Decision-making based on limited information is a common occurrence. Whether it is the possibility of a cheaper product elsewhere or the unknown qualifications of election candidates, people are regularly forced to make a decision under ignorance or uncertainty. In such situations, information about certain events is unavailable or too costly to acquire and people rely on subjective probability allocation to guide decision-making processes. This allocation seems to result in what is known as ignorance priors, i.e., decision-makers assigning equal probabilities to each possible outcome within a given set. How events are grouped or partitioned is often subjective and may influence probability judgments and subsequent decisions. In such cases, the way the choices within a choice set are presented may shape the perceived likelihood of different outcomes. Understanding the impact of partitioning on probability estimation is crucial for both psychological and economic theories of judgment and decision.

 

The question of evaluating probabilities under uncertainty has received much attention in the psychology and economics literature over the past decades given the wide range of possible applications. In the current work, Ding and Feldman (2024) seek to replicate one of the foundational works on the topic: Fox and Rottenstreich (2003). In the original work, the authors provided exploratory evidence indicating that the framing of a situation affects the way individuals perceive probabilities of possible outcomes. They showed that people assigned uniform probabilities to sets of events described in a problem, such that the way the events are described partly determines people’s partitioning of those events and evaluations of the probabilities of the possible outcomes. Additionally, this partitioned framing affected judgments both under conditions of ignorance (where individuals have no information and rely solely on uniform probability assignments) and uncertainty (where individuals have some information but still rely on heuristics influenced by partitioning). This suggests that priors resulting from the inference of available evidence are sometimes partly contaminated by partitioning bias, affecting both uninformed and partially informed decision-making processes. As a consequence, the partitioning of events into different subsets might lead to varying evaluations of a single situation, resulting in inconsistencies and poorly calibrated probability assessments.

 

Ding and Feldman (2024) aim to replicate Studies 1a, 1b, 3, and 4 from Fox and Rottenstreich (2003). Their close replication will rely on original data (US participants, Prolific, N=600, not collected yet) with a large statistical power (>95%). Their replication aims to examine whether the partitioned framing affects prior formation under ignorance (Studies 1a, 1b, and 4) and uncertainty (Study 3). In addition, the authors propose an extension examining estimations of alternative event(s) contrasting estimations of the probabilities of events happening versus of events not happening.

 

The Stage 1 manuscript was evaluated by two external reviewers and the recommender. Based on detailed responses to the reviewers' and the recommender’s comments, the recommender judged that the manuscript met the Stage 1 criteria and therefore awarded in-principle acceptance (IPA).

 

 

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.

 

 

 

 

1. Ding, K. & Feldman, G. (2024). Revisiting Partition Priming in judgment under uncertainty:
Replication and extension Registered Report of Fox and Rottenstreich (2003). In principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/px6vb

 

2. Fox, C. R. & Rottenstreich, Y. (2003). Partition priming in judgment under uncertainty. Psychological Science, 14, 195-200. https://doi.org/10.1111/1467-9280.02431

When offered a range of options and asked to make multiple selections, how do people choose? Over the last 30 years, a key finding to emerge from behavioural economics is that people distribute their choices more evenly than would be considered optimal – a phenomenon termed “diversification bias” or the “diversification heuristic” (Read & Loewenstein, 1995). For example, when filling a plate from a buffet, you might be inclined to choose a relatively even amount of everything on offer, even when you prefer some foods over others. Similarly, when allocating savings among different investment options, people are prone to spreading their money more evenly than would maximise utility.

 

In an influential study, Fox et al. (2005) found that diversification bias can be shaped by so-called “partition dependence” – the tendency to allocate resources differently across options depending on how they are subjectively grouped. Such groupings could be arbitrary; so, for example, to return to the buffet example, people might diversify across high-level categories such as cooked and uncooked, savoury and sweet, or surf and turf, and then diversify across the options within those categories. The nature of level of these subjective (and potentially arbitrary) categorisations can strongly influence the final allocation of resources. Diversification bias and partition dependence have important implications for basic theory in judgment and decision-making as well as applications in behavioral economics and finance.

 

In the current study, Li and Feldman (2022) propose to replicate Studies 1, 2 and 5 from Fox et al. (2002) in a large online sample. In particular, they plan to ask how partitioning influences the allocation of choices between options, and the extent to which partition dependence is reduced in people with greater relevant expertise. The authors also propose extending the original study to explore individual differences in the desire for choice diversity as predictors of partition dependence.

 

The Stage 1 manuscript was evaluated over one round 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/bx8vq

 

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

 
References

 

1. Read, D., & Loewenstein, G. (1995). Diversification bias: Explaining the discrepancy in variety seeking between combined and separated choices. Journal of Experimental Psychology: Applied, 1, 34-49. https://doi.org/10.1037/1076-898X.1.1.34

 

2. Fox, C. R., Ratner, R. K., & Lieb, D. S. (2005). How subjective grouping of options influences choice and allocation: Diversification bias and the phenomenon of partition dependence. Journal of Experimental Psychology: General, 134, 538-551. https://doi.org/10.1037/0096-3445.134.4.538

  
3. Li, M. Y. & Feldman, G. (2022). Revisiting diversification bias and partition dependence: 
Replication and extension of Fox, Ratner, and Lieb (2005) Studies 1, 2, and 5, in principle acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/bx8vq

The Psychological Research Preregistration-Quantitative Template has been created in 2022 to provide more structure and detail to preregistrations. The goal of the current study is to test if the PRP-QUANT template indeed provides greater restriction of the flexibility in a study for preregistered hypotheses than other existing templates. This question is important because one concern that has been raised about the practice of preregistration is that the quality of preregistrations is often low. Metascientific research has shown that preregistrations are often of low quality (Bakker et al., 2020), and hypothesis tests from preregistrations are still selectively reported (van den Akker, van Assen, Enting, et al., 2023). It is important to improve the quality of preregistrations, and if a better template can help, it is a cost-effective approach to improve quality if the wider adoption of the better template can be promoted. 

 

In the current study, Spitzer and Mueller (2024) will follow the procedure of a previous meta-scientific study by Heirene et al. (2021). 74 existing preregistrations with the PRP-QUANT template are available, and will be compared with an existing dataset coded by Bakker and colleagues (2020). The sample size is limited, but allows detecting some differences that would be considered large enough to matter, even though there might be smaller differences that would not be detectable based on the currently available sample size. Nevertheless, given that there is a need for improvement, even preliminary data might already be useful to provide tentative recommendations. Restrictiveness will be coded in 23 items, and adherence to or deviations from the preregistration are coded as well. As such deviations are common, the question whether this template reduced the likelihood of deviations is important. Two coders will code all studies. 

 

The study should provide a useful initial evaluation of the PRP-QUANT template, and has the potential to have practical implications if the PRP-QUANT template shows clear benefits. Both authors have declared COI's related to the PRP-QUANT template, making the Registered Report format a fitting approach to prevent confirmation bias from influencing the reported results. 

 

This Stage 1 manuscript was evaluated over two rounds of in-depth review by two expert reviewers and the recommender. After the revisions, 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/vhezj
 
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. van den Akker, O. R., van Assen, M. A. L. M., Bakker, M., Elsherif, M., Wong, T. K., & Wicherts, J. M. (2023). Preregistration in practice: A comparison of preregistered and non-preregistered studies in psychology. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02277-0

 

2. Bakker, M., Veldkamp, C. L. S., Assen, M. A. L. M. van, Crompvoets, E. A. V., Ong, H. H., Nosek, B. A., Soderberg, C. K., Mellor, D., & Wicherts, J. M. (2020). Ensuring the quality and specificity of preregistrations. PLOS Biology, 18(12), e3000937. https://doi.org/10.1371/journal.pbio.3000937

 

3. Spitzer, L. & Mueller, S. (2024). Stage 1 Registered Report: Restriction of researcher degrees of freedom through the Psychological Research Preregistration-Quantitative (PRP-QUANT) Template. In principle acceptance of Version 3 by Peer Community in Registered Reports. https://osf.io/vhezj

 

4. Heirene, R., LaPlante, D., Louderback, E. R., Keen, B., Bakker, M., Serafimovska, A., & Gainsbury, S. M. (2021). Preregistration specificity & adherence: A review of preregistered gambling studies & cross-disciplinary comparison. PsyArXiv. https://doi.org/10.31234/osf.io/nj4es

Social media is a popular tool for online discussion and debate, bringing with it various forms of hostile interactions –  from offensive remarks and insults, to harassment and threats of physical violence. The nature of such online toxicity has been well studied, but much remains to be understood regarding strategies to reduce it. Existing theory and evidence suggests that a range of responses – including those that emphasise prosociality and empathy – might be effective at mitigating online toxicity. But do such strategies work in practice?

 

In the current study, Young Reusser et al (2023) tested the effectiveness of three types of responses to online toxicity – benevolent correction (including disagreement), benevolent going along (including joking/agreement) and retaliation (additional toxicity) – on how able participants feel to contribute to conversations, their belief that the toxicity would be reduced by the intervention, and their belief that justice had been restored.

 

The results showed the benevolent correction – while an uncommon strategy in online communities – was most effective in helping participants feel freer to contribute to online discussions. Benevolent correction was also the preferred approach for discouraging toxicity and restoring justice. Overall, the findings suggest that responding to toxic commenters with empathy and understanding while (crucially) also correcting their toxicity may be an effective intervention for bystanders seeking to improve the health of online interaction. The authors note that future research should focus on whether benevolent correction actually discourages toxicity, which wasn't tested in the current experiment, and if so how the use of benevolent corrections might be encouraged.

 

Following one round of review and revisions, 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/hfjnb

 

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. Young Reusser, A. I., Veit, K. M., Gassin, E. A., & Case, J. P. (2023). Responding to Online Toxicity: 
Which Strategies Make Others Feel Freer to Contribute, Believe That Toxicity Will Decrease, and Believe that Justice Has Been Restored? [Stage 2 Registered Report] Acceptance of Version 2 by Peer Community in Registered Reports. https://osf.io/k46e8

Revisiting a true classic, this registered replication report addresses Kahneman and Tversky’s (1973) introduction of the representativeness heuristic. The heuristic refers to deviations of judgments from normative evaluations of the evidence when the stimulus fits to a prototype. For instance, when an individual is described by features stereotypically associated with a certain target group (e.g., a person who attends dance training several times a week and has a passion for singing and performing), likelihood judgments that the individual belongs to a target group (K-Pop artists) compared to a non-target group (e.g., accountants) are inflated. 

The impact of the original research on the field is clearly immense and long-lasting. All the better that a systematic replication attempt is being undertaken in this registered report, which addresses studies 1 through 7 of Kahneman and Tversky’s classic 1973 paper. Chan and Feldman (2024) propose a well-powered online study, in which all studies from the original article are presented to participants within-subjects. The materials are carefully constructed and closely documented in the accompanying OSF project, where in-depth information on planned data analyses is supported with a simulated dataset.  

The Stage 1 manuscript was evaluated over three 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).

 

 

Level of bias control achieved: Level 6. Data collection commenced during the later part of Stage 1 peer review; however, since no substantive changes to the design were made after this point, the risk of bias due to prior data observation remains zero and the manuscript therefore qualifies for Level 6.

 

 

 

1. Chan, H. C. & Feldman, G. (2024). Representativeness heuristic in intuitive predictions: Replication Registered Report of problems reviewed in Kahneman and Tversky (1973). In principle acceptance of Version 5 by Peer Community in Registered Reports. https://osf.io/er2cq

 

2. Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237–251. https://doi.org/10.1037/h0034747