Subjective affective experience under threat is shaped by environmental affordances.

In this pre-registered study, we ask how people's emotional responses under threat may be causally affected by what is available to them in the environment, i.e. environmental affordances.

Using large language models to detect outcomes in qualitative studies of adolescent depression.

Objective: We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative enough to accurately label these experiences.

A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more.

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g.

Behavioral nudges in social media ads show limited ability to encourage COVID-19 vaccination across countries.

Behavioral nudges in Facebook ads reached nearly 15 million people across six diverse countries and, consequently, many thousands took the step of navigating to governments' vaccine signup sites. However, none of the treatment ads caused significantly more vaccine signup intent than placebo uniformly across all countries. Critically, reporting the descriptive norm that 87% of people worldwide had either been vaccinated or planned vaccination-social proof-did not meaningfully increase vaccine signup intent in any country and significantly backfired in Taiwan.

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets.

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as from resting state functional connectivity.