David J. Hauser

People erroneously think that things they know little about improve over time. We propose that, due to salient cultural narratives, improvement is a highly accessible expectation that leads people to presume improvement in the absence of diagnostic information. Five studies investigated an improvement default: a general tendency to presume improvement even in self-irrelevant domains. Participants erroneously presumed improvement over esoteric historical time periods associated with decline (Study 1). Participants arranged a stranger’s experiences to produce trends of improvement (Study 2). Participants presumed improvement for a fictional city when given no diagnostic information about it (Study 3). Finally, participants who perceived more past improvement were less supportive of policies that may precipitate further improvement (Study 4). Implications for consequences, such as complacency toward improving inequality, are discussed.

Is disease risk perception accurately calibrated among the unvaccinated? People shift their attitudes to rationalize their choices, so those who choose to be unvaccinated may be motivated to feel less at risk. In three studies (total N = 1446), we asked Americans how worried they were about catching/spreading influenza and COVID‐19 and whether they were vaccinated against those diseases. Unvaccinated participants felt less at risk of catching/spreading the diseases they were unvaccinated against than vaccinated participants. For instance, unvaccinated participants felt ∼24% less at risk of catching/spreading COVID‐19 and had ∼28% stronger intention to engage in activities that carried a high risk of COVID‐19 transmission (Study 3). Overall, those who choose to be the most vulnerable to disease feel and act the least vulnerable.

Behavioral scientists looking to run online studies are confronted with a bevy of options. Where to recruit participants? Which tools to use for survey creation and study management? How to maintain data quality? In this tutorial, we highlight the unique capabilities of market-research panels and demonstrate how researchers can effectively sample from such panels. Unlike the microtask platforms most academics are familiar with (e.g., MTurk and Prolific), market-research panels have access to more than 100 million potential participants worldwide, provide more representative samples, and excel at demographic targeting. However, efficiently gathering data from online panels requires integration between the panel and a researcher’s survey in ways that are uncommon on microtask sites. For example, panels allow researchers to target participants according to preprofiled demographics (“Level 1” targeting, e.g., parents) and demographics that are not preprofiled but are screened for within the survey (“Level 2” targeting, e.g., parents of autistic children). In this article, we demonstrate how to sample hard-to-reach groups using market-research panels. We also describe several best practices for conducting research using online panels, including setting in-survey quotas to control sample composition and managing data quality. Our aim is to provide researchers with enough information to determine whether market-research panels are right for their research and to outline the necessary considerations for using such panels.

We like people and objects more when they are described in positive than in negative terms. But even seemingly neutral words can elicit positive or negative responses. This is the case for words that predominantly occur alongside positive (or negative) words in natural language. Despite lacking positivity/negativity when evaluated in isolation, such semantically prosodic words activate the evaluative associations of their usual company, which can color judgment in unrelated domains. For example, people are more likely to infer that “endocrination” (a fictional medical outcome) is negative when it is “caused” (a word with negative semantic prosody) rather than “produced” (a synonymous word without semantic prosody). We review what is known about the influence of semantically prosodic words and highlight their importance for judgment and decision making.

Maintaining data quality on Amazon Mechanical Turk (MTurk) has always been a concern for researchers. These concerns have grown recently due to the bot crisis of 2018 and observations that past safeguards of data quality (e.g., approval ratings of 95%) no longer work. To address data quality concerns, CloudResearch, a third-party website that interfaces with MTurk, has assessed ~165,000 MTurkers and categorized them into those that provide high- (~100,000, Approved) and low- (~65,000, Blocked) quality data. Here, we examined the predictive validity of CloudResearch’s vetting. In a pre-registered study, participants (N = 900) from the Approved and Blocked groups, along with a Standard MTurk sample (95% HIT acceptance ratio, 100+ completed HITs), completed an array of data-quality measures. Across several indices, Approved participants (i) identified the content of images more accurately, (ii) answered more reading comprehension questions correctly, (iii) responded to reversed coded items more consistently, (iv) passed a greater number of attention checks, (v) self-reported less cheating and actually left the survey window less often on easily Googleable questions, (vi) replicated classic psychology experimental effects more reliably, and (vii) answered AI-stumping questions more accurately than Blocked participants, who performed at chance on multiple outcomes. Data quality of the Standard sample was generally in between the Approved and Blocked groups. We discuss how MTurk’s Approval Rating system is no longer an effective data-quality control, and we discuss the advantages afforded by using the Approved group for scientific studies on MTurk.

In everyday language, concepts appear alongside (i.e., collocate with) related concepts. Societal biases often emerge in these collocations; e.g., female (vs. male) names collocate with art- (vs. science-) related concepts, and African American (vs. White American) names collocate with negative (vs. positive) concepts. It is unknown whether such collocations merely reflect societal biases or contribute to them. Concepts that are themselves neutral in valence but nevertheless collocate with valenced concepts provide a unique opportunity to address this question. For example, when asked, most people evaluate the concept “cause” as neutral, but “cause” is frequently followed by negative concepts (e.g., death, pain, and trouble). We use such semantically prosodic concepts to test the influence of collocation on the emergence of implicit bias: do neutral concepts that frequently collocate with valenced concepts have corresponding implicit bias? In evaluative priming tasks, participants evaluated positive/negative nouns (Study 1) or pictures (Study 2) after seeing verb primes that were (a) strongly valenced (e.g., hate and comfort), (b) neutral in valence but collocated with valenced concepts in corpora (e.g., ease and gain), or (c) neutral in valence and not collocated with valenced concepts in corpora (e.g., reply and describe). Throughout, neutral primes with positive (negative) collocates facilitated the evaluation of positive (negative) targets much like strongly valenced primes, whereas neutral primes without valenced collocates did not. That neutral concepts with valenced collocates parallel the influence of valenced concepts suggests that their collocations in natural language may be sufficient for fostering implicit bias. Societal implications of the causal embedding hypothesis are discussed.

Natural disasters are often described as having antagonistic qualities (e.g., wildfires ravage). The information deficit model presumes that when people assess the risk of weather hazards, they ignore irrelevant metaphoric descriptors. However, metaphoric frames affect reasoning. The current research assessed whether antagonist metaphors for natural disasters affect perceptions of the risk they pose. Three studies (N = 1,936) demonstrated that participants forecasted an antagonist-framed natural hazard as being more severe, and intended to evacuate more often, than a literal-framed natural hazard. Thus, the metaphorical language used to discuss natural disasters deserves consideration in the development of effective risk communication.