Scientists have started to explore whether novel artificial intelligence (AI) tools based on large language models, such as GPT-4, could support the scientific peer review process. We sought to understand (i) whether AI versus human reviewers are able to distinguish between made-up AI-generated and human-written conference abstracts reporting on actual research, and (ii) how the quality assessments by AI versus human reviewers of the reported research correspond to each other. We conducted a large-scale field experiment during a medium-sized scientific conference, relying on 305 human-written and 20 AI-written abstracts that were reviewed either by AI or 217 human reviewers. The results show that human reviewers and GPTZero were better in discerning (AI vs. human) authorship than GPT-4. Regarding quality assessments, there was rather low agreement between both human–human and human–AI reviewer pairs, but AI reviewers were more aligned with human reviewers in classifying the very best abstracts. This indicates that AI could become a prescreening tool for scientific abstracts. The results are discussed with regard to the future development and use of AI tools during the scientific peer review process.

Past research suggested that assimilation (i.e., the tendency to exaggerate the similarity between one’s ideological position and the position of a preferred political object) is stronger than contrast (i.e., the tendency to exaggerate the dissimilarity between one’s ideological position and the position of a non-preferred political object). However, critiques of this research argue that this conclusion is unwarranted because it is biased toward assimilation appearing stronger than contrast. In the current study, we examine the ideological judgments of American presidential candidates between 1972 and 2020 and analyze all available subjects (in contrast to previous studies that relied primarily on data collected in the 1970s and 1980s and analyzed only subjects who actually voted), and show that, in these years, contrast was stronger than assimilation. We also show that during these years, there was very little change in assimilation but a substantial increase in contrast. We attribute this change to increased polarization among the American electorate.

What role does reasoning about moral principles play in people’s judgments about what is right or wrong? According to one view, reasoning usually plays little role. People tend to do what suits their self-interests and concoct moral reasons afterward to justify their own behavior. Thus, in this view, people are far more forgiving of their own violations than of others’ violations. According to a contrasting view, principled reasoning generally guides judgments and decisions about our own and others’ actions. This view predicts that people usually can, and do, articulate the principles that guide their moral judgments and decisions. The present research examined a phenomenon at the center of these debates: students’ evaluations of academic cheating. Across three studies, we used structured interviews and online surveys to examine first- and third-party judgments and reasoning about cheating events. Third-party scenarios were derived from students’ own accounts of cheating events and manipulated based on the reasons students provided. Findings supported the view that reasoning is central to evaluations of cheating. Participants articulated reasons consistent with their judgments about their own and others’ actions. The findings advance classic debates about reasoning in morality and exemplify a paradigm that can bring further advances.

Research on advice taking has demonstrated a phenomenon of egocentric discounting: people weight their own estimates more than advice from others. However, this research is mostly conducted in highly controlled lab settings with low or no stakes. We used unique data from a game show on Norwegian television to investigate advice taking in a high stakes and highly public setting. Parallel to the standard procedure in judge–advisor systems studies, contestants give numerical estimates for several tasks and solicit advice (another estimate) from three different sources during the game. The average weight of advice was 0.58, indicating that contestants weighted advice more than their own estimates. Of potential predictors of weight of advice, we did not detect associations with the use of intuition (e.g., gut feeling, guessing) and advice source (family, celebrities, average of viewers from hometown), but own estimation success (the proportion of previous rounds won) was associated with less weight of advice. Solicitation of advice was associated with higher stakes. Together with the relatively high weight on advice, this suggests that participants considered the advice valuable. On average, estimates did not improve much after advice taking, and the potential for improvement by averaging estimates and advice was negligible. We discuss different factors that could contribute to these findings, including stakes, solicited versus unsolicited advice, task difficulty, and high public scrutiny. The results suggest that highly controlled lab studies may not give an accurate representation of advice taking in high stakes and highly public settings.

This study examined the sour grapes/sweet lemons rationalization through 2 conditions: ‘attainable’ (sweet lemons) and ‘unattainable’ (sour grapes), reflecting China’s 2019-nCoV vaccination strategy. The aim was to find ways to change people’s beliefs and preferences regarding vaccines by easing their safety concerns and encouraging more willingness to get vaccinated. An online survey was conducted from January 22 to 27, 2021, with 3,123 residents across 30 provinces and municipalities in the Chinese mainland. The direction of belief and preference changed in line with the sour grapes/sweet lemons rationalization. Using hypothetical and real contrasts, we compared those for whom the vaccine was relatively unattainable (‘sour grapes’ condition) with those who could get the vaccine easily (‘sweet lemons’). Whether the vaccine was attainable was determined in the early stage of the vaccine roll-out by membership in a select group of workers that was supposed to be vaccinated to the greatest extent possible, or, by being in the second stage when the vaccine was available to all. The attainable conditions demonstrated higher evaluation in vaccine safety, higher willingness to be vaccinated, and lower willingness to wait and see. Hence, we propose that the manipulation of vaccine attainability, which formed the basis of the application of sour grapes/sweet lemons rationalization, can be utilized as a means to manipulate the choice architecture to nudge individuals to ease vaccine safety concerns, reducing wait-and-see tendencies, and enhancing vaccination willingness. This approach can expedite universal vaccination and its associated benefits in future scenarios resembling the 2019-nCoV vaccine rollout.

Most research on intertemporal choice has examined choices between smaller, sooner gains and larger, later gains. A much smaller number of papers have examined intertemporal choices for losses. In this article, we explore whether mixed-sign choices with both gains and losses may better correlate with real-world behaviors. In two high-powered studies (pilot: N = 3,200; main study: N = 7,000), participants completed one of four normatively equivalent measures consisting of pure gain, pure loss, or mixed sign (Gain-Now-Loss-Later or Loss-Now-Gain-Later) intertemporal choices. Participants also self-reported a large number of demographic measures and real-world choice behaviors thought to be linked to intertemporal choice. The results indicate that (1) mixed-sign intertemporal choices yield more patient time preferences than pure-gain choices but less patient than pure-loss choices and (2) pure-gain intertemporal choices yield equivalent or superior predictive power across a range of real-world intertemporal choice behaviors.

The literature on emotion and risk-taking is large and heterogeneous. Whereas some studies have found that positive emotions increase risk-taking and negative emotions increase risk aversion, others have found just the opposite. In this study, we investigated this question in the context of a risky decision-making task with embedded high-resolution sampling of participants’ subjective emotional valence. Across two large-scale experiments (N = 329 and 524), we consistently found evidence for a negative association between self-reported emotional valence and risk-taking behaviors. That is, more negative subjective affect was associated with increased risk-seeking, and more positive subjective affect was associated with increased risk aversion. This effect was evident both when we compared participants with different levels of mean emotional valence as well as when we considered within-participant emotional fluctuations over the course of the task. Prospect-theoretic computational modeling analyses suggested that both between- and within-participant effects were driven by an effect of emotional valence on the curvature of the subjective utility function (i.e., increased risk tolerance in more negative emotional states), as well as by an effect of within-person emotion fluctuations on loss aversion. We interpret findings in terms of a tendency for participants in negative emotional states to choose high-risk, high-reward options in an attempt to improve their emotional state.