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To beckon or not to beckon: Testing a causal-evaluative modelling approach to moral judgment: A registered report
Moral judgments are increasingly being understood as showing context dependent variability. A growing literature has identified a range of specific contextual factors (e.g., emotions, intentions) that can influence moral judgments in predictable ways. Integrating these diverse influences into a unified approach to understanding moral judgments remains a challenge. Recent work by Railton (2017) attempted to address this with a causal-evaluative modelling approach to moral judgment. In support of this model Railton presents evidence from novel variations of classic trolley type dilemmas. We present results from a pre-registered pilot study that highlight a significant confound and demonstrate that it likely influenced Railton's results. Building on this, our registered report presents a replication-extension of Railton's study, using larger more diverse samples, and more rigorous methods and materials, specifically controlling for potential confounds. We found that participants' judgments in sacrificial dilemmas are influenced by both direct personal force, and by whether harm occurs as a means or as a side-effect of action. We also show the relationship between a range of individual difference variables and responses to sacrificial moral dilemmas. Our results provide novel insights into the factors that influence people's moral judgments, and contribute to ongoing theoretical debates in moral psychology.
Moral judgement and decision-making: theoretical predictions and null results.
The study of moral judgement and decision making examines the way predictions made by moral and ethical theories fare in real world settings. Such investigations are carried out using a variety of approaches and methods, such as experiments, modeling, and observational and field studies, in a variety of populations. The current Collection on moral judgments and decision making includes works that represent this variety, while focusing on some common themes, including group morality and the role of affect in moral judgment. The Collection also includes a significant number of studies that made theoretically driven predictions and failed to find support for them. We highlight the importance of such null-results papers, especially in fields that are traditionally governed by theoretical frameworks.
The role of confidence in knowledge ascriptions: an evidence-seeking approach
AbstractTwo methods have been used in the investigation of the stakes-sensitivity of knowledge as it occurs in ordinary language: (a) asking participants about the truth or acceptability of knowledge ascriptions and (b) asking participants how much evidence someone needs to gather before they know that something is the case. This second, “evidence-seeking”, method has reliably found effects of stakes-sensitivity while the method of asking about knowledge ascriptions has not. Consistent with this pattern, in Francis et al. (Ergo, 2019), we found evidence of scalar stakes effects using an evidence-seeking approach. Whilst we found this evidence across several cases using both negative (“don’t know”) and positive (“know”) polarities, there remain questions about the directness of the relationship between stakes and knowledge ascriptions; it is possible that stakes are affecting knowledge by affecting the confidence of the attributor. For example, Bach (in: Peter & Preyer, 2005) has argued that knowledge attributions do not track truth attributions but rather thresholds for doxastic confidence. To investigate the role of confidence in knowledge ascriptions, we use our existing paradigm (Francis et al., 2019) but include measures of both participant and protagonist confidence. As far as we are aware, this is the first empirical investigation of the role of confidence in stakes effects on knowledge that incorporates an evidence-seeking approach using several scenarios. Overall, across both positive (“know”) and negative (“don’t know”) polarity conditions, we find further evidence of a stakes effect on knowledge using an evidence-seeking paradigm. However, and importantly, we do not find evidence that changes in participant confidence partially or fully mediate the stakes effect on knowledge.
Moral psychology of nursing robots: Exploring the role of robots in dilemmas of patient autonomy
AbstractArtificial intelligences (AIs) are widely used in tasks ranging from transportation to healthcare and military, but it is not yet known how people prefer them to act in ethically difficult situations. In five studies (an anthropological field study, n = 30, and four experiments, total n = 2150), we presented people with vignettes where a human or an advanced robot nurse is ordered by a doctor to forcefully medicate an unwilling patient. Participants were more accepting of a human nurse's than a robot nurse's forceful medication of the patient, and more accepting of (human or robot) nurses who respected patient autonomy rather than those that followed the orders to forcefully medicate (Study 2). The findings were robust against the perceived competence of the robot (Study 3), moral luck (whether the patient lived or died afterwards; Study 4), and command chain effects (Study 5; fully automated supervision or not). Thus, people prefer robots capable of disobeying orders in favour of abstract moral principles like valuing personal autonomy. Our studies fit in a new era in research, where moral psychological phenomena no longer reflect only interactions between people, but between people and autonomous AIs.
Data from an International Multi-Centre Study of Statistics and Mathematics Anxieties and Related Variables in University Students (the SMARVUS Dataset).
This large, international dataset contains survey responses from N = 12,570 students from 100 universities in 35 countries, collected in 21 languages. We measured anxieties (statistics, mathematics, test, trait, social interaction, performance, creativity, intolerance of uncertainty, and fear of negative evaluation), self-efficacy, persistence, and the cognitive reflection test, and collected demographics, previous mathematics grades, self-reported and official statistics grades, and statistics module details. Data reuse potential is broad, including testing links between anxieties and statistics/mathematics education factors, and examining instruments' psychometric properties across different languages and contexts. Data and metadata are stored on the Open Science Framework website [https://osf.io/mhg94/].
Identifying subphenotypes of patients undergoing post-operative delirium assessment.
INTRODUCTION: Delirium has heterogeneous etiologies and clinical presentations and is often associated with poor outcomes. Its pathophysiological mechanisms remain largely hypothetical and without targeted pharmacological treatment. This work investigates subphenotypes of patients undergoing delirium assessment based on clinical features and fluid biomarkers. METHODS: We performed latent class analysis of an observational cohort of older adults undergoing elective surgery. RESULTS: Two classes were identified, both containing individuals experiencing delirium symptoms, with a higher number in Class 1 (p
High epilepsy prevalence and excess mortality in onchocerciasis-endemic counties of South Sudan: A call for integrated interventions.
BACKGROUND: Epilepsy is a major health concern in onchocerciasis-endemic regions with intense transmission, where the infection is associated with a high epilepsy burden. This study investigated epilepsy prevalence and mortality in five onchocerciasis-endemic counties of South Sudan, and the association between onchocerciasis transmission and epilepsy, including probable nodding syndrome (pNS). METHODOLOGY: House-to-house cross-sectional surveys (2021-2024) identified persons with suspected epilepsy (sPWE) and retrospectively documented deaths among sPWE and individuals without epilepsy (IWE). Epilepsy diagnoses, including pNS, were confirmed by trained clinicians. Ongoing transmission was assessed using anti-Ov16 seroprevalence in children aged 3‒9 years. Age- and sex-standardised epilepsy, pNS and anti-Ov16 prevalence were calculated, along with age- and sex-standardised mortality rates and standardised mortality ratios (SMRs) with 95% confidence intervals (95%CIs), using IWE as the reference population. Weighted arcsin-transformed linear regression was used to explore the association between epilepsy and anti-Ov16 prevalence. PRINCIPAL FINDINGS: Among 34,019 individuals screened, 166 deaths occurred in 3,101 person-years for sPWE versus 466 deaths in 63,420 person-years for IWE. Epilepsy prevalence was 4.1% (range: 2.3-7.1%), and pNS prevalence was 1.5% (range: 0.6-2.2%). Anti-Ov16 seroprevalence among children was 23.3% (range: 1.4-44.1%). Each 1.0 percentage point increase in standardised anti-Ov16 seroprevalence was statistically significantly associated with an average rise of 0.10 percentage points in standardised epilepsy prevalence and 0.04 percentage points in standardised pNS prevalence. Median age at death was lower for sPWE (20 years) than IWE (38 years; Mann-Whitney U-test p-value
Are we hallucinating or can psychedelic drugs modulate the immune system to control inflammation?
Psychedelic drugs that activate 5-HT2A receptors have been long used for cultural, medicinal and recreational purposes. Interest in psychedelics for treating psychiatric disorders has resurged recently and is well documented; less well recognised are their anti-inflammatory properties. Growing evidence now demonstrates that psychedelics modulate immune responses, including inhibiting pro-inflammatory cytokine release. Furthermore, in vivo studies demonstrate that psychedelics, like (R)-DOI, reduce inflammation in animal models of acute and chronic inflammatory disease such as asthma. Likewise, some clinical studies with psychedelic drugs (e.g. psilocybin) demonstrate an impact upon circulating cytokine levels, supporting a translation from the animal models to the clinical arena. Such data emphasise the promise of therapeutic approaches targeting inflammation. Interestingly, recent research has also uncovered compounds that maintain therapeutic potential without likely causing psychedelic effects. These discoveries suggest that drugs informed by psychedelic drugs, but which do not evoke psychedelic experiences, which we term PIPI drugs (Psychedelic drug Informed but Psychedelic experience Inactive), could offer effective treatments for mental health and inflammation, presenting new avenues for therapeutic development.
Current Position and Future Direction of Inflammation in Neuropsychiatric Disorders: A Review.
IMPORTANCE: There has been a large increase in research focusing on inflammation across psychiatric disorders, with the hope of achieving breakthroughs seen with this approach in cancer and other conditions. Current findings suggest that immune-related pathophysiological processes involving inflammation could play a key role for many major mental illnesses. How far reaching this role would be and how soon we can expect translation into treatment, however, remain open questions. OBSERVATIONS: In this narrative review, new evidence from clinical populations, new trials, and preclinical models was summarized. Converging evidence suggests that inflammation plays a significant role in subgroups of patients with psychosis, depression, and autism. Interleukin (IL) 6, T-cell control, immune-metabolic function, and the complement system represent fundamental areas of further research. New treatments have yet to reach clinical impact, but targeted trials are ongoing. Developing and refining human cellular models will aid mechanistic target validation and further understanding of causal pathways and networks. CONCLUSIONS AND RELEVANCE: To advance to and achieve clinical impact, investigations need to include a collaborative, united effort, pulling information across disciplines and translational scales. A focused approach is needed to validate key emerging targets, where evidence and potential for new and repurposed treatments are strongest.
Digital health technologies in the accelerating medicines Partnership® Schizophrenia Program.
Although meta-analytic studies have shown that 25-33% of those at Clinical High Risk (CHR) for psychosis transition to a first episode of psychosis within three years, less is known about estimating the risk of transition at an individual level. Digital phenotyping offers a novel approach to explore the nature of CHR and may help to improve personalized risk prediction. Specifically, digital data enable detailed mapping of experiences, moods and behaviors during longer periods of time (e.g., weeks, months) and offer more insight into patterns over time at the individual level across their routine daily life. However, while novel digital health technologies open up many new avenues of research, they also come with specific challenges, including replicability of results and the adherence of participants. This paper outlines the design of the digital component of the Accelerating Medicines Partnership® Schizophrenia Program (AMP SCZ) project, a large international collaborative project that follows individuals at CHR for psychosis over a period of two years. The digital component comprises one-year smartphone-based digital phenotyping and actigraphy. Smartphone-based digital phenotyping includes 30-item short daily self-report surveys and voice diaries as well as passive data capture (geolocation, on/off screen state, and accelerometer). Actigraphy data are collected via an Axivity wristwatch. The aim of this paper is to describe the design and the three goals of the digital measures used in AMP SCZ to: (i) better understand the symptoms, real-life experiences, and behaviors of those at CHR for psychosis, (ii) improve the prediction of transition to psychosis and other health outcomes in this population based on digital phenotyping and, (iii) serve as an example for replicable and ethical research across geographically diverse regions and cultures. Accordingly, we describe the rationale, protocol and implementation of these digital components of the AMP SCZ project. **Link to video interview: https://vimeo.com/1060935583 *.
Development and evaluation of prompts for a large language model to screen titles and abstracts in a living systematic review.
BACKGROUND: Living systematic reviews (LSRs) maintain an updated summary of evidence by incorporating newly published research. While they improve review currency, repeated screening and selection of new references make them labourious and difficult to maintain. Large language models (LLMs) show promise in assisting with screening and data extraction, but more work is needed to achieve the high accuracy required for evidence that informs clinical and policy decisions. OBJECTIVE: The study evaluated the effectiveness of an LLM (GPT-4o) in title and abstract screening compared with human reviewers. METHODS: Human decisions from an LSR on prodopaminergic interventions for anhedonia served as the reference standard. The baseline search results were divided into a development and a test set. Prompts guiding the LLM's eligibility assessments were refined using the development set and evaluated on the test set and two subsequent LSR updates. Consistency of the LLM outputs was also assessed. RESULTS: Prompt development required 1045 records. When applied to the remaining baseline 11 939 records and two updates, the refined prompts achieved 100% sensitivity for studies ultimately included in the review after full-text screening, though sensitivity for records included by humans at the title and abstract stage varied (58-100%) across updates. Simulated workload reductions of 65-85% were observed. Prompt decisions showed high consistency, with minimal false exclusions, satisfying established screening performance benchmarks for systematic reviews. CONCLUSIONS: Refined GPT-4o prompts demonstrated high sensitivity and moderate specificity while reducing human workload. This approach shows potential for integrating LLMs into systematic review workflows to enhance efficiency.
Human-Like Epistemic Trust? A Conceptual and Normative Analysis of Conversational AI in Mental Healthcare.
The attribution of human concepts to conversational artificial intelligence (CAI) simulating human characteristics and conversation in psychotherapeutic settings presents significant conceptual and normative challenges. First, this article analyzes the concept of epistemic trust by identifying its problematic conditions when attributed to CAI, arguing for conceptual shift. We propose a conceptual, visual tool to navigate this shift. Second, three conceptualizations of AI are analyzed to understand their influence on the interpretation and evaluation of conceptual shift of epistemic trust and associated risks. We contrast two common AI conceptualizations from literature: a dichotomic account, distinguishing between AI's real and simulated abilities, and a relational account. Finally, we propose a novel approach: conceptualizing AI as a fictional character to combine their strengths, arguing for shifting focus from merely simulating human abilities to addressing CAI's actual strengths and weaknesses. The article sheds light on underlying theoretical assumptions that influence the ethical analysis of CAI.