Should We Think of Early Career Cheaters as Capable of Stewardship?

Resistance to acknowledging and curbing cheating should be seen as expressing academic organizations' dereliction of their tacit early career health professional self-regulatory duties. Cheating among students and trainees deserves ethical attention, scrutiny, and self-regulatory responses because cheating behaviors express characterological vices that undermine trust and trustworthiness, which, among other virtues, are key to good stewardship and other duties of health professionals.

Deception abilities emerged in large language models.

Large language models (LLMs) are currently at the forefront of intertwining AI systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, but were nonexistent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can trigger misaligned deceptive behavior. GPT-4, for instance, exhibits deceptive behavior in simple test scenarios 99.16% of the time (P < 0.001). In complex second-order deception test scenarios where the aim is to mislead someone who expects to be deceived, GPT-4 resorts to deceptive behavior 71.46% of the time (P < 0.001) when augmented with chain-of-thought reasoning. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Misunderstanding the harms of online misinformation.

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

Development and validation of a questionnaire about reasons for academic cheating by nursing students: A cross-sectional study.

Cheating behavior is spreading among nursing students worldwide, necessitating the development of a validated questionnaire evaluating the reasons for such behavior. Nursing students (N = 482) from 2 universities in Saudi Arabia participated in this observational study. A survey containing items on socio-demographics and the 33-item Reasons for Cheating Scale (RCS) was completed by the respondents. The RCS had a 1-factor structure; the model fit indices were similar between the 1-, 2-, and 3-factor models, but the inter-factor correlations were too high for the 2- and 3-factor models. The measures of the quality of the factor score estimates were as follows: factor determinacy index, 0.987; expected a posteriori marginal reliability, 0.974; sensitivity ratio, 6.178; and expected percentage of true differences, 97.3%. The measures of the closeness to unidimensionality for the overall RCS were as follows: unidimensional congruence, 0.957; explained common variance, 0.875; and mean item residual absolute loading, 0.223. The intraclass correlation coefficient and McDonald's omega were 0.96 (CI: 0.93-0.98) and 0.962 (95% CI: 0.958-0.967), respectively. The severity score, infit, and outfit ranged from -0.847 to -2.015, 0.813 to 1.742, and 0.837 to 1.661, respectively. For all RCS items, the thresholds ranked τi1 < τi2 < τi3 < τi4 and showed invariance between the sexes. The RCS showed robust psychometric validity for both classical and item response theory parameters. It also had excellent test-retest reliability, internal consistency, item discrimination, factorial validity, measurement invariance, and ordered threshold level for the responses. Therefore, the RCS is a valid and reliable tool for assessing cheating behavior among nursing students.

Softly empowering a prosocial expert in the family: lasting effects of a counter-misinformation intervention in an informational autocracy.

The present work is the first to comprehensively analyze the gravity of the misinformation problem in Hungary, where misinformation appears regularly in the pro-governmental, populist, and socially conservative mainstream media. In line with international data, using a Hungarian representative sample (Study 1, N = 991), we found that voters of the reigning populist, conservative party could hardly distinguish fake from real news. In Study 2, we demonstrated that a prosocial intervention of ~ 10 min (N = 801) helped young adult participants discern misinformation four weeks later compared to the control group without implementing any boosters. This effect was the most salient regarding pro-governmental conservative fake news content, leaving real news evaluations intact. Although the hypotheses of the present work were not preregistered, it appears that prosocial misinformation interventions might be promising attempts to counter misinformation in an informational autocracy in which the media is highly centralized. Despite using social motivations, it does not mean that long-term cognitive changes cannot occur. Future studies might explore exactly how these interventions can have an impact on the long-term cognitive processing of news content as well as their underlying neural structures.

Supersharers of fake news on Twitter.

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. In this work, we identify 2107 registered US voters who account for 80% of the fake news shared on Twitter during the 2020 US presidential election by an entire panel of 664,391 voters. We found that supersharers were important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers had a significant overrepresentation of women, older adults, and registered Republicans. Supersharers' massive volume did not seem automated but was rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many.

A broader view of misinformation reveals potential for intervention.

Misleading claims from credible sources can be more damaging than blatant falsehoods.

Detecting lies in investigative interviews through the analysis of response latencies and error rates to unexpected questions.

In this study, we propose an approach to detect deception during investigative interviews by integrating response latency and error analysis with the unexpected question technique. Sixty participants were assigned to an honest (n = 30) or deceptive group (n = 30). The deceptive group was instructed to memorize the false biographical details of a fictitious identity. Throughout the interviews, participants were presented with a randomized sequence of control, expected, and unexpected open-ended questions about identity. Responses were audio recorded for detailed examination. Our findings indicate that deceptive participants showed markedly longer latencies and higher error rates when answering expected (requiring deception) and unexpected questions (for which premeditated deception was not possible). Longer response latencies were also observed in participants attempting deception when answering control questions (which necessitated truthful answers). Moreover, a within-subject analysis highlighted that responding to unexpected questions significantly impaired individuals' performance compared to answering control and expected questions. Leveraging machine-learning algorithms, our approach attained a classification accuracy of 98% in distinguishing deceptive and honest participants. Additionally, a classification analysis on single response levels was conducted. Our findings underscore the effectiveness of merging response latency metrics and error rates with unexpected questioning as a robust method for identity deception detection in investigative interviews. We also discuss significant implications for enhancing interview strategies.

Memory and belief updating following complete and partial reminders of fake news.

Fake news can have enduring effects on memory and beliefs. An ongoing theoretical debate has investigated whether corrections (fact-checks) should include reminders of fake news. The familiarity backfire account proposes that reminders hinder correction (increasing interference), whereas integration-based accounts argue that reminders facilitate correction (promoting memory integration). In three experiments, we examined how different types of corrections influenced memory for and belief in news headlines. In the exposure phase, participants viewed real and fake news headlines. In the correction phase, participants viewed reminders of fake news that either reiterated the false details (complete) or prompted recall of missing false details (partial); reminders were followed by fact-checked headlines correcting the false details. Both reminder types led to proactive interference in memory for corrected details, but complete reminders produced less interference than partial reminders (Experiment 1). However, when participants had fewer initial exposures to fake news and experienced a delay between exposure and correction, this effect was reversed; partial reminders led to proactive facilitation, enhancing correction (Experiment 2). This effect occurred regardless of the delay before correction (Experiment 3), suggesting that the effects of partial reminders depend on the number of prior fake news exposures. In all experiments, memory and perceived accuracy were better when fake news and corrections were recollected, implicating a critical role for integrative encoding. Overall, we show that when memories of fake news are weak or less accessible, partial reminders are more effective for correction; when memories of fake news are stronger or more accessible, complete reminders are preferable.

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior.

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Who won?

This poem describes the ugly face of misinformation and lies-spewing bile as COVID shook us and humanity came undone. Vaccines were rejected and lockdowns were broken. We barely withstood. Who won? No one. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

When liars are considered honest.

This article introduces a theoretical model of truth and honesty from a psychological perspective. We examine its application in political discourse and discuss empirical findings distinguishing between conceptions of honesty and their influence on public perception, misinformation dissemination, and the integrity of democracy.

Committed (dis)honesty: A systematic meta-analytic review of the divergent effects of social commitment to individuals or honesty oaths on dishonest behavior.

People feel committed to other individuals, groups, organizations, or moral norms in many contexts of everyday life. Such social commitment can lead to positive outcomes, such as increased job satisfaction or relationship longevity; yet, there can also be detrimental effects to feeling committed. Recent high-profile cases of fraud or corruption in companies like Enron or Volkswagen are likely influenced by strong commitment to the organization or coworkers. Although social commitment might increase dishonest behavior, there is little systematic knowledge about when and how this may occur. In the present project, we reviewed 20,988 articles, focusing on studies that experimentally manipulated social commitment and measured dishonest behavior. We retained 445 effect sizes from 121 articles featuring a total of 91,683 participants across 33 countries. We found no evidence that social commitment increases or reduces dishonest behavior in general. Nonetheless, we did find evidence that the effect strongly depends on the target of the commitment. Feeling committed to other individuals or groups reduces honest behavior (g = -0.17 [-0.24, -0.11]), whereas feeling committed to honesty norms through honesty oaths or pledges increases honest behavior (g = 0.27 [0.19, 0.36]). The analysis identified several moderating variables and detected some degree of publication bias across effects. Our findings highlight the diverging effects of different forms of social commitment on dishonest behavior and suggest a combination of the different forms of commitment could be a possible means to combat corruption and dishonest behavior in the organizational context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Detecting deception with artificial intelligence: promises and perils.

Rapid advancements in artificial intelligence (AI) have driven interest in its potential application for lie detection. Unfortunately, the current approaches have primarily focused on technical aspects at the expense of a solid methodological and theoretical foundation. We discuss the implications thereof and offer recommendations for the development and regulation of AI-based deception detection.

False Health Claims Abound, but Physicians Are Still the Most Trusted Source for Health Information.

This Medical News article discusses a KFF poll about the public's exposure to and beliefs about inaccurate health information, as well as media use and trust in sources.