Self and other-perceived deception detection abilities are highly correlated but unassociated with objective detection ability: Examining the detection consensus effect.

Subjective lying rates are often strongly and positively correlated. Called the deception consensus effect, people who lie often tend to believe others lie often, too. The present paper evaluated how this cognitive bias also extends to deception detection. Two studies (Study 1: N = 180 students; Study 2: N = 250 people from the general public) had participants make 10 veracity judgments based on videotaped interviews, and also indicate subjective detection abilities (self and other). Subjective, perceived detection abilities were significantly linked, supporting a detection consensus effect, yet they were unassociated with objective detection accuracy. More overconfident detectors-those whose subjective detection accuracy was greater than their objective detection accuracy-reported telling more white and big lies, cheated more on a behavioral task, and were more ideologically conservative than less overconfident detectors. This evidence supports and extends contextual models of deception (e.g., the COLD model), highlighting possible (a)symmetries in subjective and objective veracity assessments.

Numeric social-media posts engage people with climate science.

Innumeracy (lack of math skills) among nonscientists often leads climate scientists and others to avoid communicating numbers due to concerns that the public will not understand them and may disengage. However, people often report preferring to receive numbers; providing them also can improve decisions. Here, we demonstrated that the presence vs. absence of at least one Arabic integer in climate-related social-media posts increased sharing up to 31.7% but, counter to hypothesis, decreased liking of messages 5.2% in two preregistered observational studies (climate scientists on Twitter, N  > 8 million Tweets; climate subreddit, N  > 17,000 posts and comments). We speculated that the decreased liking was due, not to reduced engagement, but to more negative feelings towards climate-related content described with numeric precision. A preregistered within-participant experiment (N = 212) then varied whether climate consequences were described using Arabic integers (e.g. "90%") or another format (e.g. verbal terms, "almost all"). The presence of Arabic integers about consequences led to more sharing, wanting to find out more, and greater trust and perceptions of an expert messenger; perceived trust and expertise appeared to mediate effects on sharing and wanting to find out more. Arabic integers about consequences again led to more negative feelings about the Tweets as if numbers clarified the dismaying magnitude of climate threats. Our results indicate that harnessing the power of numbers could increase public trust and concern regarding this defining issue of our time. Communicators, however, should also consider counteracting associated negative feelings-that could halt action-by providing feasible solutions to increase people's self-efficacy.

Reading dies in complexity: Online news consumers prefer simple writing.

Over 30,000 field experiments with The Washington Post and Upworthy showed that readers prefer simpler headlines (e.g., more common words and more readable writing) over more complex ones. A follow-up mechanism experiment showed that readers from the general public paid more attention to, and processed more deeply, the simpler headlines compared to the complex headlines. That is, a signal detection study suggested readers were guided by a simpler-writing heuristic, such that they skipped over relatively complex headlines to focus their attention on the simpler headlines. Notably, a sample of professional writers, including journalists, did not show this pattern, suggesting that those writing the news may read it differently from those consuming it. Simplifying writing can help news outlets compete in the competitive online attention economy, and simple language can make news more approachable to online readers.

Verbal behavior and the future of social science.

Natural language processing (NLP)-previously the domain of a select few language and computer scientists-is undergoing an unprecedented surge in popularity across disciplines. The ubiquity of language data, alongside extremely rapid methodological innovations, has magnetized the field, attracting researchers with the promise of measuring, forecasting, and understanding the most central questions in business, psychology, biology, sociology, the humanities, and beyond. The power of language analysis to reveal insights into human thought, feeling, and behavior has become a core interest emerging from recent technological advances, which are being probed to unearth deeply embedded truths about the human condition. However, NLP research has reached a critical juncture, sitting at the cusp of societal transformation in many aspects of daily life. The details of how NLP research develops over the next 3-5 years will define this transformation. In this emerging, near-infinite space of NLP-driven research, we provide a critical frame of reference for how, when, and why these technologies should evolve in a particularly transdisciplinary manner. Specifically, we discuss (a) the urgency of pairing existing and emerging NLP research with existing scientific knowledge, theory, and principles from the behavioral sciences; (b) the coevolution of NLP technologies; and (c) the practical implications and ethical consequences of expanding language analysis using broader psychosocial theories of the human condition. While our discussion focuses principally on using language as a window in the individual mind, this topic holds substantial implications for other disciplines and lines of inquiry, including the dynamics of social interaction and beyond. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Can generative AI infer thinking style from language? Evaluating the utility of AI as a psychological text analysis tool.

Generative AI, short for Generative Artificial Intelligence, a class of artificial intelligence systems, is not currently the choice technology for text analysis, but prior work suggests it may have some utility to assess dynamics like emotion. The current work builds upon this empirical foundation to consider how analytic thinking scores from a large language model chatbot, ChatGPT, were linked to analytic thinking scores from dictionary-based tools like Linguistic Inquiry and Word Count (LIWC). Using over 16,000 texts from four samples and tested against three prompts and two large language models (GPT-3.5, GPT-4), the evidence suggests there were small associations between ChatGPT and LIWC analytic thinking scores (meta-analytic effect sizes: .058 < rs < .304; ps < .001). When given the formula to calculate the LIWC analytic thinking index, ChatGPT performed incorrect mathematical operations in 22% of the cases, suggesting basic word and number processing may be unreliable with large language models. Researchers should be cautious when using AI for text analysis.

Political ideology shapes risk and benefit judgments of COVID-19 vaccines.

In April 2021, the use of the Johnson & Johnson COVID-19 vaccine was paused to investigate whether it had caused serious blood clots to a small number of women (six out of 6.8 million Americans who had been administered that vaccine). As these events were unfolding, we surveyed a sample of Americans (N = 625) to assess their reactions to this news, whether they supported the pausing of the vaccine, and potential psychological factors underlying their decision. In addition, we employed automated text analyses as a supporting method to more classical quantitative measures. Results showed that political ideology influenced the support for the pausing of the vaccine; liberals were more likely to oppose it than conservatives. In addition, the effect of political ideology was mediated by the difference between perceived benefit and risk and the language style used to produce reasons in support (or against) the decision to pause the vaccine. Liberals perceived the benefit of vaccines higher than the risk, used a more analytic language style when stating their reasons, and had a more positive attitude toward the vaccine. We discuss the implications of our findings considering vaccine hesitancy and risk perception during the COVID-19 pandemic.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex.

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, we trained two rhesus monkeys on a two-target, free-choice luminance-reward selection task. We demonstrate that visual-movement (VM) neurons and nonvisual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

Cross-checking journalistic fact-checkers: The role of sampling and scaling in interpreting false and misleading statements.

Professional fact-checkers and fact-checking organizations provide a critical public service. Skeptics of modern media, however, often question the accuracy and objectivity of fact-checkers. The current study assessed agreement among two independent fact-checkers, The Washington Post and PolitiFact, regarding the false and misleading statements of then President Donald J. Trump. Differences in statement selection and deceptiveness scaling were investigated. The Washington Post checked PolitiFact fact-checks 77.4% of the time (22.6% selection disagreement). Moderate agreement was observed for deceptiveness scaling. Nearly complete agreement was observed for bottom-line attributed veracity. Additional cross-checking with other sources (Snopes, FactCheck.org), original sources, and with fact-checking for the first 100 days of President Joe Biden's administration were inconsistent with potential ideology effects. Our evidence suggests fact-checking is a difficult enterprise, there is considerable variability between fact-checkers in the raw number of statements that are checked, and finally, selection and scaling account for apparent discrepancies among fact-checkers.

Contextual considerations for deception production and detection in forensic interviews.

Most deception scholars agree that deception production and deception detection effects often display mixed results across settings. For example, some liars use more emotion than truth-tellers when discussing fake opinions on abortion, but not when communicating fake distress. Similarly, verbal and nonverbal cues are often inconsistent predictors to assist in deception detection, leading to mixed accuracies and detection rates. Why are lie production and detection effects typically inconsistent? In this piece, we argue that aspects of the context are often unconsidered in how lies are produced and detected. Greater theory-building related to contextual constraints of deception are therefore required. We reintroduce and extend the Contextual Organization of Language and Deception (COLD) model, a framework that outlines how psychological dynamics, pragmatic goals, and genre conventions are aspects of the context that moderate the relationship between deception and communication behavior such as language. We extend this foundation by proposing three additional aspects of the context - individual differences, situational opportunities for deception, and interpersonal characteristics - for the COLD model that can specifically inform and potentially improve forensic interviewing. We conclude with a forward-looking perspective for deception researchers and practitioners related to the need for more theoretical explication of deception and its detection related to the context.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex.

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. During conflict i.e, when salience and goal each favor the selection of different targets, endogenous selection of the task-relevant target relies on top-down control. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, using a two-target free-choice luminance-reward selection task, we demonstrate that visual-movement neurons and not visual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent-beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

Instrumental goal activation increases online petition support across languages.

Research on processing fluency and instrumental goal activation suggests people often perceive complex information positively when effort in a task is valued. The current article evaluates this idea in five online petition samples (total N = 1,047,655 petitions and over 200 million words), assessing how the linguistic fluency of a petition associates with support. Consistent with prior work, petitions with lower rates of lexical fluency (fewer common words) associated with more signatures and an increased probability of petitions making a concrete change than those with higher rates of lexical fluency (more common words). Exploratory results suggest other forms of linguistic complexity also associated with petition support: petitions with more analytic writing (e.g., more formal and complex writing patterns) and with less structural fluency (less readable writing) received more signatures than those with less analytic writing and more structural fluency. Controlling for the political leaning of the petition writers as inferred by their language patterns revealed consistent effects. Crucially, the lexical fluency results were maintained across eight languages as well. Various types of linguistic complexity are therefore instrumental to get people to support online causes. Contributions to fluency theory and the psychology of giving are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Psychological trauma and emotional upheaval as revealed in academic writing: The case of COVID-19.

The current paper used a preregistered set of language dimensions to indicate how scientists psychologically managed the COVID-19 pandemic and its effects. Study 1 evaluated over 1.8 million preprints from arXiv.org and assessed how papers written during the COVID-19 pandemic reflected patterns of psychological trauma and emotional upheaval compared to those written before the pandemic. The data suggest papers written during the pandemic contained more affect and more cognitive processing terms to indicate writers working through a crisis than papers written before the pandemic. Study 2 (N = 74,744 published PLoS One papers) observed consistent emotion results, though cognitive processing patterns were inconsistent. Papers written specifically about COVID-19 contained more emotion than those not written about COVID-19. Finally, Study 3 (N = 361,189 published papers) replicated the Study 2 emotion results across more diverse journals and observed papers written during the pandemic contained a greater rate of cognitive processing terms, but a lower rate of analytic thinking, than papers written before the pandemic. These data suggest emotional upheavals are associated with psychological correlates reflected in the language of scientists at scale. Implications for psychology of language research and trauma are discussed.

Examining the Examiners: How Medical Death Investigators Describe Suicidal, Homicidal, and Accidental Death.

This study describes differences in medicolegal death investigators' written descriptions for people who died by homicide, suicide, or accident. We evaluated 17 years of death descriptions from a midsized metropolitan midwestern county in the United States to assess how death investigators psychologically respond to different manners of death (N = 10,408 cases). Automated text analyses suggest investigators describe accidental deaths with more immediacy relative to homicides, while they also described suicidal deaths in less emotional terms than homicides as well. These data suggest medicolegal death investigators have different psychological reactions to circumstances and manners of death as indicated by their professional writing. Future research may surface context-specific psychological reactions to vicarious trauma that could inform the design or personalization of workplace-coping interventions.

Gender and ethnicity bias in medicine: a text analysis of 1.8 million critical care records.

Gender and ethnicity biases are pervasive across many societal domains including politics, employment, and medicine. Such biases will facilitate inequalities until they are revealed and mitigated at scale. To this end, over 1.8 million caregiver notes (502 million words) from a large US hospital were evaluated with natural language processing techniques in search of gender and ethnicity bias indicators. Consistent with nonlinguistic evidence of bias in medicine, physicians focused more on the emotions of women compared to men and focused more on the scientific and bodily diagnoses of men compared to women. Content patterns were relatively consistent across genders. Physicians also attended to fewer emotions for Black/African and Asian patients compared to White patients, and physicians demonstrated the greatest need to work through diagnoses for Black/African women compared to other patients. Content disparities were clearer across ethnicities, as physicians focused less on the pain of Black/African and Asian patients compared to White patients in their critical care notes. This research provides evidence of gender and ethnicity biases in medicine as communicated by physicians in the field and requires the critical examination of institutions that perpetuate bias in social systems.

Why we dehumanize illegal immigrants: A US mixed-methods study.

Dehumanization is a topic of significant interest for academia and society at large. Empirical studies often have people rate the evolved nature of outgroups and prior work suggests immigrants are common victims of less-than-human treatment. Despite existing work that suggests who dehumanizes particular outgroups and who is often dehumanized, the extant literature knows less about why people dehumanize outgroups such as immigrants. The current work takes up this opportunity by examining why people dehumanize immigrants said to be illegal and how measurement format affects dehumanization ratings. Participants (N = 672) dehumanized such immigrants more if their ratings were made on a slider versus clicking images of hominids, an effect most pronounced for Republicans. Dehumanization was negatively associated with warmth toward illegal immigrants and the perceived unhappiness felt by illegal immigrants from U.S. immigration policies. Finally, most dehumanization is not entirely blatant but instead, captured by virtuous violence and affect as well, suggesting the many ways that dehumanization can manifest as predicted by theory. This work offers a mechanistic account for why people dehumanize immigrants and addresses how survey measurement artifacts (e.g., clicking on images of hominids vs. using a slider) affect dehumanization rates. We discuss how these data extend dehumanization theory and inform empirical research.

Ability-related political polarization in the COVID-19 pandemic.

In two large-scale longitudinal datasets (combined N = 5761), we investigated ability-related political polarization in responses to the COVID-19 pandemic. We observed more polarization with greater ability in emotional responses, risk perceptions, and product-purchase intentions across five waves of data collection with a diverse, convenience sample from February 2020 through July 2020 (Study 1, N = 1267). Specifically, more liberal participants had more negative emotional responses and greater risk perceptions of COVID-19 than conservative participants. Compared to conservatives, liberal participants also interpreted quantitative information as indicating higher COVID-19 risk and sought COVID-related news more from liberal than conservative news media. Of key importance, we also compared verbal and numeric cognitive abilities for their independent capacity to predict greater polarization. Although measures of numeric ability, such as objective numeracy, are often used to index ability-related polarization, ideological differences were more pronounced among those higher in verbal ability specifically. Similar results emerged in secondary analysis of risk perceptions in a nationally representative longitudinal dataset (Study 2, N = 4494; emotions and purchase intentions were not included in this dataset). We further confirmed verbal-ability-related polarization findings on non-COVID policy attitudes (i.e., weapons bans and Medicare-for-all) measured cross-sectionally. The present Study 2 documented ability-related polarization emerging over time for the first time (rather than simply measuring polarization in existing beliefs). Both studies demonstrated verbal ability measures as the most robust predictors of ability-related polarization. Together, these results suggest that polarization may be a function of the amount and/or application of verbal knowledge rather than selective application of quantitative reasoning skills.

The predictive utility of word familiarity for online engagements and funding.

Metacognitive frameworks such as processing fluency often suggest people respond more favorably to simple and common language versus complex and technical language. It is easier for people to process information that is simple and nontechnical compared to complex information, therefore leading to more engagement with targets. In two studies covering 12 field samples (total n = 1,064,533), we establish and replicate this simpler-is-better phenomenon by demonstrating people engage more with nontechnical language when giving their time and attention (e.g., simple online language tends to receive more social engagements). However, people respond to complex language when giving their money (e.g., complex language within charitable giving campaigns and grant abstracts tend to receive more money). This evidence suggests people engage with the heuristic of complex language differently depending on a time or money target. These results underscore language as a lens into social and psychological processes and computational methods to measure text patterns at scale.

Virtual reality and the psychology of climate change.

Researchers and practitioners have used virtual reality (VR) as a tool to understand attitudes and behaviors around climate change for decades. As VR has become more immersive, mainstream, and commercially available, it has also become a medium for education about climate issues, a way to indirectly expose users to novel stimuli, and a tool to tell stories about antienvironmental activity. This review explicates the relationship between VR and climate change from a psychological perspective and offers recommendations to make virtual experiences engaging, available, and impactful for users. Climate change is perhaps the most urgent global issue of our lifetime with irreversible consequences. It therefore requires innovative experiential approaches to teach its effects and modify attitudes in support of proenvironmental actions.

Dehumanization During the COVID-19 Pandemic.

Communities often unite during a crisis, though some cope by ascribing blame or stigmas to those who might be linked to distressing life events. In a preregistered two-wave survey, we evaluated the dehumanization of Asians and Asian Americans during the COVID-19 pandemic. Our first wave (March 26-April 2, 2020; N = 917) revealed dehumanization was prevalent, between 6.1% and 39% of our sample depending on measurement. Compared to non-dehumanizers, people who dehumanized also perceived the virus as less risky to human health and caused less severe consequences for infected people. They were more likely to be ideologically Conservative and believe in conspiracy theories about the virus. We largely replicated the results 1 month later in our second wave (May 6-May 13, 2020; N = 723). Together, many Americans dehumanize Asians and Asian Americans during the COVID-19 pandemic with related perceptions that the virus is less problematic. Implications and applications for dehumanization theory are discussed.

Type II Achalasia Is Increasing in Prevalence.

BACKGROUND: Three manometric subtypes of achalasia were defined in the Chicago Classification approximately 10 years ago: type I (aperistalsis), type II (pan-pressurization), and type III (spastic). Since the widespread use of this classification scheme, the evolving prevalence of these subtypes has not been elucidated. We aim to determine the prevalence of each subtype a decade after the adoption of the Chicago Classification. METHODS: This is a retrospective cohort analysis of patients diagnosed with achalasia on high-resolution manometry (HRM) at two major academic medical centers between 2015 and 2018. Patients were excluded if they had a diagnosis of another esophageal motility disorder, previously treated achalasia, or foregut surgery. Demographic data, manometric subtype, and esophageal dilatation grade on endoscopy were obtained. Prevalence of achalasia subtypes was compared with a published historical control population (2004-2007). Fischer's exact and t tests were used for analysis. RESULTS: Of 147 patients in the contemporary cohort and 99 in the historical control cohort, the prevalence of type I achalasia was 8% versus 21%, type II 63% versus 50%, and type III 29% versus 29%, respectively (p = 0.01). The mean age in our population was 58 years compared to 57 years in the historical control, and the proportion of men 48% versus 47%, respectively (p = 0.78). Mean endoscopic dilatation grade in the contemporary cohort was 1.5 for type I patients, 0.9 for type II, and 0.4 for type III, compared with 1.5, 0.6, and 0.4, respectively. Overall mean dilatation grade was 0.8 in our cohort versus 0.7 in the historical control (p = 0.58). CONCLUSION: The prevalence of type II achalasia was significantly greater and prevalence of type I significantly less in our patient population compared to our predefined historical control. Other characteristics such as age and sex did not appear to contribute to these differences. Histopathological evidence has suggested that type II achalasia may be an earlier form of type I; thus, the increased prevalence of type II achalasia may be related to earlier detection of the disease. The adoption of HRM, widespread use of the Chicago Classification, and increased disease awareness in the past decade may be contributing to these changes in epidemiology.