List of eligible PCI-RR-friendly journals:

 

• Advances in Cognitive Psychology
• Collabra: Psychology
• Cortex
• Journal of Cognition
• Peer Community Journal
• PeerJ
• Psychology of Consciousness: Theory, Research and Practice
• Royal Society Open Science
• Studia Psychologica
• Swiss Psychology Open

 

 

1. Daoudi, L. D., Doerig, A., Parkosadze, K., Kunchulia, M. & Herzog, M. H. (2017). The role of one-shot learning in #TheDress. Journal of Vision, 17, 15-15. https://doi.org/10.1167/17.3.15 

 

2. Chang, R., Baria, A. T., Flounders, M. W., & He, B. J. (2016). Unconsciously elicited perceptual prior. Neuroscience of Consciousness, 2016. https://doi.org/10.1093/nc/niw008 

 

3. Halchin, A.-M., Teuful, C. & Bompas, A. (2023). Can one-shot learning be elicited from unconscious information? In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/juckg

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.