The sunk cost fallacy is a cognitive bias in which people persist with a decision that is no longer optimal because of previous resources they have invested (now considered to be spent or “sunk”). Most of us will have heard sunk costs reflected in the saying “throwing good money after bad”, but sunk costs can, in theory, occur more broadly, whether for money, time or any other resource-limited investment. The sunk cost effect for money has been widely studied and appears robust; in contrast, the sunk cost effect for time is more uncertain, and is potentially moderated by the age of respondents (and likely resource availability), the fact that time is irreplaceable, and the tendency for people to account for time less easily than they do for money. In an impactful study, Soman (2001) found that the sunk cost effect for time was indeed weaker than for money, although this finding has not been widely replicated.

 

In the current study, Petrov et al. (2023) propose a replication of three studies from Soman (2001), asking whether sunk costs are weaker for time than for money, and then testing whether the relative absence of a sunk time cost arises from the inability of participants to account for time or due to more rational beliefs in the evaluation of past time investments.

 

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/65htv

 

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. Soman, D. (2001). The mental accounting of sunk time costs: Why time is not like money. Journal of Behavioral Decision Making,14, 169-185. https://doi.org/10.1002/bdm.370

 

2. Petrov, N. B., Chan, Y. K., Lau, C. N., Kwok, T. H., Chow, L. C., Lo, W. Y. V, Song W., & Feldman, G. (2023). Sunk cost effects for time versus money: Replication of Soman (2001) [Registered Report Stage 1], in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/u34zb

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

Vocal sounds, including both speech and non-speech sounds, have been found to activate the Superior Temporal Sulci and Gyri in comparison to non-vocal sounds. These regions, termed Temporal Voice Areas (TVAs), are considered to be involved in early voice processing and therefore critical for social interaction. TVA activation has been examined in Autism Spectrum Disorder (ASD) to determine if the characteristic difficulties in social communication and interaction are linked to an impaired early voice processing. Using functional magnetic resonance imaging (fMRI), one study found typical brain activation in TVAs for 15 out of 16 autistic participants (Schelinski et al., 2016), whereas another found atypical activation in 4 out of 5 autistic participants (Gervais et al., 2004).

 

Here, the inconsistencies in the previous literature propel Gautier et al. (2023) to examine brain activation of TVAs with a larger sample size (26 ASD and 26 non-ASD participants). Gautier et al. (2023) will present vocal sounds and non-vocal sounds to both groups of participants during fMRI and predict that fewer participants in the ASD group will show a preferential response to voices in TVAs compared to the non-ASD group. These results would suggest that symptoms of ASD interfere with early stages of social interaction, at the level of voice processing.

 

This Stage 1 manuscript was evaluated in an initial round by the co-recommenders and another two rounds of in-depth review by two expert reviewers. With these revisions, the recommenders judged that the manuscript met the Stage 1 criteria and awarded in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/538m4
 
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. Gautier, R., Houy-Durand, E., Barantin, L., Briend, F. & Latinus, M. (2023). Cortical voice processing in Autism Spectrum Disorder. In principle acceptance of Version 4 by Peer Community in Registered Reports. https://osf.io/538m4

Recent studies using an EEG frequency tagging approach have reported modulations of alpha, beta and theta bands at the stimulation frequency during nociceptive/painful thermal stimulation compared to non-nociceptive/non-painful vibrotactile stimulation. Prior expectations of the intensity of upcoming painful stimuli are known to strongly modulate the subjective experience of those stimuli. Thus, modulating the expectation of pain should result in a change in the modulation of oscillations if these factors are indeed linked.

 

In this study, Leu, Glineur and Liberati will modulate expectations of pain (low or high intensity) prior to delivering thermal cutaneous stimulation (low, medium or high intensity). They will record how intense participants expect the pain to be, and how intense they felt it to be, as well as record EEG to assess oscillatory differences across the expectation and intensity conditions.

 

The Stage 1 manuscript was reviewed over 5 rounds by 3 reviewers. Based on detailed responses to the reviewers’ comments and edits to the Stage 1 report, 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/y6fb8
 
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
• F1000Research
• Brain and Neuroscience Advances
• Cortex
• Imaging Neuroscience
• In&Vertebrates
• NeuroImage: Reports
• Peer Community Journal
• PeerJ
• Psychology of Consciousness: Theory, Research and Practice
• Studia Psychologica
• Royal Society Open Science
   

References
 
1. Leu, C., Glineur, E. & Liberati, G. (2023). Cue-based modulation of pain stimulus expectation: do ongoing oscillations reflect changes in pain perception? In principle acceptance of Version 5 by Peer Community in Registered Reports. https://osf.io/y6fb8

Data sharing is perhaps the most fundamental step for increasing the transparency and reproducibility of scientific research. However, the goals of open science must be tempered by ethical considerations, protecting the privacy and safety of research participants. Bridging this gap causes challenges for many fields, such as human neuroimaging. Brain images, as measured with magnetic resonance imaging (MRI), are unique to the participant and therefore contain identifying information by definition. One way to mitigate the risk to participants arising from public data sharing has been "defacing" the MRI scans, i.e., literally removing the part of the image that contains the face and surrounding tissue, while preserving the brain structure. This procedure however also removes information that is not (or at least minimally) identifiable. It also remains unclear whether defacing the images affects image quality and thus the information necessary for addressing many research questions.

 

The current study by Provins et al. (2023) seeks to address this question. Leveraging a publicly available "IXI dataset" comprising hundreds of T1-weighted structural MRI scans, they will assess the effect of defacing on manual and automatic estimates of image quality. Specifically, the researchers will compare image quality ratings by experts for a subset of 185 images. They hypothesise that images in which facial features have been removed are typically assigned higher quality ratings. Moreover, using a full data set of 580 images, which have been obtained across three scanning sites, they will also test the impact defacing MRI scans has on automated quality measures obtained with MRIQC software. The results of this study should have important implications for open science policy and for designing the optimal procedures for sharing structural MRI data in an ethical way. For example, if the authors' hypothesis is confirmed, studies relying on MRI quality measures might be better served by a custodianship model where identifiable data is shared under strict conditions, rather than relying on publishing defaced data. More generally, the outcome of this study may have significant legal implications in many jurisdictions.

 

The Stage 1 manuscript was evaluated at the inital triage stage by the Recommender and PCI:RR team, and another round of in-depth review by two experts. After a detailed response and substantial revisions, the recommender judged the manuscript met the Stage 1 criteria and awarded in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/qcket (under temporary private embargo)
 
Level of bias control achieved: Level 2. At least some data/evidence that will be used to answer the research question has been accessed and partially observed by the authors, but the authors certify that they have not yet observed the key variables within the data that will be used to answer the research question AND they have taken additional steps to maximise bias control and rigour.
 
List of eligible PCI RR-friendly journals:
 

 

1. Provins, C., Savary, E., Alemán-Gómez, Y., Richiardi, J., Poldrack, R. A., Hagmann, P. & Esteban, O. (2023). Defacing biases in manual and automated quality assessments of structural MRI with MRIQC, in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/qcket