Disrupted computations of social control in individuals with obsessive-compulsive and misophonia symptoms

Banker, Sarah; Na, Soojung; Beltrán, Jacqueline; Koenigsberg, Harold W.; Foss‐Feig, Jennifer H.; Gu, Xiaosi; Schiller, Daniela

doi:10.1016/j.isci.2022.104617

Cited by 6 publications

(6 citation statements)

References 44 publications

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“…This difference suggests that self-reported autism symptoms in the general population may be more re ective of general social avoidance and self-consciousness regarding social skills rather than autismspeci c social di culties. Supporting the existence of this phenotype, largescale online studies investigating latent psychiatric factors in the general population have identi ed transdiagnostic dimensions characterized by similar socially-avoidant/anxious traits 39,24 . As we have shown, these online participants who report elevated internal social di culties (i.e., emotional or cognitive struggles that others may not notice, as described by self-report) also show different social behaviors from those with a clinical diagnosis who show elevated external di culties (i.e., inappropriate actions or visible struggles, as described by clinician-report), suggesting the diagnosis and the dimension are not synonymous.…”

Section: Discussionmentioning

confidence: 99%

“…Social controllability, or one's ability to in uence other people, is crucial for achieving optimal behavior during dynamic interactions and subsequently, mental wellbeing. To measure social controllability, we used an monetary exchange task [23][24][25][26] modi ed from the ultimatum game, in which participants decide whether to accept or reject proposed splits of $20 offered by players from two independent teams (Fig. 2A; see Methods for details).…”

Section: Social Controllabilitymentioning

confidence: 99%

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Phenotypical divergence between self-reported and clinically ascertained autism

Banker,

Schafer,

Harrington

et al. 2024

Preprint

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While allowing for rapid recruitment of large samples, online psychiatric and neurodevelopmental research relies heavily on participants’ self-report of neuropsychiatric symptoms, foregoing the rigorous clinical characterization of laboratory settings. Autism spectrum disorder (ASD) research is one example where the clinical validity of such an approach remains elusive. Here, we compared participants characterized online via self-reports against in-person participants evaluated by clinicians. Despite having comparable self-reported autism symptoms, the online high-trait group reported significantly more social anxiety and avoidant behavior than in-person ASD subjects. Within the in-person sample, there was no relationship between self-rated and clinician-rated autism symptoms, suggesting these approaches may capture different aspects of ASD. The online high-trait and in-person ASD participants also differed in their behavior in well-validated social decision-making tasks: the in-person group perceived having less social control and acted less affiliative towards virtual characters. Our study aimed to draw comparisons at three levels: methodological platform (online versus in-person), symptom measurement (self- versus clinician-report), and social behavior. We identified a lack of agreement between self- and clinician-rated measures of symptoms and divergent social tendencies in groups ascertained by each method, highlighting the need for differentiation between in-person versus online samples in autism research.

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Section: Discussionmentioning

confidence: 99%

Section: Social Controllabilitymentioning

confidence: 99%

Phenotypical divergence between self-reported and clinically ascertained autism

Banker,

Schafer,

Harrington

et al. 2024

Preprint

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“…On the data science side, especially in the field of machine learning and artificial intelligence, the predictive principles and structure of neural and behavioral data are surfacing at faster rates with few human formal assumptions 15 . This computational approach, which Meehl would likely classify under actuarial methods, now provides experimental capacity for researchers to analyze natural language, vocal acoustics, facial expressions, neural signals, behavioral task, and smartphone data for diagnostic purposes 16-21 . The launching of the Research Domain Criteria (RDoC) program in 2009 by the National Institute of Mental Health to generate a transdiagnostic framework for classifying mental illness represents the field’s turn toward quantitatively interpreting the large amount of newly available data 22 …”

Section: Meehl’s Legacy For Scientists and Clinicians Studying Human ...mentioning

confidence: 99%

“…15 This computational approach, which Meehl would likely classify under actuarial methods, now provides experimental capacity for researchers to analyze natural language, vocal acoustics, facial expressions, neural signals, behavioral task, and smartphone data for diagnostic purposes. [16][17][18][19][20][21] The launching of the Research Domain Criteria (RDoC) program in 2009 by the National Institute of Mental Health to generate a transdiagnostic framework for classifying mental illness represents the field's turn toward quantitatively interpreting the large amount of newly available data. 22 In parallel, scientists argue that data require plausible models if they are to be understood and explained.…”

Section: Remembering Paul E Meehlmentioning

confidence: 99%

Remembering Paul E. Meehl: Historical Contributions to Predictive Modeling in Human Behavior

Ryu

2023

Harv Rev Psychiatry

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This article briefly examines the life and work of the late clinical psychologist and philosopher of science Paul E. Meehl. His thesis in Clinical versus Statistical Prediction (1954) that the data combination performed by mechanical operations, as compared to clinicians, achieves higher accuracy in predicting human behavior is one of the earliest theoretical works that laid the groundwork for utilizing statistics and computational modeling in research in psychiatry and clinical psychology. For today’s psychiatric researchers and clinicians grappling with the challenges of translating the ever-increasing data of the human mind into practice tools, Meehl’s advocacy for both accurate modeling of the data and their clinically relevant use is timely.

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“…While some researchers describe misophonia as a symptom of other disorders such as obsessive-compulsive disorder (Banker et al, 2022), autism spectrum disorder (Williams et al, 2021) or tinnitus (Raj-Koziak et al, 2021), others have highlighted that misophonia may occur in the absence of any clinical or neurological condition (Jastreboff & Jastreboff, 2015). In fact, it may present without any pathology to the auditory system (Duddy & Oeding, 2014) and should therefore be a classified, standalone disorder (Schröder et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Investigating Sound-Specific Attention Control in Mild to Moderate Misophonia Using a Modified Attention Network Test

Zumbrunn,

McGovern,

Boran

2023

Preprint

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A growing body of evidence suggests that attentional differences might be a core mechanism of misophonia, a condition characterised by negative psychological responses to bodily and/or repetitive sounds. However, it is unclear whether aberrant attentional processing affects people with misophonia (PwM) globally or only in the context of symptom provocation. Moreover, it is unclear whether attentional differences also exist for those with less severe misophonia levels. Here, a modified Attention Network Test with misophonic (e.g., chewing), aversive (e.g., baby crying), and neutral (e.g., birds singing) sounds was administered to explore whether mild/moderate misophonics have differing alerting, orienting, and executive control responses when listening to misophonia sounds compared to other types of sounds, as well as in comparison to a control group. Additionally, we explored whether the presence of specific types of triggers impacted attentional processing. In doing so, our results show a different pattern of alerting and orienting behaviour in PwM compared to the control group. Furthermore, PwM showed less efficient executive control upon hearing misophonia-eliciting sounds compared to aversive sounds. We propose that this difference in processing between groups reflects unpredictable threats in PwM. However, both a facilitation and distracting effect of the sound are possible explanations, which require further investigation.

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Disrupted computations of social control in individuals with obsessive-compulsive and misophonia symptoms

Cited by 6 publications

References 44 publications

Phenotypical divergence between self-reported and clinically ascertained autism

Phenotypical divergence between self-reported and clinically ascertained autism

Remembering Paul E. Meehl: Historical Contributions to Predictive Modeling in Human Behavior

Investigating Sound-Specific Attention Control in Mild to Moderate Misophonia Using a Modified Attention Network Test

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