Everyday decisions can often be biased by how different options are presented and which choices were made previously, over and above whichever option is rationally preferable. Two such major biases in judgment and decision-masking are status quo bias and the default effect. The status quo bias reflects a tendency for people to choose an option that has already been implemented or represents the current state of affairs (for instance, choosing to remain with a current energy supplier rather than switching to a different one, even when the current supplier provides less value for money), while the default effect is a bias toward taking a course of action that would occur automatically in the absence of an active choice (such as the pre-selected energy supplier in questionnaire). Status quo bias and default effects can overlap or diverge: the status quo option will often be the same as the default, while on other occasions the default option in a choice set will be determined by other factors and differ from the option that was already implemented.

 

In the current study, Yam and Feldman (2023) propose a replication and extension of an influential study by Dinner et al. (2011) that purported to examine default effects but, arguably, actually studied its nearby cousin, status quo bias. Using a large online sample, the authors will independently test for the existence of both status quo bias and the default effect, before then asking whether (and if so how) status quo bias and default effects interact and how they are related to several potential explanatory variables, including perceived effort (which may be lower for the status quo or default) and endorsements (whether through direct recommendations or indirectly through perceived norms). The authors also build in additional manipulation checks and comprehension checks, in addition to exploratory analyses examining (among other variables), the political orientation of participants.

 

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/ep3jh
 
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. Dinner, I., Johnson, E. J., Goldstein, D. G., & Liu, K. (2011). Partitioning default effects: Why people choose not to choose. Journal of Experimental Psychology: Applied, 17, 332– 341. https://doi.org/10.1037/a0024354

2. Yam, M. L. M.  & Feldman, G. (2023). Reference points and decision-making: Impact of status quo and defaults in a conceptual replication and extensions Registered Report of Dinner et al. (2011), in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/ep3jh

Probability and randomness are foundational statistical concepts used not only throughout the sciences, but also in our daily lives to guide our behavior and make sense of the world. Their importance and widespread use may suggest that they are easy concepts to understand, yet that seems not to be the case. A classic article by Tversky and Kahneman (1971) on the “belief in the law of small numbers” revealed that professional psychologists tended to incorrectly perceive a small sample that is randomly drawn from a population as representative of that population. This finding has been hugely influential, inspiring myriad subsequent studies into error and bias when reasoning about probability. 

 

In the current study, Hong and Feldman (2023) propose a conceptual replication and extension of Tversky and Kahneman (1971). The original article was shockingly sparse on details regarding the method, sample, and findings, and, to our knowledge, has never been replicated. These facts are especially concerning given the foundational status that the article holds in the field. Hong and Feldman (2023) have developed a conceptual replication project, using the same approach and targeting the same claims from Tversky and Kahneman (1971), but modifying the wording of the stimuli for clarity and appropriateness for lay respondents. Although Tversky and Kahneman (1971) relied on professional psychologists as participants, many of their claims were not restricted to that population, but rather were generalized to all people—which is also how the findings have been subsequently applied. Thus, the change from professional to lay responders is entirely appropriate and the study will be diagnostic of the original claims.

 

Finally, Hong and Feldman (2023) extend the target study by manipulating the sample size indicated in the stimuli. Tversky and Kahneman (1971) relied on a single sample size in each scenario, leaving open the question as to how sample size might impact respondents’ reasoning. Accordingly, Hong and Feldman (2023) vary the sample size across the scenarios to determine whether participants answer differently as the sample size increases. 

 

The Stage 1 manuscript was evaluated over two rounds of in-depth review, the first round consisting of detailed comments from two reviewers and the second round consisting of a close read by the recommender. Based on detailed responses to the reviewers' comments, the recommender judged that the manuscript met the Stage 1 criteria and was therefore awarded in-principle acceptance (IPA).

 

URL to the preregistered Stage 1 protocol: https://osf.io/fzbq7
 
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
• Experimental Psychology
• F1000Research
• Journal of Cognition
• Meta-Psychology
• Peer Community Journal
• PeerJ
• Royal Society Open Science
• Swiss Psychology Open

 

 

1. Hong, C. K., & Feldman, G. (2023). Revisiting the “Belief in the law of small numbers”: Conceptual replication and extensions Registered Report of problems reviewed in Tversky and Kahneman (1971). In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/fzbq7

 

2. Tversky, A., & Kahneman, D. (1971). Belief in the law of small numbers. Psychological Bulletin, 76(2), 105–110. https://doi.org/10.1037/h0031322   

Emotion is a well-established mediator of decision-making, including prospective economic decisions, but does it affect the way we respond economically to loss? According to classic economic theories, when an object is lost and cannot be recovered, our emotional attachment to that object should be irrelevant for decisions such as choosing whether to claim insurance or compensation. Intriguingly, however, this does not appear to be the case: in a series of experiments, Hsee and Kunreuther (2000) found that when people have higher affection towards an object, they are more sensitive to its loss and are more willing to claim compensation or purchase insurance for the object. They explained these findings according to an influential “consolation hypothesis” in which people see insurance compensation as means to mitigate against the emotional distress associated with property loss.

 

Using a large online sample (N=1000), Law and Feldman (2023) propose to replicate four of six studies from Hsee and Kunreuther (2000), each asking (primarily) whether people with higher affection towards an object are more willing to claim compensation or purchase insurance for that object. In each experiment, participants are randomly assigned to either a high affection group or a low affection group and then given a scenario in which the level of affection to an object is correspondingly manipulated while the monetary value is held constant. For example, for high affection: “You liked the now-damaged painting very much and you fell in love with it at first sight. Although you paid only $100, it was worth a lot more to you”, and for low affection: “You were not particularly crazy about the now-damaged painting. You paid $100 for it, and that’s about how much you think it was worth.” A range of dependent measures are then collected, including the maximum hours participants would be willing to spend driving to claim compensation, the maximum amount participants would be willing to pay for insurance, and how likely participants would be to claim compensation or purchase insurance. As part of the replication, the authors have also built in manipulation checks to confirm that the scenarios influenced participants' (imagined) level of affection for the object, and a range of exploratory analyses.

 

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/b7y5z
 
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. Hsee, C. K., & Kunreuther, H. C. (2000). The affection effect in insurance decisions. Journal of Risk and Uncertainty, 20, 141-159. https://doi.org/10.1023/A:1007876907268

2. Law, Y. Y. & Feldman, G. (2023). Revisiting the impact of affection on insurance purchase and claim decision-making: Replication and extensions Registered Report of Hsee and Kunreuther (2000), in principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/b7y5z

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

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) replicated three studies (1, 2 and 5) 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.

 

Results provided mixed support for the original findings. Consistent with Soman (2001), the sunk cost effect for money was reliably stronger than for time in Study 1; however, sunk costs for both money and time were comparable in Study 2 (and if anything, slightly stronger for time). The indirect replication of Study 5 from Soman (2001) found that the sunk cost effect for time was not significantly influenced by accounting for time, either using education or by highlighting opportunity costs. Robustness checks confirmed the main preregistered outcomes while also ruling out a range of potential alternative explanations. Overall, the results suggest that that sunk cost effects for time are more context-dependent and empirically volatile than sunk costs for money.
 
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 was used to answer the research question was 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 and extensions Registered Report of Soman (2001), acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/q9s6p