Acceptability of RealConsent: A Sexual Violence Web-Based Risk Reduction Program for College Women.

Acceptability of a sexual violence (SV) risk reduction program called RealConsent designed for first-year female college students was conducted as part of a larger, randomized controlled trial. RealConsent uses web-based and mobile technology to deliver four 45-minute theoretically and empirically derived modules designed to increase knowledge, affect attitudes and normative beliefs, increase bystander and other protective behaviors, and reduce alcohol misuse. Educational entertainment is used throughout the program to achieve these aims. A total of 444 first-year female college students recruited from three Southeastern universities who were eligible and provided informed consent were randomized to RealConsent. Acceptability was ascertained both quantitatively and qualitatively through a survey administered following the completion of each of four modules. Results suggest that overall, RealConsent was viewed as relatable, realistic, and relevant. Most participants rated modules as good/excellent in quality, in organization, and in the conveying of a high degree of knowledge regarding alcohol misuse, consent for sex, sexual communication, defense strategies, protective strategies, and intervening to prevent SV. Suggestions to improve RealConsent centered on having more content inclusive of sexual and gender-minoritized students. Results suggest that RealConsent is an acceptable SV risk reduction program among first-year female college students and may have advantages for dissemination over in-person programs due to its web-based and mobile technology.

Large-scale encoding of emotion concepts becomes increasingly similar between individuals from childhood to adolescence.

Humans require a shared conceptualization of others' emotions for adaptive social functioning. A concept is a mental blueprint that gives our brains parameters for predicting what will happen next. Emotion concepts undergo refinement with development, but it is not known whether their neural representations change in parallel. Here, in a sample of 5-15-year-old children (n = 823), we show that the brain represents different emotion concepts distinctly throughout the cortex, cerebellum and caudate. Patterns of activation to each emotion changed little across development. Using a model-free approach, we show that activation patterns were more similar between older children than between younger children. Moreover, scenes that required inferring negative emotional states elicited higher default mode network activation similarity in older children than younger children. These results suggest that representations of emotion concepts are relatively stable by mid to late childhood and synchronize between individuals during adolescence.

EmoCodes: a Standardized Coding System for Socio-emotional Content in Complex Video Stimuli.

Social information processing is vital for inferring emotional states in others, yet affective neuroscience has only begun to scratch the surface of how we represent emotional information in the brain. Most previous affective neuroscience work has used isolated stimuli such as static images of affective faces or scenes to probe affective processing. While this work has provided rich insight to the initial stages of emotion processing (encoding cues), activation to isolated stimuli provides limited insight into later phases of emotion processing such as interpretation of cues or interactions between cues and established cognitive schemas. Recent work has highlighted the potential value of using complex video stimuli to probe socio-emotional processing, highlighting the need to develop standardized video coding schemas as this exciting field expands. Toward that end, we present a standardized and open-source coding system for complex videos, two fully coded videos, and a video and code processing Python library. The EmoCodes manual coding system provides an externally validated and replicable system for coding complex cartoon stimuli, with future plans to validate the system for other video types. The emocodes Python library provides automated tools for extracting low-level features from video files as well as tools for summarizing and analyzing the manual codes for suitability of use in neuroimaging analysis. Materials can be freely accessed at https://emocodes.org/. These tools represent an important step toward replicable and standardized study of socio-emotional processing using complex video stimuli. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-021-00100-7.