Towards a neurocomputational account of social controllability: From models to mental health

Phenotypic divergence between individuals with self-reported autistic traits and clinically ascertained autism

While allowing for rapid recruitment of large samples, online research relies heavily on participants' self-reports of neuropsychiatric traits, foregoing the clinical characterizations available in laboratory settings. Autism spectrum disorder (ASD) research is one example for which the clinical validity of such an approach remains elusive. Here we compared 56 adults with ASD recruited in person and evaluated by clinicians to matched samples of adults recruited through an online platform (Prolific; 56 with high autistic traits and 56 with low autistic traits) and evaluated via self-reported surveys. Despite having comparable self-reported autistic traits, the online high-trait group reported significantly more social anxiety and avoidant symptoms than in-person ASD participants. Within the in-person sample, there was no relationship between self-rated and clinician-rated autistic traits, suggesting they may capture different aspects of ASD. The groups also differed in their social tendencies during two decision-making tasks; the in-person ASD group was less perceptive of opportunities for social influence and acted less affiliative toward virtual characters. These findings highlight the need for a differentiation between clinically ascertained and trait-defined samples in autism research.

Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test-retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test-retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes.

Aberrant neural computation of social controllability in nicotine-dependent humans

Social controllability, or the ability to exert control during social interactions, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as smoking, which often takes place in social settings. Here, we examined social controllability in nicotine-dependent humans as they performed an fMRI task where they could influence the offers made by simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population. Pre-registrations: OSF Registries|How interoceptive state interacts with value-based decision-making in addiction (fMRI study). OSF Registries|COVID-19: social cognition, mental health, and social distancing (online study).

Phenotypical divergence between self-reported and clinically ascertained autism

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.

The Human Affectome

Over the last decades, theoretical perspectives in the interdisciplinary field of the affective sciences have proliferated rather than converged due to differing assumptions about what human affective phenomena are and how they work. These metaphysical and mechanistic assumptions, shaped by academic context and values, have dictated affective constructs and operationalizations. However, an assumption about the purpose of affective phenomena can guide us to a common set of metaphysical and mechanistic assumptions. In this capstone paper, we home in on a nested teleological principle for human affective phenomena in order to synthesize metaphysical and mechanistic assumptions. Under this framework, human affective phenomena can collectively be considered algorithms that either adjust based on the human comfort zone (affective concerns) or monitor those adaptive processes (affective features). This teleologically-grounded framework offers a principled agenda and launchpad for both organizing existing perspectives and generating new ones. Ultimately, we hope the Human Affectome brings us a step closer to not only an integrated understanding of human affective phenomena, but an integrated field for affective research.

Adolescents flexibly adapt action selection based on controllability inferences

From early in life, we encounter both controllable environments, in which our actions can causally influence the reward outcomes we experience, and uncontrollable environments, in which they cannot. Environmental controllability is theoretically proposed to organize our behavior. In controllable contexts, we can learn to proactively select instrumental actions that bring about desired outcomes. In uncontrollable environments, Pavlovian learning enables hard-wired, reflexive reactions to anticipated, motivationally salient events, providing "default" behavioral responses. Previous studies characterizing the balance between Pavlovian and instrumental learning systems across development have yielded divergent findings, with some studies observing heightened expression of Pavlovian learning during adolescence and others observing a reduced influence of Pavlovian learning during this developmental stage. In this study, we aimed to investigate whether a theoretical model of controllability-dependent arbitration between learning systems might explain these seemingly divergent findings in the developmental literature, with the specific hypothesis that adolescents' action selection might be particularly sensitive to environmental controllability. To test this hypothesis, 90 participants, aged 8-27, performed a probabilistic-learning task that enables estimation of Pavlovian influence on instrumental learning, across both controllable and uncontrollable conditions. We fit participants' data with a reinforcement-learning model in which controllability inferences adaptively modulate the dominance of Pavlovian versus instrumental control. Relative to children and adults, adolescents exhibited greater flexibility in calibrating the expression of Pavlovian bias to the degree of environmental controllability. These findings suggest that sensitivity to environmental reward statistics that organize motivated behavior may be heightened during adolescence.

Aberrant neural computation of social controllability in nicotine-dependent humans

Social controllability, defined as the ability to exert influence when interacting with others, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as drug use, which often takes place in social settings. Here, we examined nicotine-dependent humans using fMRI, as they made choices that could influence the proposals from simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population.