Individual differences in computational psychiatry: a review of current challenges

Using computational models of learning to advance cognitive behavioral therapy

Many psychotherapy interventions have a large evidence base and can help a substantial number of people with symptoms of mental health conditions. However, we still have little understanding of why treatments work. Early advances in psychotherapy, such as the development of exposure therapy, built on theoretical and experimental evidence from Pavlovian and instrumental conditioning. More generally, all psychotherapy achieves change through learning. The past 25 years have seen substantial developments in computational models of learning, with increased computational precision and a focus on multiple learning mechanisms and their interaction. Now might be a good time to formalize psychotherapy interventions as computational models of learning to improve our understanding of mechanisms of change in psychotherapy. To advance research and help bring together a new joint field of theory-driven computational psychotherapy, we first review literature on cognitive behavioral therapy (exposure therapy and cognitive restructuring) and introduce computational models of reinforcement learning and representation learning. We then suggest a mapping of these learning algorithms on change processes presumably underlying the effects of exposure therapy and cognitive restructuring. Finally, we outline how the understanding of interventions through the lens of learning algorithms can inform intervention research.

Individual differences in probabilistic learning and updating predictive representations in individuals with obsessive-compulsive tendencies

BACKGROUND

Obsessive-compulsive (OC) tendencies involve intrusive thoughts and rigid, repetitive behaviours that also manifest at the subclinical level in the general population. The neurocognitive factors driving the development and persistence of the excessive presence of these tendencies remain highly elusive, though emerging theories emphasize the role of implicit information processing. Despite various empirical studies on distinct neurocognitive processes, the incidental retrieval of environmental structures in dynamic and noisy environments, such as probabilistic learning, has received relatively little attention.

METHODS

In this study, we aimed to unravel potential individual differences in implicit probabilistic learning and the updating of predictive representations related to OC tendencies in a non-clinical population. We conducted two independent online experiments (NStudy1 = 164, NStudy2 = 256) with university students. Probabilistic learning was assessed using an implicit visuomotor probabilistic learning task, involving sequences with second-order non-adjacent dependencies.

RESULTS

Our findings revealed that implicit probabilistic learning remained remarkably robust among OC tendencies within a non-clinical population. Furthermore, the results highlighted effective updating capabilities of predictive representations, which were not influenced by OC tendencies.

CONCLUSIONS

These results offer new insights into individual differences in probabilistic learning and updating in relation to OC tendencies, contributing to theoretical, methodological, and practical approaches for understanding the maladaptive behavioural manifestations of OC disorder and subclinical tendencies.

A computational cognitive neuroscience approach for characterizing individual differences in autism: Introduction to Special Issue

Traditional psychological research has often treated inter-subject variability as statistical noise (even, nuisance variance), focusing instead on averages rather than individual differences. This approach has limited our understanding of the substantial heterogeneity observed in neuropsychiatric disorders, particularly autism spectrum disorder (ASD). In this introduction to a special issue on this theme, we discuss recent advances in cognitive computational neuroscience that can lead to a more systematic notion of core symptom dimensions that differentiate between ASD subtypes. These advances include large participant databases and data-sharing initiatives to increase sample sizes of autistic individuals across a wider range of cultural and socioeconomic backgrounds. Our perspective helps to build bridges between autism symptomatology and individual differences in autistic traits in the non-autistic population and introduces finer-grained dynamic methods to capture behavioral dynamics at the individual level. We specifically focus on how cognitive computational models have emerged as powerful tools to better characterize autistic traits in the general population and autistic population, particularly with respect to social decision-making. We finally outline how we can combine and harness these recent advances, on the one hand, big data initiatives, and on the other hand, cognitive computational models, to achieve a more systematic and nuanced understanding of autism that can lead to improved diagnostic accuracy and personalized interventions.

A computational approach to understanding effort-based decision-making in depression

Objective

Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes.

Methods

Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression.

Results

Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity.

Conclusions

This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

Day-to-day fluctuations in motivation drive effort-based decision-making

Internal states like motivation fluctuate substantially over time. However, studies of the neurocomputational mechanims of motivated behavior have failed to capture this. Here, we examined how naturalistic ups and downs in state motivation influence the subjective value of reward and effort. In a microlongitudinal design (N = 155, state timepoints = 3,344, decision-making tasks = 845), we captured fluctuations in state and effort-based decision-making using smartphone-based momentary assessments as people went about their daily lives. We found that both state and trait have independent and multiplicative effects on decision-making. State-behavior coupling was particularly pronounced in individuals with higher trait apathy, meaning that their choices were even more state dependent. Using computational modeling, we demonstrate that state motivation prospectively boosted reward sensitivity, making people more willing to exert effort in future. Our results show that day-to-day fluctuations in state and cognition are tightly linked and critical for understanding fundamental human behaviors and mental ill-health.

Test-retest reliability of decisions under risk with outcome evaluation: evidence from behavioral and event-related potentials (ERPs) measures in 2 monetary gambling tasks

The balance between potential gains and losses under risk, the stability of risk propensity, the associated reward processing, and the prediction of subsequent risk behaviors over time have become increasingly important topics in recent years. In this study, we asked participants to carry out 2 risk tasks with outcome evaluation-the monetary gambling task and mixed lottery task twice, with simultaneous recording of behavioral and electroencephalography data. Regarding risk behavior, we observed both individual-specific risk attitudes and outcome-contingent risky inclination following a loss outcome, which remained stable across sessions. In terms of event-related potential (ERP) results, low outcomes, compared to high outcomes, induced a larger feedback-related negativity, which was modulated by the magnitude of the outcome. Similarly, high outcomes evoked a larger deflection of the P300 compared to low outcomes, with P300 amplitude also being sensitive to outcome magnitude. Intraclass correlation coefficient analyses indicated that both the feedback-related negativity and P300 exhibited modest to good test-retest reliability across both tasks. Regarding choice prediction, we found that neural responses-especially those following a loss outcome-predicted subsequent risk-taking behavior at the single-trial level for both tasks. Therefore, this study extends our understanding of the reliability of risky preferences in gain-loss trade-offs.

Deep transcranial magnetic stimulation for adolescents with treatment-resistant depression: Behavioral and neural correlates of clinical improvement

BACKGROUND

Affective bias toward negativity is associated with depression and may represent a promising treatment target. Stimulating the dorsolateral prefrontal cortex (dlPFC) with deep Transcranial Magnetic Stimulation (dTMS) could lead to shifts in affective bias. The current study examined behavioral and neural correlates of affective bias in the context of dTMS in adolescents with treatment-resistant depression (TRD).

METHODS

Adolescents completed a Word-Face Stroop (WFS) task during an fMRI scan before and after 30 sessions of dTMS targeting the left dlPFC. In the task, participants were shown words superimposed on faces in either a "congruent" (both word and face were positive or both negative) or an "incongruent" fashion; in both cases, participants identified whether the words were positive or negative. We examined pre-post intervention neural and behavioral WFS changes and their correlations with clinical improvement.

RESULTS

Usable pre- and post-intervention WFS data were available for 10 adolescents with TRD (Age, years: M = 16.3, SD = 1.09) for behavioral data; 9 for neuroimaging data. After treatment, although changes in behavioral performance did not suggest improved affective bias, amygdala activation decreased during the negative word/happy face condition, which correlated with clinical improvement. Overall, clinical improvement correlated with decreased neural activation during congruent conditions.

LIMITATIONS

Major limitations include the small sample size, lack of a sham control group, and unknown psychometric properties.

CONCLUSIONS

Preliminary findings suggesting improving neural efficiency and normalizing affective bias in those with the most clinical improvement highlight the potential importance of targeting affective bias in treating adolescents with TRD.

Understanding learning through uncertainty and bias

Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning should be adjusted to different sources of uncertainty, including perceptual uncertainty, risk, and uncertainty due to environmental changes. Such models explain many hallmarks of human learning in terms of specific statistical considerations that come into play when updating predictions under uncertainty. However, humans also display systematic learning biases that deviate from normative models, as studied in computational psychiatry. Some biases can be explained as normative inference conditioned on inaccurate prior assumptions about the environment, while others reflect approximations to Bayesian inference aimed at reducing cognitive demands. These biases offer insights into cognitive mechanisms underlying learning and how they might go awry in psychiatric illness.

An Active Inference Model of the Optimism Bias

The optimism bias is a cognitive bias where individuals overestimate the likelihood of good outcomes and underestimate the likelihood of bad outcomes. Associated with improved quality of life, optimism bias is considered to be adaptive and is a promising avenue of research for mental health interventions in conditions where individuals lack optimism such as major depressive disorder. Here we lay the groundwork for future research on optimism as an intervention by introducing a domain general formal model of optimism bias, which can be applied in different task settings. Employing the active inference framework, we propose a model of the optimism bias as high precision likelihood biased towards positive outcomes. First, we simulate how optimism may be lost during development by exposure to negative events. We then ground our model in the empirical literature by showing how the developmentally acquired differences in optimism are expressed in a belief updating task typically used to assess optimism bias. Finally, we show how optimism affects action in a modified two-armed bandit task. Our model and the simulations it affords provide a computational basis for understanding how optimism bias may emerge, how it may be expressed in standard tasks used to assess optimism, and how it affects agents' decision-making and actions; in combination, this provides a basis for future research on optimism as a mental health intervention.

Deconstructing Delay Discounting in Human Cocaine Addiction Using Computational Modeling and Neuroimaging

BACKGROUND

A preference for sooner-smaller over later-larger rewards, known as delay discounting, is a candidate transdiagnostic marker of waiting impulsivity and a research domain criterion. While abnormal discounting rates have been associated with many psychiatric diagnoses and abnormal brain structure, the underlying neuropsychological processes remain largely unknown. Here, we deconstruct delay discounting into choice and rate processes by testing different computational models and investigate their associations with white matter tracts.

METHODS

Patients with cocaine use disorder (CUD) (n = 107) and healthy participants (n = 81) completed the Monetary Choice Questionnaire. We computed their discounting rate using the well-known Kirby method, as well as logistic regression, single-subject Bayesian, and full hierarchical Bayesian models. In Bayesian models, we also included a choice sharpness parameter. Seventy patients with CUD and 69 healthy participants also underwent diffusion tensor imaging tractography to quantify streamlines that connect the executive control and valuation brain networks.

RESULTS

Patients with CUD showed significantly higher discounting rates and lower choice sharpness, suggesting greater indifference in their choices. Importantly, the full Bayesian model had the greatest reliability for parameter recovery when compared to the Kirby and logistic regression methods. Using Bayesian estimates, we found that white matter streamlines that connect the executive control network with the nucleus accumbens predicted the discounting rate in healthy participants but not in patients with CUD.

CONCLUSIONS

We demonstrated that measuring delay discounting and choice sharpness directly with a novel computational model explained impulsive discounting choices in patients with CUD better than standard hyperbolic discounting. Our findings highlight a distinct neuropsychological phenotype of impulsive discounting, which may be generalizable to other patient groups.

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.

Psychotropic Taxonomies: Constructing a Therapeutic Framework for Psychiatry

Pharmacological interventions are a cornerstone of psychiatric practice. The taxonomies used to classify these interventions influence the treatment and interpretation of psychiatric symptoms. Disease-based classification systems (e.g., antidepressant and antipsychotic) do not reflect the fact that psychotropic agents are used across diagnostic categories or account for the dimensional nature of both the psychopathology and biology of psychiatric illnesses. In this review, we discuss the history of psychotropic drug taxonomies and their influence on both clinical practice and drug development. We frame taxonomies as existing on a spectrum, with high-level disease-based approaches at one end and target-based molecular approaches at the other. Finally, we consider how data-driven methods might address the issue of classification at an intermediate level, based around transdiagnostic neurobiological and psychopathological markers.

Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to characterize the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here, we address these fundamental questions by applying machine learning methods and functional magnetic resonance imaging (fMRI) to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from processes other than deception. Specifically, we demonstrate that discriminant validity is compromised by the predictor's ability to predict behavior in a control task that does not involve deception. Second, we show that the presence of confounding signals need not be fatal and that the validity of the neural predictor can be improved by removing confounding signals while retaining those associated with the task of interest. To this end, we develop a "dual-goal tuning" approach in which, beyond the typical goal of predicting the behavior of interest, the predictor also incorporates a second compulsory goal that enforces chance performance in the control task. Together, these findings provide a firmer scientific foundation for understanding the neural basis of a neglected class of behavior, and they suggest an approach for improving validity of neural predictors.

The influence of anxiety on exploration: A review of computational modeling studies

Exploratory behaviors can serve an adaptive role within novel or changing environments. Namely, they facilitate information gain, allowing an organism to maintain accurate beliefs about the environment and select actions that better maximize reward. However, finding the optimal balance between exploration and reward-seeking behavior - the so-called explore-exploit dilemma - can be challenging, as it requires sensitivity to one's own uncertainty and to the predictability of one's surroundings. Given the close relationship between uncertainty and anxiety, a body of work has now also emerged identifying associated effects on exploration. In particular, the field of computational psychiatry has begun to use cognitive computational models to characterize how anxiety may modulate underlying information processing mechanisms, such as estimation of uncertainty and the value of information, and how this might contribute to psychopathology. Here, we review computational modeling studies investigating how exploration is influenced by anxiety. While some apparent inconsistencies remain to be resolved, studies using reinforcement learning tasks suggest that directed (but not random) forms of exploration may be elevated by trait and/or cognitive anxiety, but reduced by state and/or somatic anxiety. Anxiety is also consistently associated with less exploration in foraging tasks. Some differences in exploration may further stem from how anxiety modulates changes in uncertainty over time (learning rates). Jointly, these results highlight important directions for future work in refining choice of tasks and anxiety measures and maintaining consistent methodology across studies.

Computational modeling of social evaluative decision-making elucidates individual differences in adolescent anxiety

Adolescents experience significant developmental changes during a time of heightened sensitivity to social cues, particularly rejection by peers, which can be especially overwhelming for those with elevated levels of social anxiety. Social evaluative decision-making tasks have been useful in uncovering the neural correlates of information processing biases; however, linking youths' task-based performance to individual differences in psychopathology (e.g., anxiety symptoms) has proven more elusive. Here, we address this weakness with drift diffusion modeling to decompose youths' performance on the social judgment paradigm (SJP) to determine if this approach is useful in discovering individual differences in anxiety symptoms, as well as puberty, age, and sex. A sample of 103 adolescents (55 males, Mage = 14.49, SD = 1.69) completed the SJP and self-report measures of anxiety, as well as self- and parent-reported measures of puberty. The decision threshold parameter, reflecting the amount of evidence needed to make a social evaluative decision, predicted youth self-reported anxiety, above and beyond typical metrics of SJP performance. Our results highlight the potential advantage of parsing task performance according to the underlying cognitive processes. Future research would likely benefit from applying computational modeling approaches to social judgment tasks when attempting to uncover performance-based individual differences in psychopathology.

Does the reliability of computational models truly improve with hierarchical modeling? Some recommendations and considerations for the assessment of model parameter reliability : Reliability of computational model parameters

Computational modeling of behavior is increasingly being adopted as a standard methodology in psychology, cognitive neuroscience, and computational psychiatry. This approach involves estimating parameters in a computational (or cognitive) model that represents the computational processes of the underlying behavior. In this approach, the reliability of the parameter estimates is an important issue. The use of hierarchical (Bayesian) approaches, which place a prior on each model parameter of the individual participants, is thought to improve the reliability of the parameters. However, the characteristics of reliability in parameter estimates, especially when individual-level priors are assumed, as in hierarchical models, have not yet been fully discussed. Furthermore, the suitability of different reliability measures for assessing parameter reliability is not thoroughly understood. In this study, we conduct a systematic examination of these issues through theoretical analysis and numerical simulations, focusing specifically on reinforcement learning models. We note that the heterogeneity in the estimation precision of individual parameters, particularly with priors, can skew reliability measures toward individuals with higher precision. We further note that there are two factors that reduce reliability, namely estimation error and intersession variation in the true parameters, and we discuss how to evaluate these factors separately. Based on the considerations of this study, we present several recommendations and cautions for assessing the reliability of the model parameters.

Test-retest reliability of behavioral and computational measures of advice taking under volatility

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk of undermining their use in empirical research and clinical translation. Here we investigated test-retest reliability (with a 2-week interval) of a computational assay probing advice-taking under volatility with a Hierarchical Gaussian Filter (HGF) model. In a sample of 39 healthy participants, we found the computational measures to have largely poor reliability (intra-class correlation coefficient or ICC < 0.5), on par with the behavioral measures of task performance. Further analysis revealed that reliability was substantially impacted by intrinsic measurement noise (indicated by parameter recovery analysis) and to a smaller extent by practice effects. However, a large portion of within-subject variance remained unexplained and may be attributable to state-like fluctuations. Despite the poor test-retest reliability, we found the assay to have face validity at the group level. Overall, our work highlights that the different sources of variance affecting test-retest reliability need to be studied in greater detail. A better understanding of these sources would facilitate the design of more psychometrically sound assays, which would improve the quality of future research and increase the probability of clinical translation.

Revisiting the role of computational neuroimaging in the era of integrative neuroscience

Computational models have become integral to human neuroimaging research, providing both mechanistic insights and predictive tools for human cognition and behavior. However, concerns persist regarding the ecological validity of lab-based neuroimaging studies and whether their spatiotemporal resolution is not sufficient for capturing neural dynamics. This review aims to re-examine the utility of computational neuroimaging, particularly in light of the growing prominence of alternative neuroscientific methods and the growing emphasis on more naturalistic behaviors and paradigms. Specifically, we will explore how computational modeling can both enhance the analysis of high-dimensional imaging datasets and, conversely, how neuroimaging, in conjunction with other data modalities, can inform computational models through the lens of neurobiological plausibility. Collectively, this evidence suggests that neuroimaging remains critical for human neuroscience research, and when enhanced by computational models, imaging can serve an important role in bridging levels of analysis and understanding. We conclude by proposing key directions for future research, emphasizing the development of standardized paradigms and the integrative use of computational modeling across neuroimaging techniques.

Psychiatric neuroimaging designs for individualised, cohort, and population studies

Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.

Temporal stability of Bayesian belief updating in perceptual decision-making

Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI [0.53, 0.85]) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.

Test-retest reliability of the play-or-pass version of the Iowa Gambling Task

The Iowa Gambling Task (IGT) is used to assess decision-making in clinical populations. The original IGT does not disambiguate reward and punishment learning; however, an adaptation of the task, the "play-or-pass" IGT, was developed to better distinguish between reward and punishment learning. We evaluated the test-retest reliability of measures of reward and punishment learning from the play-or-pass IGT and examined associations with self-reported measures of reward/punishment sensitivity and internalizing symptoms. Participants completed the task across two sessions, and we calculated mean-level differences and rank-order stability of behavioral measures across the two sessions using traditional scoring, involving session-wide choice proportions, and computational modeling, involving estimates of different aspects of trial-level learning. Measures using both approaches were reliable; however, computational modeling provided more insights regarding between-session changes in performance, and how performance related to self-reported measures of reward/punishment sensitivity and internalizing symptoms. Our results show promise in using the play-or-pass IGT to assess decision-making; however, further work is still necessary to validate the play-or-pass IGT.

Rethinking interpersonal judgments: dopamine antagonists impact attributional dynamics

Barnby et al. investigated the effects of haloperidol, a D2/D3 dopamine antagonist, on social attributions. Using computational modeling, they demonstrate that haloperidol increases belief flexibility, reducing paranoia-like interpretations by enhancing sensitivity to social context and reducing self-relevant perspective taking, offering a mechanistic explanation for its therapeutic potential in schizophrenia.

Reliable, rapid, and remote measurement of metacognitive bias

Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of 'anxious-depression' and 'compulsivity and intrusive thought', cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure visuo-perceptual metacognitive (confidence) bias and investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N = 52 paid participants). We assessed convergent validity, split-half and test-retest reliability, and identified the minimum number of trials required to capture its clinical correlates. Convergent validity of metacognitive bias was moderate (r(50) = 0.64, p < 0.001) and it demonstrated excellent split-half reliability (r(50) = 0.91, p < 0.001). Anxious-depression was associated with decreased confidence (β =  - 0.23, SE = 0.02, p < 0.001), while compulsivity and intrusive thought was associated with greater confidence (β = 0.07, SE = 0.02, p < 0.001). The associations between metacognitive biases and transdiagnostic psychiatry dimensions are evident in as few as 40 trials. Metacognitive biases in decision-making are stable within and across sessions, exhibiting very high test-retest reliability for the 100-trial (ICC = 0.86, N = 110) and 40-trial (ICC = 0.86, N = 120) versions of Meta Mind. Hybrid 'self-report cognition' tasks may be one way to bridge the recently discussed reliability gap in computational psychiatry.

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Assessing the Longitudinal Associations Between Decision-Making Processes and Attention Problems in Early Adolescence

Cognitive functions and psychopathology develop in parallel in childhood and adolescence, but the temporal dynamics of their associations are poorly understood. The present study sought to elucidate the intertwined development of decision-making processes and attention problems using longitudinal data from late childhood (9-10 years) to mid-adolescence (11-13 years) from the Adolescent Brain Cognitive Development (ABCD) Study (n = 8918). We utilised hierarchical drift-diffusion modelling of behavioural data from the stop-signal task, parent-reported attention problems from the Child Behavior Checklist (CBCL), and multigroup univariate and bivariate latent change score models. The results showed faster drift rate was associated with lower levels of inattention at baseline, as well as a greater reduction of inattention over time. Moreover, baseline drift rate negatively predicted change in attention problems in females, and baseline attention problems negatively predicted change in drift rate. Neither response caution (decision threshold) nor encoding- and responding processes (non-decision time) were significantly associated with attention problems. There were no significant sex differences in the associations between decision-making processes and attention problems. The study supports previous findings of reduced evidence accumulation in attention problems and additionally shows that development of this aspect of decision-making plays a role in developmental changes in attention problems in youth.

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.

Out of control: computational dynamic control dysfunction in stress- and anxiety-related disorders

Control theory, which has played a central role in technological progress over the last 150 years, has also yielded critical insights into biology and neuroscience. Recently, there has been a surging interest in integrating control theory with computational psychiatry. Here, we review the state of the field of using control theory approaches in computational psychiatry and show that recent research has mapped a neural control circuit consisting of frontal cortex, parietal cortex, and the cerebellum. This basic feedback control circuit is modulated by estimates of reward and cost via the basal ganglia as well as by arousal states coordinated by the insula, dorsal anterior cingulate cortex, amygdala, and locus coeruleus. One major approach within the broader field of control theory, known as proportion-integral-derivative (PID) control, has shown promise as a model of human behavior which enables precise and reliable estimates of underlying control parameters at the individual level. These control parameters correlate with self-reported fear and with both structural and functional variation in affect-related brain regions. This suggests that dysfunctional engagement of stress and arousal systems may suboptimally modulate parameters of domain-general goal-directed control algorithms, impairing performance in complex tasks involving movement, cognition, and affect. Future directions include clarifying the causal role of control deficits in stress- and anxiety-related disorders and developing clinically useful tools based on insights from control theory.

Listening to the Data: Computational Approaches to Addiction and Learning

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence

The ability to maintain focus and process task-relevant information continues developing during adolescence, but the specific physical environmental factors that influence this development remain poorly characterized. One candidate factor is air pollution. Evidence suggests that small particulate matter and NO2 concentrations in the air may negatively impact cognitive development in childhood. We assessed the relationship between neighborhood air pollution and the changes in performance on the n-back task, a test of attention and working memory, in the Adolescent Brain Cognitive Development (ABCD) Study's baseline (ages 9-10) and two-year-follow-up releases (Y2, ages 11-12; n = 5,256). In the behavioral domain, multiple linear regression showed that developmental change in n-back task performance was negatively associated with neighborhood air pollution (β = -.044, t = -3.11, p = .002), adjusted for covariates capturing baseline cognitive performance of the child, their parental income and education, family conflicts, and their neighborhood's population density, crime rate, perceived safety, and Area Deprivation Index (ADI). The strength of the adjusted association for air pollution was similar to parental income, family conflict, and neighborhood ADI. In the neuroimaging domain, we evaluated a previously published youth cognitive composite Connectome-based Predictive Model (ccCPM), and again found that decreased developmental change in the strength of the ccCPM from pre- to early adolescence was associated with neighborhood air pollution (β = -.110, t = -2.69, p = .007), adjusted for the covariates mentioned above and head motion. Finally, we found that the developmental change in ccCPM strength was predictive of the developmental change in n-back performance (r = .157, p < .001), and there was an indirect-only mediation where the effect of air pollution on change in n-back performance was mediated by the change in the ccCPM strength (βindirect effect = -.013, p = .029). In conclusion, neighborhood air pollution is associated with lags in the maturation of youth cognitive performance and decreased strengthening of the brain networks supporting cognitive abilities over time.

The role of pharmacogenomics in precision psychiatry

The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.

Suicide prevention and ketamine: insights from computational modeling

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.