Annual Research Review: Developmental computational psychiatry

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.

Neural differentiation of emotional faces as a function of interpersonal violence among adolescent girls

Interpersonal violence (IV) is associated with altered neural threat processing and risk for psychiatric disorder. Representational similarity analysis (RSA) is a multivariate approach examining the extent to which differences between stimuli correspond to differences in multivoxel activation patterns to these stimuli within each ROI. Using RSA, we examine overlap in neural patterns between threat and neutral faces in youth with IV. Participants were female adolescents aged 11-17 who had a history of IV exposure (n = 77) or no history of IV, psychiatric diagnoses, nor psychiatric medications (n = 37). Participants completed a facial emotion processing task during fMRI. Linear mixed models indicated that increasing hippocampal differentiation of fear and neutral faces was associated with increasing IV severity. Increased neural differentiation of these facial stimuli in the left and right hippocampus was associated with increasing physical abuse severity. Increased differentiation by the dACC correlated with increasing physical assault severity. RSA for most ROIs were not significantly associated with univariate activity, except for a positive association between amygdala RSA and activity to fear faces. Differences in statistically significant ROIs for physical assault and physical abuse may highlight distinct effects of trauma type on encoding of threat vs. neutral faces. Null associations between RSA and univariate activation in most ROIs suggest unique contributions of RSA for understanding IV compared to traditional activation. Implications include understanding mechanisms of risk in IV and trauma-specific treatment selection. Future work should replicate these findings in longitudinal studies and identify sensitive periods for neural alterations in RSA.

Probing apathy in children and adolescents with the Apathy Motivation Index-Child version

Apathy is linked to mental health and altered neurocognitive functions such as learning and decision-making in healthy adults. Mental health problems typically begin to emerge during adolescence, yet little is known about how apathy develops due to an absence of quantitative measurements specific to young people. Here, we present and evaluate the Apathy Motivation Index-Child Version (AMI-CV) for children and adolescents. We show across two samples of young people (aged 8 to 17 years, total N = 191) tested in schools in the UK and on a smartphone app, that the AMI-CV is a short, psychometrically sound measure to assess levels of apathy and motivation in young people. Similar to adult versions, the AMI-CV captures three distinct apathy domains: Behavioural Activation, Social Motivation and Emotional Sensitivity. The AMI-CV showed excellent construct validity with an alternative measure of apathy and external validity replicating specific links with related mental health traits shown in adults. Our results provide a short measure of self-reported apathy in young people that enables research into apathy development. The AMI-CV can be used in conjunction with the adult version to investigate the impact of levels of apathy across the lifespan.

Goal-directed learning in adolescence: neurocognitive development and contextual influences

Adolescence is a time during which we transition to independence, explore new activities and begin pursuit of major life goals. Goal-directed learning, in which we learn to perform actions that enable us to obtain desired outcomes, is central to many of these processes. Currently, our understanding of goal-directed learning in adolescence is itself in a state of transition, with the scientific community grappling with inconsistent results. When we examine metrics of goal-directed learning through the second decade of life, we find that many studies agree there are steady gains in performance in the teenage years, but others report that adolescent goal-directed learning is already adult-like, and some find adolescents can outperform adults. To explain the current variability in results, sophisticated experimental designs are being applied to test learning in different contexts. There is also increasing recognition that individuals of different ages and in different states will draw on different neurocognitive systems to support goal-directed learning. Through adoption of more nuanced approaches, we can be better prepared to recognize and harness adolescent strengths and to decipher the purpose (or goals) of adolescence itself.

The amplitude of low-frequency fluctuation characteristics in depressed adolescents with suicide attempts: a resting-state fMRI study

Background

The amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous brain activity derived from resting-state functional magnetic resonance imaging (rs-fMRI). Previous research has suggested that abnormal ALFF values may be associated with major depressive disorder (MDD) and suicide attempts in adolescents. In this study, our aim was to investigate the differences in ALFF values between adolescent MDD patients with and without a history of suicide attempts, and to explore the potential utility of ALFF as a neuroimaging biomarker for aiding in the diagnosis and prediction of suicide attempts in this population.

Methods

The study included 34 adolescent depression patients with suicide attempts (SU group), 43 depression patients without suicide attempts (NSU group), and 36 healthy controls (HC group). Depression was diagnosed using a threshold score greater than 17 on the Hamilton Depression Rating Scale (HDRS). The rs-fMRI was employed to calculate zALFF values and compare differences among the groups. Associations between zALFF values in specific brain regions and clinical variables such as emotion regulation difficulties were explored using Pearson partial correlation analysis. Receiver-Operating Characteristics (ROC) analysis assessed the ability of mean zALFF values to differentiate between SU and NSU groups.

Results

Significant differences in zALFF values were observed in the left and right inferior temporal gyrus (l-ITG, r-ITG) and right fusiform gyrus (r-FG) among the three groups (GRF corrected). Both SU and NSU groups exhibited increased zALFF values in the inferior temporal gyrus compared to the HC group. Furthermore, the SU group showed significantly higher zALFF values in the l-ITG and r-FG compared to both the NSU group and the HC group. Partial correlation analysis revealed a negative correlation between zALFF values in the left superior and middle frontal gyrus (l-SFG, l-MFG) and the degree of emotional dysregulation in the SU group (R = -0.496, p = 0.003; R = -0.484, p = 0.005). Combining zALFF values from the l-ITG and r-FG achieved successful discrimination between depressed adolescents with and without suicide attempts (AUC = 0.855) with high sensitivity (86%) and specificity (71%).

Conclusion

Depressed adolescents with suicidal behavior exhibit unique neural activity patterns in the inferior temporal gyrus and fusiform gyrus. These findings highlight the potential utility of these specific brain regions as biomarkers for identifying suicide risk in depressed adolescents. Furthermore, associations between emotion dysregulation and activity in their frontal gyrus regions were observed. These findings provide preliminary yet pertinent insights into the pathophysiology of suicide in depressed adolescents.

Sensitivity and specificity in affective and social learning in adolescence

Adolescence is a period of heightened affective and social sensitivity. In this review we address how this increased sensitivity influences associative learning. Based on recent evidence from human and rodent studies, as well as advances in computational biology, we suggest that, compared to other age groups, adolescents show features of heightened Pavlovian learning but tend to perform worse than adults at instrumental learning. Because Pavlovian learning does not involve decision-making, whereas instrumental learning does, we propose that these developmental differences might be due to heightened sensitivity to rewards and threats in adolescence, coupled with a lower specificity of responding. We discuss the implications of these findings for adolescent mental health and education.

Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

The computational psychiatry of antisocial behaviour and psychopathy

Antisocial behaviours such as disobedience, lying, stealing, destruction of property, and aggression towards others are common to multiple disorders of childhood and adulthood, including conduct disorder, oppositional defiant disorder, psychopathy, and antisocial personality disorder. These disorders have a significant negative impact for individuals and for society, but whether they represent clinically different phenomena, or simply different approaches to diagnosing the same underlying psychopathology is highly debated. Computational psychiatry, with its dual focus on identifying different classes of disorder and health (data-driven) and latent cognitive and neurobiological mechanisms (theory-driven), is well placed to address these questions. The elucidation of mechanisms that might characterise latent processes across different disorders of antisocial behaviour can also provide important advances. In this review, we critically discuss the contribution of computational research to our understanding of various antisocial behaviour disorders, and highlight suggestions for how computational psychiatry can address important clinical and scientific questions about these disorders in the future.

Persistence of Anxiety/Depression Symptoms in Early Adolescence: A Prospective Study of Daily Life Stress, Rumination, and Daytime Sleepiness in a Genetically Informative Cohort

In this prospective study of mental health, we examine the influence of three interrelated traits - perceived stress, rumination, and daytime sleepiness - and their association with symptoms of anxiety and depression in early adolescence. Given the known associations between these traits, an important objective is to determine the extent to which they may independently predict anxiety/depression symptoms. Twin pairs from the Queensland Twin Adolescent Brain (QTAB) project were assessed on two occasions (N = 211 pairs aged 9-14 years at baseline and 152 pairs aged 10-16 years at follow-up). Linear regression models and quantitative genetic modeling were used to analyze the data. Prospectively, perceived stress, rumination, and daytime sleepiness accounted for 8-11% of the variation in later anxiety/depression; familial influences contributed strongly to these associations. However, only perceived stress significantly predicted change in anxiety/depression, accounting for 3% of variance at follow-up after adjusting for anxiety/depression at baseline, although it did not do so independently of rumination and daytime sleepiness. Bidirectional effects were found between all traits over time. These findings suggest an underlying architecture that is shared, to some degree, by all traits, while the literature points to hypothalamic-pituitary-adrenal (HPA) axis and/or circadian systems as potential sources of overlapping influence and possible avenues for intervention.

Deficient prefrontal-amygdalar connectivity underlies inefficient face processing in adolescent major depressive disorder

Adolescence represents a critical developmental period where the prevalence of major depressive disorder (MDD) increases. Aberrant emotion processing is a core feature of adolescent MDD that has been associated with functional alterations within the prefrontal-amygdala circuitry. In this study, we tested cognitive and neural mechanisms of emotional face processing in adolescents with MDD utilizing a combination of computational modeling and neuroimaging. Thirty adolescents with MDD (age: M = 16.1 SD = 1.4, 20 females) and 33 healthy controls (age: M = 16.2 SD = 1.9, 20 females) performed a dynamic face- and shape-matching task. A linear ballistic accumulator model was fit to the behavioral data to study differences in evidence accumulation. We used dynamic causal modeling (DCM) to study effective connectivity in the prefrontal-amygdala network to reveal the neural underpinnings of cognitive impairments while performing the task. Face processing efficiency was reduced in the MDD group and most pronounced for ambiguous faces with neutral emotional expressions. Critically, this reduction was related to increased deactivation of the subgenual anterior cingulate (sgACC). Connectivity analysis showed that MDD exhibited altered functional coupling in a distributed network spanning the fusiform face area-lateral prefrontal cortex-sgACC and the sgACC-amygdala pathway. Our results suggest that MDD is related to impairments of processing nuanced facial expressions. Distributed dysfunctional coupling in the face processing network might result in inefficient evidence sampling and inappropriate emotional responses contributing to depressive symptomatology. Our study provides novel insights in the characterization of brain function in adolescents with MDD that strongly emphasize the critical role of aberrant prefrontal-amygdala interactions during emotional face processing.

Alternative female and male developmental trajectories in the dynamic balance of human visual perception

The numerous multistable phenomena in vision, hearing and touch attest that the inner workings of perception are prone to instability. We investigated a visual example-binocular rivalry-with an accurate no-report paradigm, and uncovered developmental and maturational lifespan trajectories that were specific for age and sex. To interpret these trajectories, we hypothesized that conflicting objectives of visual perception-such as stability of appearance, sensitivity to visual detail, and exploration of fundamental alternatives-change in relative importance over the lifespan. Computational modelling of our empirical results allowed us to estimate this putative development of stability, sensitivity, and exploration over the lifespan. Our results confirmed prior findings of developmental psychology and appear to quantify important aspects of neurocognitive phenotype. Additionally, we report atypical function of binocular rivalry in autism spectrum disorder and borderline personality disorder. Our computational approach offers new ways of quantifying neurocognitive phenotypes both in development and in dysfunction.

The interpretation of computational model parameters depends on the context

Reinforcement Learning (RL) models have revolutionized the cognitive and brain sciences, promising to explain behavior from simple conditioning to complex problem solving, to shed light on developmental and individual differences, and to anchor cognitive processes in specific brain mechanisms. However, the RL literature increasingly reveals contradictory results, which might cast doubt on these claims. We hypothesized that many contradictions arise from two commonly-held assumptions about computational model parameters that are actually often invalid: That parameters generalize between contexts (e.g. tasks, models) and that they capture interpretable (i.e. unique, distinctive) neurocognitive processes. To test this, we asked 291 participants aged 8–30 years to complete three learning tasks in one experimental session, and fitted RL models to each. We found that some parameters (exploration / decision noise) showed significant generalization: they followed similar developmental trajectories, and were reciprocally predictive between tasks. Still, generalization was significantly below the methodological ceiling. Furthermore, other parameters (learning rates, forgetting) did not show evidence of generalization, and sometimes even opposite developmental trajectories. Interpretability was low for all parameters. We conclude that the systematic study of context factors (e.g. reward stochasticity; task volatility) will be necessary to enhance the generalizability and interpretability of computational cognitive models.

Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants-who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied-that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.

Adolescent Dopamine Neurons Represent Reward Differently during Action and State Guided Learning

Neuronal underpinning of learning cause-and-effect associations in the adolescent brain remains poorly understood. Two fundamental forms of associative learning are Pavlovian (classical) conditioning, where a stimulus is followed by an outcome, and operant (instrumental) conditioning, where outcome is contingent on action execution. Both forms of learning, when associated with a rewarding outcome, rely on midbrain dopamine neurons in the ventral tegmental area (VTA) and substantia nigra (SN). We find that, in adolescent male rats, reward-guided associative learning is encoded differently by midbrain dopamine neurons in each conditioning paradigm. Whereas simultaneously recorded VTA and SN adult neurons have a similar phasic response to reward delivery during both forms of conditioning, adolescent neurons display a muted reward response during operant but a profoundly larger reward response during Pavlovian conditioning. These results suggest that adolescent neurons assign a different value to reward when it is not gated by action. The learning rate of adolescents and adults during both forms of conditioning was similar, supporting the notion that differences in reward response in each paradigm may be because of differences in motivation and independent of state versus action value learning. Static characteristics of dopamine neurons, such as dopamine cell number and size, were similar in the VTA and SN of both ages, but there were age-related differences in stimulated dopamine release and correlated spike activity, suggesting that differences in reward responsiveness by adolescent dopamine neurons are not because of differences in intrinsic properties of these neurons but engagement of different dopaminergic networks.SIGNIFICANCE STATEMENT Reckless behavior and impulsive decision-making by adolescents suggest that motivated behavioral states are encoded differently by the adolescent brain. Motivated behavior, which is dependent on the function of the dopamine system, follows learning of cause-and-effect associations in the environment. We find that dopamine neurons in adolescents encode reward differently depending on the cause-and-effect relationship of the means to receive that reward. Compared with adults, reward contingent on action led to a muted response, whereas reward that followed a cue but was not gated by action produced an augmented phasic response. These data demonstrate an age-related difference in dopamine neuron response to reward that is not uniform and is guided by processes that differentiate between state and action values.

What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience

Reinforcement learning (RL) is a concept that has been invaluable to fields including machine learning, neuroscience, and cognitive science. However, what RL entails differs between fields, leading to difficulties when interpreting and translating findings. After laying out these differences, this paper focuses on cognitive (neuro)science to discuss how we as a field might over-interpret RL modeling results. We too often assume-implicitly-that modeling results generalize between tasks, models, and participant populations, despite negative empirical evidence for this assumption. We also often assume that parameters measure specific, unique (neuro)cognitive processes, a concept we call interpretability, when evidence suggests that they capture different functions across studies and tasks. We conclude that future computational research needs to pay increased attention to implicit assumptions when using RL models, and suggest that a more systematic understanding of contextual factors will help address issues and improve the ability of RL to explain brain and behavior.

Computational modelling of attentional bias towards threat in paediatric anxiety

Computational modelling can be used to precisely characterize the cognitive processes involved in attentional biases towards threat, yet so far has only been applied in the context of adult anxiety. Furthermore, studies investigating attentional biases in childhood anxiety have largely used tasks that conflate automatic and controlled attentional processes. By using a perceptual load paradigm, we separately investigate contributions from automatic and controlled processes to attentional biases towards negative stimuli and their association with paediatric anxiety. We also use computational modelling to investigate these mechanisms in children for the first time. In a sample of 60 children (aged 5‐11 years) we used a perceptual load task specifically adapted for children, in order to investigate attentional biases towards fearful (compared with happy and neutral) faces. Outcome measures were reaction time and percentage accuracy. We applied a drift diffusion model to investigate the precise cognitive mechanisms involved. The load effect was associated with significant differences in response time, accuracy and the diffusion modelling parameters drift rate and extra‐decisional time. Greater anxiety was associated with greater accuracy and the diffusion modelling parameter ‘drift rate’ on the fearful face trials. This was specific to the high load condition. These findings suggest that attentional biases towards fearful faces in childhood anxiety are driven by increased perceptual sensitivity towards fear in automatic attentional systems. Our findings from computational modelling suggest that current attention bias modification treatments should target perceptual encoding directly rather than processes occurring afterwards.

Towards a computational psychiatry of juvenile obsessive-compulsive disorder

Obsessive-Compulsive Disorder (OCD) most often emerges during adolescence, but we know little about the aberrant neural and cognitive developmental mechanisms that underlie its emergence during this critical developmental period. To move towards a computational psychiatry of juvenile OCD, we review studies on the computational, neuropsychological and neural alterations in juvenile OCD and link these findings to the adult OCD and cognitive neuroscience literature. We find consistent difficulties in tasks entailing complex decision making and set shifting, but limited evidence in other areas that are altered in adult OCD, such as habit and confidence formation. Based on these findings, we establish a neurocomputational framework that illustrates how cognition can go awry and lead to symptoms of juvenile OCD. We link these possible aberrant neural processes to neuroimaging findings in juvenile OCD and show that juvenile OCD is mainly characterised by disruptions of complex reasoning systems.

The social cost of gathering information for trust decisions

Trust decisions are inherently uncertain, as people usually have incomplete information about the trustworthiness of the other person prior to their decision to trust or not trust. Therefore, it is typically beneficial to gather information about a trustee's past behaviour before deciding whether or not to trust them. However, elaborate inquiries about a trustee's behaviour may change the trustee's willingness to reciprocate, causing either a decrease due to the trustee's negative impressions of the investor or an increase because the investor appears to be highly betrayal-averse to the trustee. In turn, such a change could cause the investor to gather less or more information, respectively. Here, we examine how information acquisition is modulated by social context, monetary cost, and the trustee's trustworthiness. We gave participants the opportunity to sequentially sample information about a trustee's reciprocation history before they decided whether or not to invest. Participants sampled less when there was a monetary cost and when the gathered information was more conclusive. On some trials, we induced a social context by telling the participant that the trustee would learn how much the participant sampled ("overt sampling"). Crucially, when sampling was free, participants sampled less when sampling was overt than when it was covert, suggesting that they avoided leaving negative impressions. We find that the data were well accounted for by a Bayesian heuristic model, in which the agent continues sampling until uncertainty about trustworthiness-as measured by the width of the posterior belief-drops below a level that they find tolerable. This study opens the door to broader applications of the tools and models of information sampling to social decision-making.

Moving beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective

Almost all forms of psychopathology, including personality disorders, are arrived at through complex interactions among neurobiological vulnerabilities and environmental risk factors across development. Yet despite increasing recognition of etiological complexity, psychopathology research is still dominated by searches for large main effects causes. This derives in part from reliance on traditional inferential methods, including ordinary factor analysis, regression, ANCOVA, and other techniques that use statistical partialing to isolate unique effects. In principle, some of these methods can accommodate etiological complexity, yet as typically applied they are insensitive to interactive functional dependencies (modulating effects) among etiological influences. Here, we use our developmental model of antisocial and borderline traits to illustrate challenges faced when modeling complex etiological mechanisms of psychopathology. We then consider how computational models, which are rarely used in the personality disorders literature, remedy some of these challenges when combined with hierarchical Bayesian analysis.

Flux of life

Developmental cognitive neuroscience is flourishing but there are new challenges and new questions to be asked. I argue that we need a bigger picture and an evolutionary framework. This brings some challenges, such as the need to rewrite the old story of nature and nurture, and the need to systematically investigate innate predispositions. While brain imaging has provided some splendid insights and new puzzles to solve, its limitations must not be ignored. Can they help us to find out more about the extent to which the infant brain already configures the adult brain? Can we find out why neurodevelopmental disorders often have severe consequences on cognition and behaviour, despite the mitigating force of brain plasticity? I wish to encourage researchers of the future to take risks by letting their imagination inspire theories to pursue hard questions. I end with a wish list of topics, from start-up kits to abstract reasoning, that I hope can be tackled afresh. However, collecting physiological and behavioural data is not enough. We need a deeper understanding of the mechanisms of cognitive development.

Adolescent indirect reciprocity: Evidence from incentivized economic paradigms

INTRODUCTION

Indirect reciprocity serves as a crucial component of how we interact with strangers. Two types of indirect reciprocity can be distinguished: pay-it-forward reciprocity and third party reciprocity. Pay-it-forward reciprocity refers to behaviors where people who have been treated well by others (either fairly or generously), extend that fairness or generosity to a stranger. Third-party reciprocity refers to behaviors where third-party bystanders altruistically punish those who transgress against others or kindly help the victims. The expansion of adolescents' social world increases opportunities to exercise indirect reciprocity yet very little research has focused on this topic in this age group. The current research addresses this lacuna and investigates how younger adolescents differ from older adolescents in pay-it-forward and third party reciprocity.

METHODS

With incentivized economic paradigms, we investigated both types of indirect reciprocity in younger (n = 50) and older adolescents (n = 46).

RESULTS

The pay-it-forward task revealed that receiving an equal (vs. unequal) distribution led both younger and older adolescents to become fairer to a third person. In the third-party task, older adolescents were more likely to devote their own resources to enforce fairness norms than younger adolescents.

CONCLUSION

Our results shed light on how adolescents perceive and act in complex social settings where direct reciprocity is unrealistic. Both younger and older adolescents are capable of engaging in both forms of indirect reciprocity with older adolescents being more discriminative in their norm-enforcing behaviors.

Commentary: Building the developmental foundations of developmental computational psychiatry: reflections on Hauser et al. (2019)

There is a growing interest in applying the conceptual and analytical frameworks of computational psychiatry to developmental populations. This is motivated by appreciation that psychiatric illness needs to be understood from a neurodevelopmental perspective. The target article by Hauser and colleagues highlights progress in applying the computational psychiatry perspectives to identifying the developmental mechanisms of mental illness. We share the enthusiasm and optimism for this venture, while recognizing the substantial theoretical and pragmatic challenges associated with applying computational frameworks to developing populations. In this commentary, we highlight the ways that taking a developmental perspective in this arena stretches beyond merely identifying age differences in a computational parameter of interest. These include the need for experimental and computational frameworks to recognize that developmental changes can be quantitative or qualitative in nature, the need to consider developmental stage beyond age groupings or even numerical age, and the need for large quantities of data to model age-related changes in a reproducible manner. In doing so, we hope to stimulate progress in uncovering the mechanisms of psychiatric illness in a way that is developmentally informed.

Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.

Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood

Pavlovian influences are important in guiding decision-making across health and psychopathology. There is an increasing interest in using concise computational tasks to parametrise such influences in large populations, and especially to track their evolution during development and changes in mental health. However, the developmental course of Pavlovian influences is uncertain, a problem compounded by the unclear psychometric properties of the relevant measurements. We assessed Pavlovian influences in a longitudinal sample using a well characterised and widely used Go-NoGo task. We hypothesized that the strength of Pavlovian influences and other 'psychomarkers' guiding decision-making would behave like traits. As reliance on Pavlovian influence is not as profitable as precise instrumental decision-making in this Go-NoGo task, we expected this influence to decrease with higher IQ and age. Additionally, we hypothesized it would correlate with expressions of psychopathology. We found that Pavlovian effects had weak temporal stability, while model-fit was more stable. In terms of external validity, Pavlovian effects decreased with increasing IQ and experience within the task, in line with normative expectations. However, Pavlovian effects were poorly correlated with age or psychopathology. Thus, although this computational construct did correlate with important aspects of development, it does not meet conventional requirements for tracking individual development. We suggest measures that might improve psychometric properties of task-derived Pavlovian measures for future studies.