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

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

Paid loot boxes – items bought for real-world money that offer randomised rewards – are a prevalent feature of contemporary video games (Zendle et al., 2020). Because they employ random chance to provide rewards after spending real money, loot boxes have been considered a form of gambling, raising concerns about risk of harm to children and other vulnerable users. In response, some countries have taken legal steps to regulate and even ban the use of loot boxes, with only limited success so far (Xiao, 2022). At the same time, the Entertainment Software Rating Board (ESRB) and PEGI (Pan-European Game Information) now expect games that contain loot boxes to be marked with warning labels that, in theory, will enable users (including parents) to make more informed decisions. These requirements by ESRB/PEGI are not legally binding and may be considered a form of industry self-regulation.

 

In the current study, Xiao (2023) investigated the effectiveness of self-regulation in the use of loot box labels. Study 1 examined the consistency of warning labels by the ESRB and PEGI, with the expectation that if self-regulation works as it should then these labels should always (or nearly always) co-occur. Study 2 established the compliance rate for labelling among popular games that are known to contain loot boxes, with a rate of ≥95% considered to be successful.

 

The results of both studies reveal deficiences in industry self-regulation. The consistency rate of warning labels by the ESRB and PEGI was just 39.4% in preregistered analyses, rising to 83.9% in an unregistered exploratory analysis that took into account industry responses to the findings. Even at this upper bound, this rate is lower than expected by complete (or near-complete) consistency. The results of Study 2 indicate that only 29% of games on the Google Play Store known to contain loot boxes were accurately labelled, indicating that 71% were non-compliant with industry requirements.

 

Following careful evaluation, the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation.
 
URL to the preregistered Stage 1 protocol: https://osf.io/e6qbm

 

Level of bias control achieved: Level 3. At least some data/evidence that was used to the answer the research question had been previously accessed by the authors (e.g. downloaded or otherwise received), but the authors certifed that they had not yet observed ANY part of the data/evidence prior to in-principle-acceptance.
 
List of eligible PCI RR-friendly journals:

 

 

 

1. Zendle, D., Meyer, R., Cairns, P., Waters, S., & Ballou, N. (2020). The prevalence of loot boxes in mobile and desktop games. Addiction, 115(9), 1768-1772. https://doi.org/10.1111/add.14973

2. Xiao, L. Y. (2022). Breaking Ban: Belgium’s ineffective gambling law regulation of video game loot boxes. Stage 2 Registered Report, acceptance of Version 2 by Peer Community in Registered Reports. https://doi.org/10.31219/osf.io/hnd7w 

 

3. Xiao, L. Y. (2023). Beneath the label: Unsatisfactory compliance with ESRB, PEGI, and IARC industry self-regulation requiring loot box presence warning labels by video game companies, acceptance of Version 2 by Peer Community in Registered Reports. https://doi.org/10.31219/osf.io/asbcg

Human meat consumption is associated with a variety of risks to health, animal welfare, sustainability, and the environment (including greenhouse gas emissions and loss of biodiversity), prompting a growing research effort to develop psychological interventions to reduce it and encourage alternative diets. At the same time, although meat consumption remains the majority choice in the UK, its prevalence is declining, with the proportion of vegetarians and vegans increasing substantially over the last two decades.

 

One potential tool to accelerate behaviour change is to expose people to “dynamic norm” messaging, which, rather than providing static descriptive information about the prevalence of a desired behaviour, emphasises how the desired behaviour is changing over time so that people can begin to conform to the emerging trend. Although promising in theory, previous research offers mixed evidence on the effectiveness of dynamic norms in encouraging a reduction in meat consumption, with some studies suggesting benefits and others showing no effect or even counterproductive effects. The methodological rigour of some studies is also in question.

 

In the present study, Aldoh et al. (2023) investigated the effectiveness of dynamic norm information (compared to static norms) on several indicators of meat consumption, including interest, attitudes, and intentions toward reducing meat consumption, as well as self-reported meat consumption itself. Using an online sample of ~1500 participants, the authors also tested the role of visual cues (including data trend graphics) in causing any effects and explored the potential longevity of the intervention over a period of 7 days. Results revealed moderate evidence for no net effect of dynamic (compared to static) norm information on meat consumption outcomes, nor any positive change over the 7-day period. However, the addition of visual cues enhanced the effect of dynamic norm messages, suggesting potential avenues for increasing the potency of future messaging interventions.

 

The Stage 2 manuscript was evaluated over one round of in-depth review, following which the recommender judged that the manuscript met the Stage 2 criteria and awarded a positive recommendation.

 

 

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. 

 

 

  

1. Aldoh, A., Sparks, P. & Harris, P. R. (2023). Communicating dynamic norm information [Stage 2]. Acceptance of Version 1 by Peer Community in Registered Reports. https://doi.org/10.31234/osf.io/tac3j

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