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Item does not contain fulltext How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. 18 p.

Pre-registration document structured as the first half of a traditional manuscript (introduction, overview and hypotheses, methods, and plans for analyses sections).

Supplemental material, sj-pdf-1-cpx-10.1177_21677026221103128 for Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial by Jeremy W. Eberle, Mehdi Boukhechba, Jianhui Sun, Diheng Zhang, Daniel H. Funk, Laura E. Barnes and Bethany A. Teachman in Clinical Psychological Science

This preregistration outlines the background, main research questions, hypotheses, method, and analytic plan for this project.

The overall purpose of this research study is to understand perceptions of MindTrails (https://mindtrails.virginia.edu), an existing web-based interpretation training program for anxiety, among individuals who identify as Asian-American and Pacific Islander (AAPI), and to obtain feedback about what modifications might help improve the fit of the program for AAPI users.

The primary aim of this pilot study is to evaluate the feasibility and acceptability of a culturally enhanced, Spanish translation of a two-session, web-based cognitive bias modification for interpretation (CBM-I) intervention (called MindTrails; https://mindtrails.virginia.edu/) in a sample of Latinx individuals with a history of moderate to severe anxiety in the past two years. In line with Stage 1 of the National Institutes of Health Stage Model, this pilot study involved developing the culturally enhanced, Spanish translation version of MindTrails and pilot testing it to obtain feasibility and acceptability data, with the ultimate goal of enabling more rigorous testing of its efficacy in subsequent studies.

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety disorder. In this study, we present results using passively collected data from a within-subject experiment that assessed physiological response across different social contexts (i.e, alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study ($N=46$) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale ($\geq$34 out of 80). Univariate paired difference tests, multivariate random forest models, and follow-up cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, but that there is considerable variability in physiological response patterns even among these distinguishable contexts. Implications for real-world context detection and deployment of JITAIs are discussed.

Supplemental material, sj-pdf-1-cpx-10.1177_21677026221103128 for Shifting Episodic Prediction With Online Cognitive Bias Modification: A Randomized Controlled Trial by Jeremy W. Eberle, Mehdi Boukhechba, Jianhui Sun, Diheng Zhang, Daniel H. Funk, Laura E. Barnes and Bethany A. Teachman in Clinical Psychological Science

This pre-registration provides an overview of the study background, hypotheses, and analytic plan.

This is a preregistration. It describes our background, method, and analytic plan.

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way., Comment: IEEE EMBC 2023

Pre-registration

Cognitive-behavioral theories of emotional disorders assert that emotional responding comprises concordant responses (e.g., co-occurring subjective distress and physiological arousal), but this is often not observed in practice. We investigated whether concordance would be greater when social threat is higher. A sample of 46 socially anxious participants underwent experiences involving varying degrees of social interaction and evaluative threat. Affect, cognitions, behaviors, and physiological responses were assessed repeatedly. We used a network modeling approach to examine the associations among emotion response components that are typically associated with trait social anxiety. After estimating contemporaneous partial-𝜏 networks for each condition, we identified the optimal modularity statistic with absolute thresholding. Permutation testing was used to investigate hypotheses tied to absolute and relative concordance. Concordance was defined as a lack of community structure as measured by a non-significant modularity statistic, indicating a unidimensional anxiety response. Contrary to hypotheses, absolute concordance was not observed in any of the conditions involving social-evaluative threat but was observed in the non-social control network. Additionally, no significant differences in relative concordance emerged when comparing explicit evaluation vs. non-explicit evaluation or social vs. non-social networks. Our findings align with the extant literature suggesting that concordance is not a necessary or common feature of emotional episodes and have implications for our theoretical understanding of social anxiety. Future work should explore individual differences in concordance and interactions among components across different timescales, as well as the replicability of our findings in larger, more diverse samples.

This document contains the draft introduction, a priori hypotheses, and a priori analysis plans.

This replication study aims to examine the use of emotion regulation strategies in daily life across various contexts (i.e., location, time of day, social context).

This addendum ("Toner 2nd Prereg Addendum OSF") reflects updates to the original preregistration and first addendum for this project. These changes were made following an initial set of analyses which were run in preparation for an abstract submission due 3/31/22. See attached file for detailed description of changes.

The present study will examine how demographic and clinical characteristics (e.g. gender, age, anxiety) affect engagement with TeleCoaches in MindTrails, an online Cognitive Bias Modification for interpretation program.

This registration outlines the literature background, study procedures and measures, hypotheses, and plan for analysis.

This pre-registration includes a description of the study background, aims, hypotheses, and analytic plan.

This project is Study 2 in a two-part study designed to investigate implicit and explicit beliefs about the effectiveness (vs. unhelpfulness) of therapy (vs. medication) in a large, diverse sample of individuals. Participants were adults who visited the Project Implicit Mental Health website (https://implicit.harvard.edu/implicit/user/pimh/index.jsp) between 7/5/16 and 5/15/19, and chose the Treatment IAT study labeled “Do you implicitly favor medication or talk therapy?” An exploratory analysis of data collected from participants between September 2011 and November 2015 (Study 1) was first conducted to evaluate the Treatment IAT, and to investigate whether therapy-as-effective implicit associations varied as a function of age, gender, race, ethnicity, education, and mental health history. Based on these results, Study 2 aims to replicate Study 1’s findings in a more recent sample of visitors to Project Implicit Mental Health. In addition, several important changes to the mental health history questionnaire have been made to allow for a more nuanced and accurate evaluation of individuals’ mental health history. Specifically, in Study 1, participants had to choose whether they struggled with mental or emotional difficulties currently or in the past, and whether they received any help for mental or emotional difficulties currently or previously, without allowing them to select both current and previous options. In Study 2, these questions were modified to allow participants to choose previous, current, or both. This would capture, for instance, someone who is currently depressed and has also had prior episodes. Additionally, the mental health history questionnaire now asks whether an individual has struggled with specific mental health diagnoses (using a checklist of common diagnoses) rather than mental health or emotional difficulties broadly, and includes additional questions on the duration of mental health difficulties, and on the helpfulness of each mental health help-seeking experience. These changes were made to allow for a more thorough investigation of how implicit and explicit attitudes toward therapy and medication vary based on one’s mental health history. Thus, in addition to replicating findings from Study 1, Study 2 will test how implicit and explicit mental health treatment attitudes vary based on these new mental health history variables.

We will examine the effect of the extent participants report that they relate to training scenarios on primary and secondary outcome measures in a multi-session online Cognitive Bias Modification for Interpretation (CBM-I) training program. We may also examine rate of change in the extent to which participants relate to training scenarios on primary and secondary outcome measures as a secondary analysis. We plan to use two separate samples of community participants with moderate-to-severe anxiety to analyze this research question. Study 1 (Managing Anxiety) participants were enrolled in an eight-session version of the CBM-I program (called MindTrails; https://mindtrails.virginia.edu/) between May 2016 and May 2018 (see Ji et al., 2021). Study 2 (Calm Thinking) participants were enrolled in a five-session version of the CBM-I program between January 2019 and April 2020 (see preregistration for analyses for the full RCT at https://osf.io/af4n7).

Pre-registration for a secondary data analysis investigating emotion regulation diversity in the context of social anxiety disorder.

This is the first addendum to the preregistered project "Revisiting emotional concordance: Examining response patterns across individuals, contexts, and time in a socially anxious sample".

Using text messages and other digital data to identify and predict acute suicide risk

Mental health problems are highly prevalent among college student populations and likely increasing in frequency and severity. The present study, named DemonicSalmon, investigates how social anxiety and depression symptoms manifest in the daily life of 72 students over a two-week study period.

This preregistration outlines the background, main research questions, hypotheses, method, and analytic plan for this project.

This is a pre-registration for the planned replication of the examination of COVID-19 implicit associations and their relationships with mental health variables.

This pre-registration includes a description of the study background, aims, hypotheses, variables, and analytic plan.

The present study examines three novel brief implicit association tests (BIATs; Sriram & Greenwald, 2009) that were developed to examine relatively automatic, less consciously-controlled associations related to COVID-19. The primary goal of this manuscript is to examine whether the novel COVID-19 BIATs incrementally predict self-reported measures of mental health (i.e., anxiety, depression symptoms) above and beyond explicit reports of beliefs about COVID-19. Also, to characterize the new BIAT measures and better understand their psychometric properties, we will examine how the novel BIATs relate to self-reported beliefs, behaviors, and history with COVID-19 and examine how these BIATs vary based on demographic variables. COVID-19 has had negative implications for mental health globally. Compared to early 2019, anxiety and depression were more prevalent during the initial months of the COVID-19 pandemic, with more than one third of adults screening positive for depressive disorders, anxiety disorders, or both in April and May of 2020 (Twenge & Joiner, 2020). Research on relatively more automatic and less consciously controlled measures of attitudes suggests that implicit associations tied to health predict health behavior intentions beyond explicit reports (Ratliff & Howell, 2015) and predict health information avoidance beyond explicit reports (Howell et al., 2016). Given that there are changes in mental health in the face of the global pandemic, we were interested in examining how implicit associations tied to the pandemic would relate to measures of mental health. Examining these relationships may provide some insight into cognitive processes associated with increased psychopathology during an international pandemic. Moreover, by understanding how relatively automatic and less-consciously controlled attitudes relate to psychopathology, we can better create interventions for reducing the immense burden of mental health challenges during a pandemic. Data collection started on 4/1/2020 following IRB approval to run this study. Hypotheses were drafted in October/November 2020 prior to any of the authors analyzing the data. Data was downloaded on 11/1/2020 for this manuscript (data collection is ongoing at the website). See attached file for more detailed information about the planned data anlyses.

This is a revised/updated registration

Open-ended preregistration