Sexism in the Digital World: A Thematic Content Analysis of the Cyberbullying of Lil Miquela

As artificial intelligence (AI) evolves, social issues associated with this technology are also perpetuated. This is especially pertinent to online violence against women. In April 2016, fictional character “Lil Miquela,” an AI robot who would quickly rise to fame as a social media influencer, was created using Computer-Generated Imagery (CGI). Significant backlash has persisted against Lil Miquela throughout its existence, which has typically taken the form of demeaning comments made by other users on Lil Miquela’s social media pages. As the robot looks deceptively like a human female, the robot receives online treatment that mimics that of human women. Utilizing thematic content analysis of comments left by users on Lil Miquela’s Instagram page from late April to early May 2019 (N= 2719), I argue that misogyny, under the guise of aggrieved entitlement, is a significant driver of the demeaning comments directed at Lil Miquela, and that this is reflective of misogyny towards women in general. Furthermore, I discuss the role of male peer support in perpetuating this misogyny. By creating a typology of the different types of sexism that women experience and applying it to the case of Lil Miquela, it demonstrates how comfortable users are to degrade the bot, simply because it is a female AI that cannot feel or respond to insulting comments. Ultimately, the cyberbullying of the bot does cause harm to human women because the bot is representative of human women. This study examines the following questions: (1) How prevalent is sexism in Lil Miquela’s Instagram comments? (2) How does Lil Miquela’s sexist treatment online represent the sexist treatment that human women face? It was discovered that 15.7% of the comments on Lil Miquela’s Instagram are sexist in nature. Of those sexist comments, the majority (91.5%) reflected hatred of women in general, while other comments (7.1%) objectified women by reducing Lil Miquela to female genitalia, and the remaining comments were outright sexually violent towards women (1.4%). Undoubtedly, this breakdown reflects the association between abusive male peers, aggrieved entitlement, and sexism online that contributes to violence towards women as a norm

Unilateral deafness in children affects development of multi-modal modulation and default mode networks

Monaural auditory input due to congenital or acquired unilateral hearing loss (UHL) may have neurobiological effects on the developing brain. Using functional magnetic resonance imaging (fMRI), we investigated the effect of UHL on the development of functional brain networks used for cross-modal processing. Children ages 7–12 with moderate or greater unilateral hearing loss of sensorineural origin (UHL-SN; N = 21) and normal-hearing controls (N = 23) performed an fMRI-compatible adaptation of the Token Test involving listening to a sentence such as “touched the small green circle and the large blue square” and simultaneously viewing an arrow touching colored shapes on a video. Children with right or severe-to-profound UHL-SN displayed smaller activation in a region encompassing the right inferior temporal, middle temporal, and middle occipital gyrus (BA 19/37/39), evidencing differences due to monaural hearing in cross-modal modulation of the visual processing pathway. Children with UHL-SN displayed increased activation in the left posterior superior temporal gyrus, likely the result either of more effortful low-level processing of auditory stimuli or differences in cross-modal modulation of the auditory processing pathway. Additionally, children with UHL-SN displayed reduced deactivation of anterior and posterior regions of the default mode network. Results suggest that monaural hearing affects the development of brain networks related to cross-modal sensory processing and the regulation of the default network during processing of spoken language

Left ear advantage in speech-related dichotic listening is not specific to auditory processing disorder in children: A machine-learning fMRI and DTI study

AbstractDichotic listening (DL) tests are among the most frequently included in batteries for the diagnosis of auditory processing disorders (APD) in children. A finding of atypical left ear advantage (LEA) for speech-related stimuli is often taken by clinical audiologists as an indicator for APD. However, the precise etiology of ear advantage in DL tests has been a source of debate for decades. It is uncertain whether a finding of LEA is truly indicative of a sensory processing deficit such as APD, or whether attentional or other supramodal factors may also influence ear advantage. Multivariate machine learning was used on diffusion tensor imaging (DTI) and functional MRI (fMRI) data from a cohort of children ages 7–14 referred for APD testing with LEA, and typical controls with right-ear advantage (REA). LEA was predicted by: increased axial diffusivity in the left internal capsule (sublenticular region), and decreased functional activation in the left frontal eye fields (BA 8) during words presented diotically as compared to words presented dichotically, compared to children with right-ear advantage (REA). These results indicate that both sensory and attentional deficits may be predictive of LEA, and thus a finding of LEA, while possibly due to sensory factors, is not a specific indicator of APD as it may stem from a supramodal etiology

Partially Adaptive STAP Algorithm Approaches to Functional MRI

In this work, the architectures of three partially adaptive STAP algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in construction of brain activation maps in fMRI. Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis

Optimized simultaneous ASL and BOLD functional imaging of the whole brain

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106990/1/jmri24273.pd

A Linear Structural Equation Model for Covert Verb Generation Based on Independent Component Analysis of fMRI Data from Children and Adolescents

Human language is a complex and protean cognitive ability. Young children, following well defined developmental patterns learn language rapidly and effortlessly producing full sentences by the age of 3 years. However, the language circuitry continues to undergo significant neuroplastic changes extending well into teenage years. Evidence suggests that the developing brain adheres to two rudimentary principles of functional organization: functional integration and functional specialization. At a neurobiological level, this distinction can be identified with progressive specialization or focalization reflecting consolidation and synaptic reinforcement of a network (Lenneberg, 1967; Muller et al., 1998; Berl et al., 2006). In this paper, we used group independent component analysis and linear structural equation modeling (McIntosh and Gonzalez-Lima, 1994; Karunanayaka et al., 2007) to tease out the developmental trajectories of the language circuitry based on fMRI data from 336 children ages 5–18 years performing a blocked, covert verb generation task. The results are analyzed and presented in the framework of theoretical models for neurocognitive brain development. This study highlights the advantages of combining both modular and connectionist approaches to cognitive functions; from a methodological perspective, it demonstrates the feasibility of combining data-driven and hypothesis driven techniques to investigate the developmental shifts in the semantic network

Functional connectivity during a social emotion task in adolescents and in adults

In this fMRI study we investigated functional connectivity between components of the mentalising system during a social emotion task, using psychophysiological interaction (PPI) analysis. Ten adults (22–32 years) and 18 adolescents (11–18 years) were scanned while thinking about scenarios in which a social or a basic emotion would be experienced. Unlike basic emotions (such as disgust and fear), social emotions (such as embarrassment and guilt) require the representation of another's mental states. In both adults and adolescents, an anterior rostral region of medial prefrontal cortex (arMPFC) involved in mentalising showed greater connectivity with the posterior superior temporal sulcus (pSTS) bordering on the temporo-parietal junction (TPJ) and with anterior temporal cortex (ATC) during social than during basic emotion. This result provides novel evidence that components of the mentalising system interact functionally during a social emotion task. Furthermore, functional connectivity differed between adolescence and adulthood. The adolescent group showed stronger connectivity between arMPFC and pSTS/TPJ during social relative to basic emotion than did the adult group, suggestive of developmental changes in functional integration within the mentalising system

Altered structural and functional connectivity in late preterm preadolescence: An anatomic seed-based study of resting state networks related to the posteromedial and lateral parietal cortex

Objective: Late preterm birth confers increased risk of developmental delay, academic difficulties and social deficits. The late third trimester may represent a critical period of development of neural networks including the default mode network (DMN), which is essential to normal cognition. Our objective is to identify functional and structural connectivity differences in the posteromedial cortex related to late preterm birth. Methods: Thirty-eight preadolescents (ages 9-13; 19 born in the late preterm period (≥32 weeks gestational age) and 19 at term) without access to advanced neonatal care were recruited from a low socioeconomic status community in Brazil. Participants underwent neurocognitive testing, 3-dimensional T1-weighted imaging, diffusion-weighted imaging and resting state functional MRI (RS-fMRI). Seed-based probabilistic diffusion tractography and RS-fMRI analyses were performed using unilateral seeds within the posterior DMN (posterior cingulate cortex, precuneus) and lateral parietal DMN (superior marginal and angular gyri). Results: Late preterm children demonstrated increased functional connectivity within the posterior default mode networks and increased anti-correlation with the central-executive network when seeded from the posteromedial cortex (PMC). Key differences were demonstrated between PMC components with increased anti-correlation with the salience network seen only with posterior cingulate cortex seeding but not with precuneus seeding. Probabilistic tractography showed increased streamlines within the right inferior longitudinal fasciculus and inferior fronto-occipital fasciculus within late preterm children while decreased intrahemispheric streamlines were also observed. No significant differences in neurocognitive testing were demonstrated between groups. Conclusion: Late preterm preadolescence is associated with altered functional connectivity from the PMC and lateral parietal cortex to known distributed functional cortical networks despite no significant executive neurocognitive differences. Selective increased structural connectivity was observed in the setting of decreased posterior interhemispheric connections. Future work is needed to determine if these findings represent a compensatory adaptation employing alternate neural circuitry or could reflect subtle pathology resulting in emotional processing deficits not seen with neurocognitive testing. Copyright

Evidence that neurovascular coupling underlying the BOLD effect increases with age during childhood

Functional MRI using blood–oxygen‐level‐dependent (BOLD) imaging has provided unprecedented insights into the maturation of the human brain. Task‐based fMRI studies have shown BOLD signal increases with age during development (ages 5–18) for many cognitive domains such as language and executive function, while functional connectivity (resting‐state) fMRI studies investigating regionally synchronous BOLD fluctuations have revealed a developing functional organization of the brain from a local into a more distributed architecture. However, interpretation of these results is confounded by the fact that the BOLD signal is directly related to blood oxygenation driven by changes in blood flow and only indirectly related to neuronal activity, and may thus be affected by changing neuronal–vascular coupling. BOLD signal and cerebral blood flow (CBF) were measured simultaneously in a cohort of 113 typically developing awake participants ages 3–18 performing a narrative comprehension task. Using a novel voxelwise wild bootstrap analysis technique, an increased ratio of BOLD signal to relative CBF signal change with age (indicative of increased neuronal–vascular coupling) was seen in the middle temporal gyri and the left inferior frontal gyrus. Additionally, evidence of decreased relative oxygen metabolism (indicative of decreased neuronal activity) with age was found in the same regions. These findings raise concern that results of developmental BOLD studies cannot be unambiguously attributed to neuronal activity. Astrocytes and astrocytic processes may significantly affect the maturing functional architecture of the brain, consistent with recent research demonstrating a key role for astrocytes in mediating increased CBF following neuronal activity and for astrocyte processes in modulating synaptic connectivity. Hum Brain Mapp, 36:1–15, 2015 . © 2014 Wiley Periodicals, Inc .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110113/1/hbm22608.pd

Functional Magnetic Resonance Imaging in Pediatrics

Functional magnetic resonance imaging (fMRI) allows non-invasive assessment of human brain function in vivo by detecting blood flow differences. In this review, we want to illustrate the background and different aspects of performing functional magnetic resonance imaging (fMRI) in the pediatric age group. An overview over current and future applications of fMRI will be given, and typical problems, pitfalls, and benefits of doing fMRI in the pediatric age group are discussed. We conclude that fMRI can successfully be applied in children and holds great promise for both research and clinical purposes