Computational Modeling Differentiates Learning Rate From Reward Sensitivity Deficits Produced by Early-Life Adversity in a Rodent Touchscreen Probabilistic Reward Task.

Background: Exposure to adversity, including unpredictable environments, during early life is associated with neuropsychiatric illness in adulthood. One common factor in this sequela is anhedonia, the loss of responsivity to previously reinforcing stimuli. To accelerate the development of new treatment strategies for anhedonic disorders induced by early-life adversity, animal models have been developed to capture critical features of early-life stress and the behavioral deficits that such stressors induce. We have previously shown that rats exposed to the limited bedding and nesting protocol exhibited blunted reward responsivity in the probabilistic reward task, a touchscreen-based task reverse translated from human studies. Methods: To test the quantitative limits of this translational platform, we examined the ability of Bayesian computational modeling and probability analyses identical to those optimized in previous human studies to quantify the putative mechanisms that underlie these deficits with precision. Specifically, 2 parameters that have been shown to independently contribute to probabilistic reward task outcomes in patient populations, reward sensitivity and learning rate, were extracted, as were trial-by-trial probability analyses of choices as a function of the preceding trial. Results: Significant deficits in reward sensitivity, but not learning rate, contributed to the anhedonic phenotypes in rats exposed to early-life adversity. Conclusions: The current findings confirm and extend the translational value of these rodent models by verifying the effectiveness of computational modeling in distinguishing independent features of reward sensitivity and learning rate that complement the probabilistic reward task's signal detection end points. Together, these metrics serve to objectively quantify reinforcement learning deficits associated with anhedonic phenotypes.

Exposure to early-life adversity can lead to psychiatric illness, including anhedonia, the loss of pleasure from previously rewarding activities. This article describes findings from rats exposed to a model of simulated poverty on a touchscreen-based assay reverse translated from a task used to characterize anhedonia in humans. We documented the ability of Bayesian computational modeling and probability analyses, identical to those used with humans, to objectively quantify reinforcement learning deficits associated with anhedonia in rats.

From palm to practice: prescription digital therapeutics for mental and brain health at the National Institutes of Health.

Prescription Digital Therapeutics (PDTs) are emerging as promising tools for treating and managing mental and brain health conditions within the context of daily life. This commentary distinguishes PDTs from other Software as Medical Devices (SaMD) and explores their integration into mental and brain health treatments. We focus on research programs and support from the National Institutes of Health (NIH), discussing PDT research supported by the NIH's National Institute on Child Health and Development (NICHD), National Institute of Mental Health (NIMH), and National Institute on Aging (NIA). We present a hierarchical natural language processing topic analysis of NIH-funded digital therapeutics research projects. We delineate the PDT landscape across different mental and brain health disorders while highlighting opportunities and challenges. Additionally, we discuss the research foundation for PDTs, the unique therapeutic approaches they employ, and potential strategies to improve their validity, reliability, safety, and effectiveness. Finally, we address the research and collaborations necessary to propel the field forward, ultimately enhancing patient care through innovative digital health solutions.

A computational account of the development and evolution of psychotic symptoms.

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

A hierarchical reinforcement learning model explains individual differences in attentional set shifting.

Attentional set shifting refers to the ease with which the focus of attention is directed and switched. Cognitive tasks, such as the widely used CANTAB IED, reveal great variation in set shifting ability in the general population, with notable impairments in those with psychiatric diagnoses. The attentional and learning processes underlying this cognitive ability and how they lead to the observed variation remain unknown. To directly test this, we used a modelling approach on two independent large-scale online general-population samples performing CANTAB IED, with one including additional psychiatric symptom assessment. We found a hierarchical model that learnt both feature values and dimension attention best explained the data and that compulsive symptoms were associated with slower learning and higher attentional bias to the first relevant stimulus dimension. These data showcase a new methodology to analyse data from the CANTAB IED task, as well as suggest a possible mechanistic explanation for the variation in set shifting performance, and its relationship to compulsive symptoms.

(Mal)adaptive Mentalizing in the Cognitive Hierarchy, and Its Link to Paranoia.

Humans need to be on their toes when interacting with competitive others to avoid being taken advantage of. Too much caution out of context can, however, be detrimental and produce false beliefs of intended harm. Here, we offer a formal account of this phenomenon through the lens of Theory of Mind. We simulate agents of different depths of mentalizing within a simple game theoretic paradigm and show how, if aligned well, deep recursive mentalization gives rise to both successful deception as well as reasonable skepticism. However, we also show that if a self is mentalizing too deeply - hyper-mentalizing - false beliefs arise that a partner is trying to trick them maliciously, resulting in a material loss to the self. Importantly, we show that this is only true when hypermentalizing agents believe observed actions are generated intentionally. This theory offers a potential cognitive mechanism for suspiciousness, paranoia, and conspiratorial ideation. Rather than a deficit in Theory of Mind, paranoia may arise from the application of overly strategic thinking to ingenuous behaviour. Author Summary: Interacting competitively requires vigilance to avoid deception. However, excessive caution can have adverse effects, stemming from false beliefs of intentional harm. So far there is no formal cognitive account of what may cause this suspiciousness. Here we present an examination of this phenomenon through the lens of Theory of Mind - the cognitive ability to consider the beliefs, intentions, and desires of others. By simulating interacting computer agents we illustrate how well-aligned agents can give rise to successful deception and justified skepticism. Crucially, we also reveal that overly cautious agents develop false beliefs that an ingenuous partner is attempting malicious trickery, leading to tangible losses. As well as formally defining a plausible mechanism for suspiciousness, paranoia, and conspiratorial thinking, our theory indicates that rather than a deficit in Theory of Mind, paranoia may involve an over-application of strategy to genuine behaviour.

Artificial intelligence and psychedelic medicine.

Artificial intelligence (AI) and psychedelic medicines are among the most high-profile evolving disruptive innovations within mental healthcare in recent years. Although AI and psychedelics may not have historically shared any common ground, there exists the potential for these subjects to combine in generating innovative mental health treatment approaches. In order to inform our perspective, we conducted a scoping review of relevant literature up to late August 2024 via PubMed intersecting AI with psychomedical use of psychedelics. Our perspective covers the potential application of AI in psychedelic medicine for: drug discovery and clinical trial optimization (including pharmacodynamics); study design; understanding psychedelic experiences; personalization of treatments; clinical screening, delivery, and follow-up (potentially delivered via chatbots/apps); application of psychological preparation, integration, and general mental health support; its role in enhancing treatment via brain modulatory devices (including virtual reality and haptic suits); and the consideration of ethical and security safeguards. Challenges include the need for sufficient data protection and security, and a range of necessary ethical protections. Future avenues of exploration could involve directly administering psychedelics (or providing algorithm-generated effects) to inorganic AI-interfaced neural networks that may exceed human brain activity (i.e., cognitive capacity) and intelligence.

Hacking the Predictive Mind.

According to active inference, constantly running prediction engines in our brain play a large role in delivering all human experience. These predictions help deliver everything we see, hear, touch, and feel. In this paper, I pursue one apparent consequence of this increasingly well-supported view. Given the constant influence of hidden predictions on human experience, can we leverage the power of prediction in the service of human flourishing? Can we learn to hack our own predictive regimes in ways that better serve our needs and purposes? Asking this question rapidly reveals a landscape that is at once familiar and new. It is also challenging, suggesting important questions about scope and dangers while casting further doubt (as if any was needed) on old assumptions about a firm mind/body divide. I review a range of possible hacks, starting with the careful use of placebos, moving on to look at chronic pain and functional disorders, and ending with some speculations concerning the complex role of genetic influences on the predictive brain.

Spectral changes in electroencephalography linked to neuroactive medications: A computational pipeline for data mining and analysis.

BACKGROUND AND OBJECTIVES: The increasing amount of open-access medical data provides new opportunities to gain clinically relevant information without recruiting new patients. We developed an open-source computational pipeline, that utilizes the publicly available electroencephalographic (EEG) data of the Temple University Hospital to identify EEG profiles associated with the usage of neuroactive medications. It facilitates access to the data and ensures consistency in data processing and analysis, thus reducing the risk of errors and creating comparable and reproducible results. Using this pipeline, we analyze the influence of common neuroactive medications on brain activity. METHODS: The pipeline is constructed using easily controlled modules. The user defines the medications of interest and comparison groups. The data is downloaded and preprocessed, spectral features are extracted, and statistical group comparison with visualization through a topographic EEG map is performed. The pipeline is adjustable to answer a variety of research questions. Here, the effects of carbamazepine and risperidone were statistically compared with control data and with other medications from the same classes (anticonvulsants and antipsychotics). RESULTS: The comparison between carbamazepine and the control group showed an increase in absolute and relative power for delta and theta, and a decrease in relative power for alpha, beta, and gamma. Compared to antiseizure medications, carbamazepine showed an increase in alpha and theta for absolute powers, and for relative powers an increase in alpha and theta, and a decrease in gamma and delta. Risperidone compared with the control group showed a decrease in absolute and relative power for alpha and beta and an increase in theta for relative power. Compared to antipsychotic medications, risperidone showed a decrease in delta for absolute powers. These results show good agreement with state-of-the-art research. The database allows to create large groups for many different medications. Additionally, it provides a collection of records labeled as "normal" after expert assessment, which is convenient for the creation of control groups. CONCLUSIONS: The pipeline allows fast testing of different hypotheses regarding links between medications and EEG spectrum through ecological usage of readily available data. It can be utilized to make informed decisions about the design of new clinical studies.

Sensorimotor Feedback Control Dysfunction as a Marker of Posttraumatic Stress Disorder.

BACKGROUND: Posttraumatic stress disorder (PTSD) is characterized not only by its direct association with traumatic events but also by a potential deficit in inhibitory control across emotional, cognitive, and sensorimotor domains. Recent research has shown that a continuous sensorimotor feedback control task, the rapid assessment of motor processing paradigm, can yield reliable measures of individual sensorimotor control performance. This study used this paradigm to investigate control deficits in PTSD compared with both a healthy volunteer group and a non-PTSD psychiatric comparison group. METHODS: We examined control processing using the rapid assessment of motor processing paradigm in a sample of 40 individuals with PTSD, matched groups of 40 individuals with mood and anxiety complaints, and 40 healthy control participants. We estimated Kp (drive) and Kd (damping) parameters using a proportional-derivative control modeling approach. RESULTS: The Kp parameter was lower in the PTSD group than in the healthy control (Cohen's d = 0.86) and mood and anxiety (Cohen's d = 0.63) groups. After controlling for color-word inhibition, Kp remained lower in the PTSD group than in the healthy control (Cohen's d = 0.79) and mood and anxiety (Cohen's d = 0.62) groups. Mediation analysis showed that Kd significantly mediated the relationship between PTSD and control deficits in the Kp parameter, with 96% of the effect being mediated by Kd. CONCLUSIONS: These findings underscore the potential of using dynamic control paradigms to elucidate the control dysfunctions in PTSD and suggest that different psychiatric conditions may distinctly influence subcomponents of sensorimotor control.

Data-driven sequence of cognitive decline in people with Parkinson's disease.

BACKGROUND: Understanding the sequential progression of cognitive impairments in Parkinson's disease (PD) is crucial for elucidating neuropathological underpinnings, refining the assessment of PD-related cognitive decline stages and enhancing early identification for targeted interventions. The first aim of this study was to use an innovative event-based modeling (EBM) analytic approach to estimate the sequence of cognitive declines in PD. The second aim was to validate the EBM by examining associations with EBM-derived individual-specific estimates of cognitive decline severity and performance on independent cognitive screening measures. METHODS: This cross-sectional observational study included 99 people with PD who completed a neuropsychological battery. Individuals were classified as meeting the criteria for mild cognitive impairment (PD-MCI) or subtle cognitive decline by consensus. An EBM was constructed to compare cognitively healthy individuals with those with PD-MCI or subtle cognitive disturbances. Multivariable linear regression estimated associations between the EBM-derived stage of cognitive decline and performance on two independent cognitive screening tests. RESULTS: The EBM estimated that tests assessing executive function and visuospatial ability become abnormal early in the sequence of PD-related cognitive decline. Each higher estimated stage of cognitive decline was associated with approximately 0.24 worse performance on the Dementia Rating Scale (p<0.001) and 0.26 worse performance on the Montreal Cognitive Assessment (p<0.001) adjusting for demographic and clinical variables. CONCLUSION: Findings from this study will have important clinical implications for practitioners, on specific cognitive tests to prioritise, when conducting neuropsychological evaluations with people with PD. Results also highlight the importance of frontal-subcortical system disruption impacting executive and visuospatial abilities.

Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia.

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Clarifying the longitudinal factor structure, temporal stability, and construct validity of Go/No-Go task-related neural activation across adolescence and young adulthood.

This study aimed to clarify the psychometric properties and development of Go/No-Go (GNG) task-related neural activation across critical periods of neurobiological maturation by examining its longitudinal stability, factor structure, developmental change, and associations with a computational index of task-general cognitive control. A longitudinal sample (N=289) of adolescents from the Michigan Longitudinal Study was assessed at four time-points (mean number of timepoints per participant=2.05; standard deviation=0.89) spanning early adolescence (ages 10-13) to young adulthood (22-25). Results suggested that regional neural activations from the "successful inhibition" (SI>GO) and "failed inhibition" (FI>GO; error-monitoring) contrasts are each described well by a single general factor. Neural activity across both contrasts showed developmental increases throughout adolescence that plateau in young adulthood. Neural activity metrics evidenced low temporal stability across this period of marked developmental change, and the SI>GO factor showed no relations with a behavioral index of cognitive control. The FI>GO factor displayed stronger criterion validity in the form of significant, positive associations with behaviorally measured cognitive control. Findings emphasize the utility of well-validated psychometric methods and longitudinal data for clarifying the measurement properties of functional neuroimaging metrics and improving measurement practices in developmental cognitive neuroscience.

A computational account of the development and evolution of psychotic symptoms.

The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.

Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study.

BACKGROUND: Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. OBJECTIVE: Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. METHODS: We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). RESULTS: Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. CONCLUSIONS: This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/53857.

Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia.

The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot's motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a 'behavioral trap'. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.

Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.

BACKGROUND: Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS: Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS: Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS: We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective: Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method: MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results: The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions: Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

Perceptual Observer Modeling Reveals Likely Mechanisms of Face Expression Recognition Deficits in Depression.

BACKGROUND: Deficits in face emotion recognition are well documented in depression, but the underlying mechanisms are poorly understood. Psychophysical observer models provide a way to precisely characterize such mechanisms. Using model-based analyses, we tested 2 hypotheses about how depression may reduce sensitivity to detect face emotion: 1) via a change in selectivity for visual information diagnostic of emotion or 2) via a change in signal-to-noise ratio in the system performing emotion detection. METHODS: Sixty adults, one half meeting criteria for major depressive disorder and the other half healthy control participants, identified sadness and happiness in noisy face stimuli, and their responses were used to estimate templates encoding the visual information used for emotion identification. We analyzed these templates using traditional and model-based analyses; in the latter, the match between templates and stimuli, representing sensory evidence for the information encoded in the template, was compared against behavioral data. RESULTS: Estimated happiness templates produced sensory evidence that was less strongly correlated with response times in participants with depression than in control participants, suggesting that depression was associated with a reduced signal-to-noise ratio in the detection of happiness. The opposite results were found for the detection of sadness. We found little evidence that depression was accompanied by changes in selectivity (i.e., information used to detect emotion), but depression was associated with a stronger influence of face identity on selectivity. CONCLUSIONS: Depression is more strongly associated with changes in signal-to-noise ratio during emotion recognition, suggesting that deficits in emotion detection are driven primarily by deprecated signal quality rather than suboptimal sampling of information used to detect emotion.

The explore/exploit trade-off: An ecologically valid and translational framework that can advance mechanistic understanding of eating disorders.

The explore/exploit trade-off is a decision-making process that is conserved across species and balances exploring unfamiliar choices of unknown value with choosing familiar options of known value to maximize reward. This framework is rooted in behavioral ecology and has traditionally been used to study maladaptive versus adaptive non-human animal foraging behavior. Researchers have begun to recognize the potential utility of understanding human decision-making and psychopathology through the explore/exploit trade-off. In this article, we propose that explore/exploit trade-off holds promise for advancing our mechanistic understanding of decision-making processes that confer vulnerability for and maintain eating pathology due to its neurodevelopmental bases, conservation across species, and ability to be mathematically modeled. We present a model for how suboptimal explore/exploit decision-making can promote disordered eating and present recommendations for future research applying this framework to eating pathology. Taken together, the explore/exploit trade-off provides a translational framework for expanding etiologic and maintenance models of eating pathology, given developmental changes in explore/exploit decision-making that coincide in time with the emergence of eating pathology and evidence of biased explore/exploit decision-making in psychopathology. Additionally, understanding explore/exploit decision-making in eating disorders may improve knowledge of their underlying pathophysiology, informing targeted clinical interventions such as neuromodulation and pharmacotherapy. PUBLIC SIGNIFICANCE STATEMENT: The explore/exploit trade-off is a cross-species decision-making process whereby organisms choose between a known option with a known reward or sampling unfamiliar options. We hypothesize that imbalanced explore/exploit decision-making can promote disordered eating and present preliminary data. We propose that explore/exploit trade-off has significant potential to advance understanding of the neurocognitive and neurodevelopmental mechanisms of eating pathology, which could ultimately guide revisions of etiologic models and inform novel interventions.

El balance entre explorar y explotar es un proceso de toma de decisiones que se conserva a través de las especies y equilibra la exploración de opciones desconocidas de valor desconocido con la elección de opciones familiares de valor conocido para maximizar la recompensa. Este marco está arraigado en la ecología del comportamiento y tradicionalmente se ha utilizado para estudiar el comportamiento de forrajeo no adaptativo versus adaptativo en animales no humanos. Los investigadores han comenzado a reconocer la utilidad potencial de entender la toma de decisiones humanas y la psicopatología a través del balance entre explorar y explotar. En este artículo, proponemos que el balance entre explorar y explotar ofrece promesas para avanzar en nuestra comprensión mecanicista de los procesos de toma de decisiones que confieren vulnerabilidad y mantienen la patología alimentaria debido a sus bases neurodesarrolladoras, su conservación a través de las especies y su capacidad de ser modelado matemáticamente. Presentamos un modelo de cómo la toma de decisiones subóptima entre explorar y explotar puede promover la alimentación disfuncional y presentamos recomendaciones para futuras investigaciones que apliquen este marco a la patología alimentaria. En conjunto, el balance entre explorar y explotar proporciona un marco translacional para expandir los modelos etiológicos y de mantenimiento de la patología alimentaria, dadas los cambios en el desarrollo de la toma de decisiones entre explorar y explotar que coinciden en el tiempo con la aparición de la patología alimentaria y la evidencia de una toma de decisiones entre explorar y explotar sesgada en la psicopatología. Además, comprender la toma de decisiones entre explorar y explotar en los trastornos alimentarios puede mejorar el conocimiento de su fisiopatología subyacente, informando intervenciones clínicas dirigidas como la neuromodulación y la farmacoterapia.

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.