Boys don't cry (or kiss or dance): A computational linguistic lens into gendered actions in film.

Contemporary media is full of images that reflect traditional gender notions and stereotypes, some of which may perpetuate harmful gender representations. In an effort to highlight the occurrence of these adverse portrayals, researchers have proposed machine-learning methods to identify stereotypes in the language patterns found in character dialogues. However, not all of the harmful stereotypes are communicated just through dialogue. As a complementary approach, we present a large-scale machine-learning framework that automatically identifies character's actions from scene descriptions found in movie scripts. For this work, we collected 1.2+ million scene descriptions from 912 movie scripts, with more than 50 thousand actions and 20 thousand movie characters. Our framework allow us to study systematic gender differences in movie portrayals at a scale. We show this through a series of statistical analyses that highlight differences in gender portrayals. Our findings provide further evidence to claims from prior media studies including: (i) male characters display higher agency than female characters; (ii) female actors are more frequently the subject of gaze, and (iii) male characters are less likely to display affection. We hope that these data resources and findings help raise awareness on portrayals of character actions that reflect harmful gender stereotypes, and demonstrate novel possibilities for computational approaches in media analysis.

Representation of professions in entertainment media: Insights into frequency and sentiment trends through computational text analysis.

Societal ideas and trends dictate media narratives and cinematic depictions which in turn influence people's beliefs and perceptions of the real world. Media portrayal of individuals and social institutions related to culture, education, government, religion, and family affect their function and evolution over time as people perceive and incorporate the representations from portrayals into their everyday lives. It is important to study media depictions of social structures so that they do not propagate or reinforce negative stereotypes, or discriminate against a particular section of the society. In this work, we examine media representation of different professions and provide computational insights into their incidence, and sentiment expressed, in entertainment media content. We create a searchable taxonomy of professional groups, synsets, and titles to facilitate their retrieval from short-context speaker-agnostic text passages like movie and television (TV) show subtitles. We leverage this taxonomy and relevant natural language processing models to create a corpus of professional mentions in media content, spanning more than 136,000 IMDb titles over seven decades (1950-2017). We analyze the frequency and sentiment trends of different occupations, study the effect of media attributes such as genre, country of production, and title type on these trends, and investigate whether the incidence of professions in media subtitles correlate with their real-world employment statistics. We observe increased media mentions over time of STEM, arts, sports, and entertainment occupations in the analyzed subtitles, and a decreased frequency of manual labor jobs and military occupations. The sentiment expressed toward lawyers, police, and doctors showed increasing negative trends over time, whereas the mentions about astronauts, musicians, singers, and engineers appear more favorably. We found that genre is a good predictor of the type of professions mentioned in movies and TV shows. Professions that employ more people showed increased media frequency.

Robust diagnostic classification via Q-learning.

Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability, being able to audit and understand the decision function, and robustness, being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q-learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.

Is Increased Response Time Variability Related to Deficient Emotional Self-Regulation in Children With ADHD?

Objective: Elevated response time intrasubject variability (RT-ISV) characterizes ADHD. Deficient emotional self-regulation (DESR), defined by summating Child Behavior Checklist Anxious/Depressed, Aggressive, and Attention subscale scores, has been associated with worse outcome in ADHD. To determine if DESR is differentially associated with elevated RT-ISV, we examined RT-ISV in children with ADHD with and without DESR and in typically developing children (TDC). Method: We contrasted RT-ISV during a 6-min Eriksen Flanker Task in 31 children with ADHD without DESR, 34 with ADHD with DESR, and 65 TDC. Results: Regardless of DESR, children with ADHD showed significantly greater RT-ISV than TDC (p < .001). The ADHD subgroups, defined by presence or absence of DESR, did not differ from each other. Conclusion: Increased RT-ISV characterizes ADHD regardless of comorbid DESR. Alongside similar findings in children and adults with ADHD, these results suggest that RT-ISV is related to cognitive rather than emotional dysregulation in ADHD.

Identifying Therapist and Client Personae for Therapeutic Alliance Estimation.

Psychotherapy, from a narrative perspective, is the process in which a client relates an on-going life-story to a therapist. In each session, a client will recount events from their life, some of which stand out as more significant than others. These significant stories can ultimately shape one's identity. In this work we study these narratives in the context of therapeutic alliance-a self-reported measure on the perception of a shared bond between client and therapist. We propose that alliance can be predicted from the interactions between certain types of clients with types of therapists. To validate this method, we obtained 1235 transcribed sessions with client-reported alliance to train an unsupervised approach to discover groups of therapists and clients based on common types of narrative characters, or personae. We measure the strength of the relation between personae and alliance in two experiments. Our results show that (1) alliance can be explained by the interactions between the discovered character types, and (2) models trained on therapist and client personae achieve significant performance gains compared to competitive supervised baselines. Finally, exploratory analysis reveals important character traits that lead to an improved perception of alliance.

Computerized cognitive training for children with neurofibromatosis type 1: A pilot resting-state fMRI study.

In this pilot study, we examined training effects of a computerized working memory program on resting state functional magnetic resonance imaging (fMRI) measures in children with neurofibromatosis type 1 (NF1). We contrasted pre- with post-training resting state fMRI and cognitive measures from 16 participants (nine males; 11.1 ± 2.3 years) with NF1 and documented working memory difficulties. Using non-parametric permutation test inference, we found significant regionally specific differences (family-wise error corrected) in two of four voxel-wise resting state measures: fractional amplitude of low frequency fluctuations (indexing peak-to-trough intensity of spontaneous oscillations) and regional homogeneity (indexing local intrinsic synchrony). Some cognitive task improvement was observed as well. These preliminary findings suggest that regionally specific changes in resting state fMRI indices may be associated with treatment-related cognitive amelioration in NF1. Nevertheless, current results must be interpreted with caution pending independent controlled replication.

Test-retest repeatability of human speech biomarkers from static and real-time dynamic magnetic resonance imaging.

Static anatomical and real-time dynamic magnetic resonance imaging (RT-MRI) of the upper airway is a valuable method for studying speech production in research and clinical settings. The test-retest repeatability of quantitative imaging biomarkers is an important parameter, since it limits the effect sizes and intragroup differences that can be studied. Therefore, this study aims to present a framework for determining the test-retest repeatability of quantitative speech biomarkers from static MRI and RT-MRI, and apply the framework to healthy volunteers. Subjects (n = 8, 4 females, 4 males) are imaged in two scans on the same day, including static images and dynamic RT-MRI of speech tasks. The inter-study agreement is quantified using intraclass correlation coefficient (ICC) and mean within-subject standard deviation (σe). Inter-study agreement is strong to very strong for static measures (ICC: min/median/max 0.71/0.89/0.98, σe: 0.90/2.20/6.72 mm), poor to strong for dynamic RT-MRI measures of articulator motion range (ICC: 0.26/0.75/0.90, σe: 1.6/2.5/3.6 mm), and poor to very strong for velocities (ICC: 0.21/0.56/0.93, σe: 2.2/4.4/16.7 cm/s). In conclusion, this study characterizes repeatability of static and dynamic MRI-derived speech biomarkers using state-of-the-art imaging. The introduced framework can be used to guide future development of speech biomarkers. Test-retest MRI data are provided free for research use.

The Neural Correlates of Emotional Lability in Children with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is exceptionally heterogeneous in both clinical and physiopathological presentations. Clinical variability applies to ASD-specific symptoms and frequent comorbid psychopathology such as emotional lability (EL). To date, the physiopathological underpinnings of the co-occurrence of EL and ASD are unknown. As a first step, we examined within-ASD inter-individual variability of EL and its neuronal correlates using resting-state functional magnetic resonance imaging (R-fMRI). We analyzed R-fMRI data from 58 children diagnosed with ASD (5-12 years) in relation to the Conners' Parent Rating Scale EL index. We performed both an a priori amygdala region-of-interest (ROI) analysis, and a multivariate unbiased whole-brain data-driven approach. While no significant brain-behavior relationships were identified regarding amygdala intrinsic functional connectivity (iFC), multivariate whole-brain analyses revealed an extended functional circuitry centered on two regions: middle frontal gyrus (MFG) and posterior insula (PI). Follow-up parametric and nonparametric ROI-analyses of these regions revealed relationships between EL and MFG- and PI-iFC with default, salience, and visual networks suggesting that higher-order cognitive and somatosensory processes are critical for emotion regulation in ASD. We did not detect evidence of amygdala iFC underpinning EL in ASD. However, exploratory whole-brain analyses identified large-scale networks that have been previously reported abnormal in ASD. Future studies should consider EL as a potential source of neuronal heterogeneity in ASD and focus on multinetwork interactions.

Mode of Anisotropy Reveals Global Diffusion Alterations in Attention-Deficit/Hyperactivity Disorder.

OBJECTIVE: Diffusion tensor imaging (DTI) can identify structural connectivity alterations in attention-deficit/hyperactivity disorder (ADHD). Most ADHD DTI studies have concentrated on regional differences in fractional anisotropy (FA) despite its limited sensitivity to complex white matter architecture and increasing evidence of global brain differences in ADHD. Here, we examine multiple DTI metrics in separate samples of children and adults with and without ADHD with a principal focus on global between-group differences. METHOD: Two samples: adults with ADHD (n = 42) and without (n = 65) and children with ADHD (n = 82) and without (n = 80) were separately group matched for age, sex, and head motion. Five DTI metrics (FA, axial diffusivity, radial diffusivity, mean diffusivity, and mode of anisotropy) were analyzed via tract-based spatial statistics. Group analyses tested for diagnostic differences at the global (averaged across the entire white matter skeleton) and regional level for each metric. RESULTS: Robust global group differences in diffusion indices were found in adults, with the largest effect size for mode of anisotropy (MA; Cohen's d = 1.45). Global MA also differed significantly between groups in the pediatric sample (d = 0.68). In both samples, global MA increased classification accuracy compared to the model with clinical Conners' ADHD ratings alone. Regional diagnostic differences did not survive familywise correction for multiple comparisons. CONCLUSION: Global DTI metrics, particularly the mode of anisotropy, which is sensitive to crossing fibers, capture connectivity abnormalities in ADHD across both pediatric and adult samples. These findings highlight potential diffuse white matter microarchitecture differences in ADHD.

Short-term test-retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder.

To date, only one study has examined test-retest reliability of resting state fMRI (R-fMRI) in children, none in clinical developing groups. Here, we assessed short-term test-retest reliability in a sample of 46 children (11-17.9 years) with attention-deficit/hyperactivity disorder (ADHD) and 57 typically developing children (TDC). Our primary test-retest reliability measure was the intraclass correlation coefficient (ICC), quantified for a range of R-fMRI metrics. We aimed to (1) survey reliability within and across diagnostic groups, and (2) compare voxel-wise ICC between groups. We found moderate-to-high ICC across all children and within groups, with higher-order functional networks showing greater ICC. Nearly all R-fMRI metrics exhibited significantly higher ICC in TDC than in children with ADHD for one or more regions. In particular, posterior cingulate and ventral precuneus exhibited group differences in ICC across multiple measures. In the context of overall moderate-to-high test-retest reliability in children, regional differences in ICC related to diagnostic groups likely reflect the underlying pathophysiology for ADHD. Our currently limited understanding of the factors contributing to inter- and intra-subject variability in ADHD underscores the need for large initiatives aimed at examining their impact on test-retest reliability in both clinical and developing populations.