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1. Do People Prescribe (Over)Optimism?

   Miller, J. E. (January 2021, Psychological science)

   Brandt, M.; Bauer, P. (Ed.)

   Past work has suggested that people prescribe optimism—believing it is better to be optimistic, instead of accurate or pessimistic, about uncertain future events. Here, we identified and addressed an important ambiguity about whether those findings reflect an endorsement of biased beliefs—i.e., whether people prescribe likelihood estimates that reflect overoptimism. In three studies, participants (total N = 663 U.S. university students) read scenarios about protagonists facing uncertain events with a desired outcome. Results replicated prescriptions of optimism when using the same solicitations as in past work. However, we found quite different prescriptions when using alternative solicitations that asked about potential bias in likelihood estimations and that did not involve vague terms like “optimistic.” Participants generally prescribed being optimistic, feeling optimistic, and even thinking optimistically about the events, but they did not prescribe overestimating the likelihood of those events. 

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2. Do people desire optimism from others during a novel global crisis?

   https://doi.org/10.1002/bdm.2362  

   Miller, Jane E.; Strueder, Jeremy D.; Park, Inkyung; Windschitl, Paul D. (November 2023, Journal of Behavioral Decision Making)

   Abstract During a global crisis, does the desire for good news also mean an endorsement of an optimistic bias? Five pre‐registered studies, conducted at the start of the COVID pandemic, examined people's lay prescriptions for thinking about uncertainty—specifically whether they thought forecasters should be optimistic, realistic, or pessimistic in how they estimated key likelihoods. Participants gave prescriptions for forecasters with different roles (e.g., self, family member, and public official) and for several key outcomes (e.g., contracting COVID and vaccine development). Overall, prescribed optimism was not the norm. In fact, for negative outcomes that were of high concern, participants generally wanted others to have a pessimistic bias in how they estimated likelihoods. For positive outcomes, people favored more accurate estimation. These patterns held regardless of the assumed forecaster's role. A common justification for advocating for a pessimistic bias in forecasts was to increase others' engagement in protective or preventative behaviors. 

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3. The effects of tool comparisons when estimating the likelihood of task success.

   Li, S. (January 2021, Judgment and decision making)

   Baron, J. (Ed.)

   People often use tools for tasks, and sometimes there is uncertainty about whether a given task can be completed with a given tool. This project explored whether, when, and how people’s optimism about successfully completing a task with a given tool is affected by the contextual salience of a better or worse tool. In six studies, participants were faced with novel tasks. For each task, they were assigned a tool but also exposed to a comparison tool that was better or worse in utility (or sometimes similar in utility). In some studies, the tool comparisons were essentially social comparisons, because the tool was assigned to another person. In other studies, the tool comparisons were merely counterfactual rather than social. The studies revealed contrast effects on optimism, and the effect worked in both directions. That is, worse comparison tools boosted optimism and better tools depressed optimism. The contrast effects were observed regardless of the general type of comparison (e.g., social, counterfactual). The comparisons also influenced discrete decisions about which task to attempt (for a prize), which is an important finding for ruling out superficial scaling explanations for the contrast effects. It appears that people fail to exclude irrelevant tool-comparison information from consideration when assessing their likelihood of success on a task, resulting in biased optimism and decisions. 

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4. Combining forecasts from advisors: The impact of advice independence and verbal versus numeric format.

   https://doi.org/10.1037/xge0001611  

   Strueder, Jeremy D; Windschitl, Paul D (August 2024, Journal of Experimental Psychology: General)

   Past research on advice-taking has suggested that people are often insensitive to the level of advice independence when combining forecasts from advisors. However, this has primarily been tested for cases in which people receive numeric forecasts. Recent work by Mislavsky and Gaertig (2022) shows that people sometimes employ different strategies when combining verbal versus numeric forecasts about the likelihood of future events. Specifically, likelihood judgments based on two verbal forecasts (e.g., "rather likely") are more often extreme (relative to the forecasts) than are likelihood judgments based on two numeric forecasts (e.g., "70% probability"). The goal of the present research was to investigate whether advice-takers' use of combination strategies can be sensitive to advice independence when differences in independence are highly salient and whether sensitivity to advice independence depends on the format in which advice is given. In two studies, we found that advice-takers became more extreme with their own likelihood estimate when combining forecasts from advisors who use separate evidence, as opposed to the same evidence. We also found that two verbal forecasts generally resulted in more extreme combined likelihood estimates than two numeric forecasts. However, the results did not suggest that sensitivity to advice independence depends on the format of advice. 

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5. Keep it Simple: How Visual Complexity and Preferences Impact Search Efficiency on Websites

   Amanda Baughan, Tal August (April 2020, Human factors in computing systems)

   Past research has shown that people prefer different levels of visual complexity in websites: While some prefer simple websites with little text and few images, others prefer highly complex websites with many colors, images, and text. We investigated whether users’ visual preferences reflect which website complexity they can work with most efficiently. We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests. 

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