This cross-sectional study investigated the reasons behind academic cheating in a cohort of nursing students in Saudi Arabia. The study involved 482 nursing students from two government universities in Riyadh. We used a newly developed self-reported questionnaire called the Reasons for Cheating Scale (RCS) to collect data. The highest-scoring reasons for academic cheating in the study population included the desire to obtain high grades, encouragement from friends to cheat, and the perception that exams were too difficult. Male students scored significantly higher than female students for reasons such as not understanding the course material, unclear test questions and instructions, pressure from families to excel, difficulty of the course material, and ignorance of effective study methods (P < 0.05). Age also had a role, as students aged 15-20 years had significantly higher scores for the item \"Exams are too hard\", whereas those aged ≥25 years had higher scores for \"Difficulty of the course material\" (P < 0.05). Additionally, students in the preparatory year had significantly higher scores than those in other years for reasons such as difficult exams, unclear test questions and instructions, fear of failing, difficulty of the course material, and the desire to please their families (P < 0.05). Overall, the desire to obtain high grades emerged as the main reason for academic cheating in our cohort of nursing students in Saudi Arabia. The findings suggest that sociodemographic characteristics, including sex, age, and academic year, should be considered when addressing the issue of cheating among nursing students.

Due to differences in social communication and cognitive functioning, autistic adults may have greater difficulty engaging in and detecting deception compared to neurotypical adults. Consequently, autistic adults may experience strained social relationships or face increased risk of victimization. It is therefore crucial that research investigates the psychological mechanisms that are responsible for autistic adults\' difficulties in the deception process in order to inform interventions required to reduce risk. However, weaknesses of extant research exploring deception in autism include a heavy focus on children and limited theoretical exploration of underlying psychological mechanisms. To address these weaknesses, this review aims to introduce a system-level theoretical framework to the study of deception in autistic adulthood: The Brunswik Lens Model of Deception. Here, we provide a comprehensive account of how autism may influence all processes involved in deception, including: Choosing to Lie (1), Producing Deception Cues (2), Perceiving Deception Cues (3), and Making the Veracity Decision (4). This review also offers evidence-based, theoretical predictions and testable hypotheses concerning how autistic and neurotypical adults\' behavior may differ at each stage in the deception process. The call to organize future research in relation to a joint theoretical perspective will encourage the field to make substantive, theoretically motivated progress toward the development of a comprehensive model of deception in autistic adulthood. Moreover, the utilization of the Brunswik Lens Model of Deception in future autism research may assist in the development of interventions to help protect autistic adults against manipulation and victimization.

Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.

BACKGROUND: Deceptive movements occur when an actor seeks to fake, hide or delay kinematic information about their true movement outcomes. The purpose of deceptive movements is to impair the perception of opponents (the \'observer\') to gain an advantage over them. We argue though that a lack of conceptual clarity has led to confusion about what deception is and in understanding the different approaches by which an actor can deceive their opponent. The aim of this article is to outline a conceptual framework for understanding deceptive movements in sport.
METHODS: Adopting Interpersonal Deception Theory from the field of communication, we define deception as when an actor deliberately alters their actions in an attempt to impair the ability of an observer to anticipate their true action outcomes. Further, deception can be achieved either by what we define as deceit, the act of providing false information, or disguise, the act of concealing the action outcome. Skilled athletes often have actions that are difficult to anticipate, but an action is only classified as containing deception if the actor has explicit intent to deceive an observer. Having outlined the conceptual framework, we then review existing empirical findings on the skilled perception of deceptive movements considering the framework. This approach includes a critical evaluation of the mechanisms known to facilitate the perceptual ability to prevent being deceived, including a consideration of visual search strategies, confidence, the contribution of visual and motor experiences, and the influence of response biases and action capabilities on perceptual performance.
CONCLUSIONS: The distinction between deceit and disguise particularly helps to show that most research has examined deceit, with little known about how an actor can more effectively disguise their action, or about how an observer can improve their ability to anticipate the outcome of disguised actions. The insights help to identify fruitful areas for future research and outline implications for skill acquisition and performance enhancement.

Cross-situational inconsistency is common in the expression of honesty traits; yet, there is insufficient emphasis on behavioral dishonesty across multiple contexts. The current study aimed to investigate behavioral dishonesty in various contexts and reveal the associations between trait honesty, behavioral dishonesty, and neural patterns of observing others behave honestly or dishonestly in videos (abbr.: (dis)honesty video-watching). First, the results revealed limitations in using trait honesty to reflect variations in dishonest behaviors and predict behavioral dishonesty. The finding highlights the importance of considering neural patterns in understanding and predicting dishonest behaviors. Second, by comparing the predictive performance of seven types of data across three neural networks, the results showed that functional connectivity in the hypothesis-driven network during (dis)honesty video-watching provided the highest predictive power in predicting multitask behavioral dishonesty. Last, by applying the feature elimination method, the midline self-referential regions (medial prefrontal cortex, posterior cingulate cortex, and anterior cingulate cortex), anterior insula, and striatum were identified as the most informative brain regions in predicting behavioral dishonesty. In summary, the study offered insights into individual differences in deception and the intricate connections among trait honesty, behavioral dishonesty, and neural patterns during (dis)honesty video-watching.

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.

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.

In basketball, an attacking player often plays a pass to one side while looking to the other side. This head fake provokes a conflict in the observing opponent, as the processing of the head orientation interferes with the processing of the pass direction. Accordingly, responses to passes with head fakes are slower and result in more errors than responses to passes without head fakes (head-fake effect). The head-fake effect and structurally similar interference effects (e.g., Stroop effect) are modulated by the frequency of conflicting trials. Previous studies mostly applied a block-wise manipulation of proportion congruency. However, in basketball (and also in other team sports), where different individual opponents can be encountered, it might be important to take the individual frequency (e.g., 20% vs. 80%) of these opponents into account. Therefore, the present study investigates the possibility to quickly (i.e., on a trial-by-trial basis) reconfigure the response behavior to different proportions of incongruent trials, which are contingent on different basketball players. Results point out that participants indeed adapted to the fake-frequency of different basketball players, which could be the result of strategic adaptation processes. Multi-level analyses, however, indicate that a substantial portion of the player-specific adaptation to fake frequencies is accounted by episodic retrieval processes, suggesting that item-specific proportion congruency effects can be explained in terms of stimulus-response binding and retrieval: The head orientation (e.g., to the right) of a current stimulus retrieves the last episode with the same head orientation including the response that was part of this last episode. Thus, from a theoretical perspective, an attacking player would provoke the strongest detrimental effect on an opponent if s/he repeats the same head movement but changes the direction of the pass. Whether it is at all possible to strategically apply this recommendation in practice needs still to be answered.

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.

UNASSIGNED: The brain mechanisms of deceptive behavior are relatively well studied, and the key brain regions involved in its processing were established. At the same time, the brain mechanisms underlying the processes of preparation for deception are less known.
UNASSIGNED: We studied BOLD-signal changes during the presentation of the opponent\'s feedback to a previous deceptive or honest action during the computer game. The goal of the game was to mislead the opponent either by means of deception or by means of telling the truth.
UNASSIGNED: As a result, it was shown that several brain regions that were previously demonstrated as involved in deception execution, such as the left anterior cingulate cortex and anterior insula, also underlie processes related to deception preparation.
UNASSIGNED: The results obtained also allowed us to suggest that brain regions responsible for performance monitoring, intention assessment, suppression of non-selected solutions, and reward processing could be involved in shaping future action selection and preparation for deception. By shedding light on the brain mechanisms underlying deception, our study contributes to a deeper understanding of this complex cognitive process. Furthermore, it emphasizes the significance of exploring brain mechanisms governing the choice between deception and truth at various stages of decision-making.