Bored to death: Artificial Intelligence research reveals the role of boredom in suicide behavior.

Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Methods: The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results: An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusion: Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.

Screen Time and Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.

Importance: Contemporary studies raise concerns regarding the implications of excessive screen time on the development of autism spectrum disorder (ASD). However, the existing literature consists of mixed and unquantified findings. Objective: To conduct a systematic review and meta-analyis of the association between screen time and ASD. Data Sources: A search was conducted in the PubMed, PsycNET, and ProQuest Dissertation & Theses Global databases for studies published up to May 1, 2023. Study Selection: The search was conducted independently by 2 authors. Included studies comprised empirical, peer-reviewed articles or dissertations published in English with statistics from which relevant effect sizes could be calculated. Discrepancies were resolved by consensus. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Two authors independently coded all titles and abstracts, reviewed full-text articles against the inclusion and exclusion criteria, and resolved all discrepancies by consensus. Effect sizes were transformed into log odds ratios (ORs) and analyzed using a random-effects meta-analysis and mixed-effects meta-regression. Study quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Publication bias was tested via the Egger z test for funnel plot asymmetry. Data analysis was performed in June 2023. Main Outcomes and Measures: The 2 main variables of interest in this study were screen time and ASD. Screen time was defined as hours of screen use per day or per week, and ASD was defined as an ASD clinical diagnosis (yes or no) or ASD symptoms. The meta-regression considered screen type (ie, general use of screens, television, video games, computers, smartphones, and social media), age group (children vs adults or heterogenous age groups), and type of ASD measure (clinical diagnosis vs ASD symptoms). Results: Of the 4682 records identified, 46 studies with a total of 562 131 participants met the inclusion criteria. The studies were observational (5 were longitudinal and 41 were cross-sectional) and included 66 relevant effect sizes. The meta-analysis resulted in a positive summary effect size (log OR, 0.54 [95% CI, 0.34 to 0.74]). A trim-and-fill correction for a significant publication bias (Egger z = 2.15; P = .03) resulted in a substantially decreased and nonsignificant effect size (log OR, 0.22 [95% CI, -0.004 to 0.44]). The meta-regression results suggested that the positive summary effect size was only significant in studies targeting general screen use (ß [SE] = 0.73 [0.34]; t58 = 2.10; P = .03). This effect size was most dominant in studies of children (log OR, 0.98 [95% CI, 0.66 to 1.29]). Interestingly, a negative summary effect size was observed in studies investigating associations between social media and ASD (log OR, -1.24 [95% CI, -1.51 to -0.96]). Conclusions and Relevance: The findings of this systematic review and meta-analysis suggest that the proclaimed association between screen use and ASD is not sufficiently supported in the existing literature. Although excessive screen use may pose developmental risks, the mixed findings, the small effect sizes (especially when considering the observed publication bias), and the correlational nature of the available research require further scientific investigation. These findings also do not rule out the complementary hypothesis that children with ASD may prioritize screen activities to avoid social challenges.

Social Media Images Can Predict Suicide Risk Using Interpretable Large Language-Vision Models.

Background: Suicide, a leading cause of death and a major public health concern, became an even more pressing matter since the emergence of social media two decades ago and, more recently, following the hardships that characterized the COVID-19 crisis. Contemporary studies therefore aim to predict signs of suicide risk from social media using highly advanced artificial intelligence (AI) methods. Indeed, these new AI-based studies managed to break a longstanding prediction ceiling in suicidology; however, they still have principal limitations that prevent their implementation in real-life settings. These include "black box" methodologies, inadequate outcome measures, and scarce research on non-verbal inputs, such as images (despite their popularity today).Objective: This study aims to address these limitations and present an interpretable prediction model of clinically valid suicide risk from images.Methods: The data were extracted from a larger dataset from May through June 2018 that was used to predict suicide risk from textual postings. Specifically, the extracted data included a total of 177,220 images that were uploaded by 841 Facebook users who completed a gold-standard suicide scale. The images were represented with CLIP (Contrastive Language-Image Pre-training), a state-of-the-art deep-learning algorithm, which was utilized, unconventionally, to extract predefined interpretable features (eg, "photo of sad people") that served as inputs to a simple logistic regression model.Results: The results of this hybrid model that integrated theory-driven features with bottom-up methods indicated high prediction performance that surpassed common deep learning algorithms (area under the receiver operating characteristic curve [AUC] = 0.720, Cohen d = 0.82). Further analyses supported a theory-driven hypothesis that at-risk users would have images with increased negative emotions and decreased belongingness.Conclusions: This study provides a first proof that publicly available images can be leveraged to predict validated suicide risk. It also provides simple and flexible strategies that could enhance the development of real-life monitoring tools for suicide.

Perspective changes in human listeners are aligned with the contextual transformation of the word embedding space.

Word embedding representations have been shown to be effective in predicting human neural responses to lingual stimuli. While these representations are sensitive to the textual context, they lack the extratextual sources of context such as prior knowledge, thoughts, and beliefs, all of which constitute the listener's perspective. In this study, we propose conceptualizing the listeners' perspective as a source that induces changes in the embedding space. We relied on functional magnetic resonance imaging data collected by Yeshurun Y, Swanson S, Simony E, Chen J, Lazaridi C, Honey CJ, Hasson U. Same story, different story: the neural representation of interpretive frameworks. Psychol Sci. 2017:28(3):307-319, in which two groups of human listeners (n = 40) were listening to the same story but with different perspectives. Using a dedicated fine-tuning process, we created two modified versions of a word embedding space, corresponding to the two groups of listeners. We found that each transformed space was better fitted with neural responses of the corresponding group, and that the spatial distances between these spaces reflect both interpretational differences between the perspectives and the group-level neural differences. Together, our results demonstrate how aligning a continuous embedding space to a specific context can provide a novel way of modeling listeners' intrinsic perspectives.

Mothers' Perceptions of Children's Screen Use During the COVID-19 Lockdown in Israel.

The contemporary parenting challenge of regulating children's screen time became even more difficult during the coronavirus pandemic (COVID-19). The current research addresses the characteristics of this challenge and explores mothers' perceptions regarding their children's screen use, through two consecutive studies. Study 1 included 299 mothers of elementary school children, who were asked to complete questionnaires regarding their children's screen habits. Mothers were also asked about their own attitudes towards screens, as parents, and about their personal feelings of frustration and guilt. Study 2 replicated this procedure among a new sample of 283 mothers who also completed validated scales assessing their sense of parental competence and authority style. Retrospective reports of mothers indicated that, during the lockdown, entertainment use of screens increased by 73% among 4th-6th graders and by 108% among 1st-3rd graders. Educational use increased by 86% in both age groups. Mothers' guilt increased as well and was predicted by children's entertainment use (but not educational use), after accounting for demographic variables and mothers' attitudes. Other factors, such as parenting style and having at-least one child with a diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD), were associated with entertainment use (regardless of the COVID-19 lockdown). Factors that were found to moderate the lockdown effect were mothers' attitudes towards screens and parental confidence. The findings are discussed in the context of parents' efforts to regulate their children's screen use.

Neural Correlates of Numerical Estimation: The Role of Strategy Use.

INTRODUCTION: Computation estimation is the ability to provide an approximate answer to a complex arithmetic problem without calculating it exactly. Despite its importance in daily life, the neuronal network underlying computation estimation is largely unknown. METHODS: We looked at the neuronal correlates of two computational estimation strategies: approximated calculation and sense of magnitude (SOM)-intuitive representation of magnitude, without calculation. During an fMRI scan, thirty-one college students judged whether the result of a two-digit multiplication problem was larger or smaller than a given reference number. In two different blocks, they were asked to use a specific strategy (AC or SOM). RESULTS: The two strategies activated brain regions related to calculation, numerical cognition, decision-making, and working memory. AC more than SOM elicited activations in multiple, domain-specific brain regions in the parietal lobule, including the left SMG (BA 40), the bilateral superior parietal lobule (BA 7), and the right inferior parietal lobule (BA 7). The activation level of the IFG was positively correlated to individual accuracy, indicating that the IFG has an essential role in both strategies. CONCLUSIONS: These finding suggest that the analogic code of magnitude is more involved in the AC than the SOM strategy.

Perceived Epistemic Authority (Source Credibility) of a TV Interviewer Moderates the Media Bias Effect Caused by His Nonverbal Behavior.

The Media Bias Effect (MBE) represents the biasing influence of the nonverbal behavior of a TV interviewer on viewers' impressions of the interviewee. In the MBE experiment, participants view a 4-min made-up political interview in which they are exposed only to the nonverbal behavior of the actors. The interviewer is friendly toward the politician in one experimental condition and hostile in the other. The interviewee was a confederate filmed in the same studio, and his clips are identical in the two conditions. This experiment was used successfully in a series of studies in several countries (Babad and Peer in J Nonverbal Behav 34(1):57-78, 2010. 10.1007/s10919-009-0078-x) and was administered in the present research. The present investigation focused on the interviewer's source credibility and its persuasive influence. The viewers filled out questionnaires about their impressions of both the interviewer and the interviewee. A component of "interviewer's authority" was derived in PCA, with substantial variance in viewers' impressions of the interviewer. In our design, we preferred the conception of Epistemic Authority (Kruglanski et al. in Adv Exp Soc Psychol 37:345-392, 2005)-based on viewers' subjective perceptions for deriving authority status-to the conventional design of source credibility studies, where dimensions of authority are pre-determined as independent variables. The results demonstrated that viewers who perceived the interviewer as an effective leader demonstrated a clear MBE and were susceptible to his influencing bias, but no bias effect was found for viewers who did not perceive the interviewer as an effective leader. Thus, Epistemic Authority (source credibility) moderated the Media Bias Effect.

Deep neural networks detect suicide risk from textual facebook posts.

Detection of suicide risk is a highly prioritized, yet complicated task. Five decades of research have produced predictions slightly better than chance (AUCs = 0.56-0.58). In this study, Artificial Neural Network (ANN) models were constructed to predict suicide risk from everyday language of social media users. The dataset included 83,292 postings authored by 1002 authenticated Facebook users, alongside valid psychosocial information about the users. Using Deep Contextualized Word Embeddings for text representation, two models were constructed: A Single Task Model (STM), to predict suicide risk from Facebook postings directly (Facebook texts → suicide) and a Multi-Task Model (MTM), which included hierarchical, multilayered sets of theory-driven risk factors (Facebook texts → personality traits → psychosocial risks → psychiatric disorders → suicide). Compared with the STM predictions (0.621 ≤ AUC ≤ 0.629), the MTM produced significantly improved prediction accuracy (0.697 ≤ AUC ≤ 0.746), with substantially larger effect sizes (0.729 ≤ d ≤ 0.936). Subsequent content analyses suggested that predictions did not rely on explicit suicide-related themes, but on a range of text features. The findings suggest that machine learning based analyses of everyday social media activity can improve suicide risk predictions and contribute to the development of practical detection tools.