Tom Stafford’s research while affiliated with The University of Sheffield and other places

Online political advertising is often portrayed negatively, yet there is limited evidence regarding what exactly the public deems unacceptable. This paper provides new insights into public attitudes based on an online survey conducted in 2022, in which 1881 respondents evaluated political ads placed on Facebook during the 2019 UK General Election. We find that citizens do not inherently view political ads as unacceptable, and that perceptions of acceptability are influenced by partisan and demographic factors. We also find that ads deemed compliant with existing regulatory protocols for non-political advertising are considered more acceptable, suggesting a case for extending the existing regulatory regime to political ads. Delving deeper into our survey data, we explore the drivers behind these perceptions of acceptability and find that concerns about the content and tone of ads play a significant role. These findings provide valuable insights for those seeking to develop codes of conduct to govern practices in this space. Overall, our study offers a nuanced understanding of public attitudes toward online political advertising and identifies possible pathways for regulatory reform.

Background: Induction of negative mood increases tobacco choice in dependent smokers; however, less is known about the mechanisms behind this. This study addressed this gap by applying a computational model of value-based decision-making to tobacco and tobacco-unrelated choices following mood manipulation. Method: Using a pre-registered, within-subject design, 49 daily tobacco smokers (›10 daily cigarettes) watched two different videos which primed them to experience negative and positive mood (tobacco valuation and devaluation, respectively). Participants completed self-report measures of mood and craving to smoke before and after priming, followed by a two-alternative forced-choice task with (separate) blocks of tobacco-related and unrelated (animal) images. On each block, participants selected the image that they previously rated higher. A drift-diffusion model was fitted to the reaction time and error data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks. Results: After watching videos intended to induce negative mood, happiness scores were lower (p ‹ .001, d = 1.16), while sadness and craving to smoke scores were higher (both ps ‹ .001, ds › .60), compared to after watching videos intended to induce positive mood. However, contrary to hypotheses, the experimental manipulation did not robustly affect EA rates (F = 1.15, p = .29, ηp2 = .02) or response thresholds (F = .07, p = .79, ηp2 = .00) for either tobacco or tobacco-unrelated decisions. Conclusions: Manipulation of mood in daily smokers did not lead to alterations in the internal processes that precede value-based decisions made about tobacco and tobacco-unrelated cues.

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.

Background: Induction of negative mood increases tobacco choice in dependent smokers; however, less is known about the mechanisms behind this. This study addressed this gap by applying a computational model of value-based decision-making to tobacco and tobacco-unrelated choices following mood manipulation. Method: Using a pre-registered, within-subject design, 49 daily tobacco smokers (›10 daily cigarettes) watched two different videos which primed them to experience negative and positive mood (tobacco valuation and devaluation, respectively). Participants completed self-report measures of mood and craving to smoke before and after priming, followed by a two-alternative forced-choice task with (separate) blocks of tobacco-related and unrelated (animal) images. On each block, participants selected the image that they previously rated higher. A drift-diffusion model was fitted to the reaction time and error data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks. Results: After watching videos intended to induce negative mood, happiness scores were lower (p ‹ .001, d = 1.16), while sadness and craving to smoke scores were higher (both ps ‹ .001, ds › .60), compared to after watching videos intended to induce positive mood. However, contrary to hypotheses, the experimental manipulation did not robustly affect EA rates (F = 1.15, p = .29, ηp2 = .02) or response thresholds (F = .07, p = .79, ηp2 = .00) for either tobacco or tobacco-unrelated decisions. Conclusions: Manipulation of mood in daily smokers did not lead to alterations in the internal processes that precede value-based decisions made about tobacco and tobacco-unrelated cues.

Induction of negative mood increases tobacco choice in dependent smokers; however, less is known about the mechanisms behind this. This study addressed this gap by applying a computational model of value-based decision making to tobacco and tobacco-unrelated choices following mood manipulation. Using a preregistered, within-subject design, 49 daily tobacco smokers (>10 daily cigarettes) watched two different videos which primed them to experience negative and positive mood (tobacco valuation and devaluation, respectively). Participants completed self-report measures of mood and craving to smoke before and after priming, followed by a two-alternative forced-choice task with (separate) blocks of tobacco-related and tobacco-unrelated (animal) images. On each block, participants selected the image that they previously rated higher. A drift-diffusion model was fitted to the reaction time and error data to estimate evidence accumulation processes and response thresholds during the different blocks. After watching videos intended to induce negative mood, happiness scores were lower (p < .001, d = 1.16), while sadness and craving to smoke scores were higher (both ps < .001, ds > .60) compared to after watching videos intended to induce positive mood. However, contrary to hypotheses, the experimental manipulation did not robustly affect evidence accumulation rates (F = 1.15, p = .29, ηp² = .02) or response thresholds (F = .07, p = .79, ηp² = .00) for either tobacco or tobacco-unrelated decisions. Manipulation of mood in daily smokers did not lead to alterations in the internal processes that precede value-based decisions made about tobacco and tobacco-unrelated cues.

Background: Induction of negative mood increases tobacco choice in dependent smokers; however, less is known about the mechanisms behind this. This study addressed this gap by applying a computational model of value-based decision-making to tobacco and tobacco-unrelated choices following mood manipulation. Method: Using a pre-registered, within-subject design, 49 daily tobacco smokers (›10 daily cigarettes) watched two different videos which primed them to experience negative and positive mood (tobacco valuation and devaluation, respectively). Participants completed self-report measures of mood and craving to smoke before and after priming, followed by a two-alternative forced-choice task with (separate) blocks of tobacco-related and unrelated (animal) images. On each block, participants selected the image that they previously rated higher. A drift-diffusion model was fitted to the reaction time and error data to estimate evidence accumulation (EA) processes and response thresholds during the different blocks. Results: After watching videos intended to induce negative mood, happiness scores were lower (p ‹ .001, d = 1.16), while sadness and craving to smoke scores were higher (both ps ‹ .001, ds › .60), compared to after watching videos intended to induce positive mood. However, contrary to hypotheses, the experimental manipulation did not robustly affect EA rates (F = 1.15, p = .29, ηp2 = .02) or response thresholds (F = .07, p = .79, ηp2 = .00) for either tobacco or tobacco-unrelated decisions. Conclusions: Manipulation of mood in daily smokers did not lead to alterations in the internal processes that precede value-based decisions made about tobacco and tobacco-unrelated cues.

The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, augmented by AI tools, present a promising solution. In this study, we explore the potential of DeepFakeDeLiBot, a deliberation-enhancing chatbot, to support groups in detecting deepfake text. Our findings reveal that group-based problem-solving significantly improves the accuracy of identifying machine-generated paragraphs compared to individual efforts. While engagement with DeepFakeDeLiBot does not yield substantial performance gains overall, it enhances group dynamics by fostering greater participant engagement, consensus building, and the frequency and diversity of reasoning-based utterances. Additionally, participants with higher perceived effectiveness of group collaboration exhibited performance benefits from DeepFakeDeLiBot. These findings underscore the potential of deliberative chatbots in fostering interactive and productive group dynamics while ensuring accuracy in collaborative deepfake text detection. \textit{Dataset and source code used in this study will be made publicly available upon acceptance of the manuscript.

INTRODUCTION: The study aimed to compare cognitive trajectories between patients with reports of social isolation and loneliness and those without. METHODS: Reports of social isolation, loneliness, and Montreal Cognitive Assessment (MoCA) scores were extracted from dementia patients' medical records using Natural Language Processing models and analysed using mixed-effects models. RESULTS: Lonely patients (n = 382) showed lower MoCA scores throughout the disease (B = -0.83, t = -2.64, p = 0.008). Socially isolated patients (n = 523) experienced faster cognitive decline six months before diagnosis (B = -0.21, t = -2.18, p = 0.029), but were comparable to controls (n = 3912) before this period. This led to lower MoCA scores at diagnosis (B = -0.69, t = -2.53, p = 0.011) and in later stages. DISCUSSION: Lower cognitive levels in lonely and socially isolated patients suggest that these factors may contribute to dementia progression. KEYWORDS: Social isolation, Loneliness, Electronic Health Records, Natural Language Processing, Tertiary prevention

Background Marital status and living status are components of social isolation (SI), a modifiable factor thought to impact cognitive resilience, which has the potential to impact cognition throughout the course of Alzheimer’s and related dementia (ADRD) diagnosis. Electronic health records (EHRs) offer access to large scale clinical data, capable of longitudinal analyses. Method Cognitive function measurement – Montreal Cognitive Assessment (MoCA) – data, demographic (including marital and living status as SI proxies) data and ADRD diagnosis data from patients aged 50+ years from Oxford Health NHS Foundation Trust (UK) were extracted using natural language processing algorithms from EHRs dated 1995 to 2022. Longitudinal multilevel models were used to predict cognition as a function of the interaction between diagnosis duration and SI proxies, controlling for age, sex and diagnosis cause. Result ‘Lifelong single’ marital status significantly predicted reduced cognition intercept scores for the MoCA dataset (𝛽 = ‐1.61, SE = 0.67, t = ‐2.42, p = 0.016). No significant marital status predictors for slope were found. Living in supported accommodation significantly predicted steeper slopes for cognition (𝛽 = ‐2.37, SE = 0.33, t = ‐7.20, p < 0.001). No living status levels significantly predicted slopes. Conclusion Worldwide ADRD incidence is predicted to increase dramatically within the next 30 years, therefore studies investigating the impact of modifiable factors on the rates of cognitive change in ADRD patients are valuable to enhancing understanding of patient care. SI data extracted from EHRs can be used to predict differences in patient cognition scores.