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Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we: (1) provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; (2) discuss how various modeling principles—including those from hierarchical and structural equation modeling frameworks—can help to improve reliability; and then (3) demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.

When making decisions based on probabilistic outcomes, people guide their behavior using knowledge gathered through both indirect descriptions and direct experience. Paradoxically, how people obtain information significantly impacts apparent preferences. A ubiquitous example is the description-experience gap: individuals seemingly overweight low probability events when probabilities are described yet underweight them when probabilities must be experienced firsthand. A leading explanation for this fundamental gap in decision-making is that probabilities are weighted differently when learned through description relative to experience, yet a formal theoretical account of the mechanism responsible for such weighting differences remains elusive. Here, we demonstrate how various learning and memory retention models incorporating neuroscientifically-motivated learning mechanisms can explain why probability weighting and valuation parameters are often found to vary across description and experience. In a simulation study, we show how learning through experience can lead to systematically biased estimates of probability weighting when using a traditional cumulative prospect theory model. We then use hierarchical Bayesian modeling and Bayesian model comparison to show how various learning and memory retention models capture participants’ behavior over and above changes in outcome valuation and probability weighting, accounting for description and experience-based decisions in a within-subject experiment. We conclude with a discussion of how substantive models of psychological processes can lead to insights that heuristic, statistical models fail to capture.

Trait impulsivity—defined by strong preference for immediate over delayed rewards and difficulties inhibiting prepotent behaviors—is observed in all externalizing disorders, including substance-use disorders. Many laboratory tasks have been developed to identify decision-making mechanisms and correlates of impulsive behavior, but convergence between task measures and self-reports of impulsivity are consistently low. Long-standing theories of personality and decision-making predict that neurally mediated individual differences in sensitivity to (a) reward cues and (b) punishment cues (frustrative nonreward) interact to affect behavior. Such interactions obscure one-to-one correspondences between single personality traits and task performance. We used hierarchical Bayesian analysis in three samples with differing levels of substance use ( N = 967) to identify interactive dependencies between trait impulsivity and state anxiety on impulsive decision-making. Our findings reveal how anxiety modulates impulsive decision-making and demonstrate benefits of hierarchical Bayesian analysis over traditional approaches for testing theories of psychopathology spanning levels of analysis.

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 minutes of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.

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.

The Iowa Gambling Task (IGT) is widely used to study decision making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short- and long-term prediction accuracy and parameter recovery. Here, we propose the Outcome-Representation Learning (ORL) model, a novel model that provides the best compromise between competing models. We test the performance of the ORL model on 393 subjects’ data collected across multiple research sites, and we show that the ORL reveals distinct patterns of decision making in substance using populations. Our work highlights the importance of using multiple model comparison metrics to make valid inference with cognitive models and sheds light on learning mechanisms that play a role in underweighting of rare events.

Background Impulsivity is central to all forms of externalizing psychopathology, including problematic substance use. The Cambridge Gambling task (CGT) is a popular neurocognitive task used to assess impulsivity in both clinical and healthy populations. However, the traditional methods of analysis in the CGT do not fully capture the multiple cognitive mechanisms that give rise to impulsive behavior, which can lead to underpowered and difficult-to-interpret behavioral measures. Objectives The current study presents the cognitive modeling approach as an alternative to traditional methods and assesses predictive and convergent validity across and between approaches. Methods We used hierarchical Bayesian modeling to fit a series of cognitive models to data from healthy controls (N = 124) and individuals with histories of substance use disorders (Heroin: N = 79; Amphetamine: N = 76; Polysubstance: N = 103; final total across groups N = 382). Using Bayesian model comparison, we identified the best fitting model, which was then used to identify differences in cognitive model parameters between groups. Results The cognitive modeling approach revealed differences in quality of decision making and impulsivity between controls and individuals with substance use disorders that traditional methods alone did not detect. Crucially, convergent validity between traditional measures and cognitive model parameters was strong across all groups. Conclusion The cognitive modeling approach is a viable method of measuring the latent mechanisms that give rise to choice behavior in the CGT, which allows for stronger statistical inferences and a better understanding of impulsive and risk-seeking behavior.

Two theoretical perspectives—functionalism and constructionism—predominate modern research on emotion. This introductory chapter describes these perspectives and offers points of convergence and divergence. It pays special attention to common misconceptions about functionalism and to strengths and limitations of each perspective. Functionalism, which draws in part from phylogenetic accounts of emotion and motivation, is limited by difficulties drawing inferences about human emotion from animal research, even though animal research is conducted using very precise methods of high spatial and temporal resolution. In contrast, constructionism is limited by difficulties falsifying its core propositions given reliance on research using functional magnetic resonance imaging, which has poor temporal resolution. These limitations notwithstanding, both functionalism and constructionism have much to offer current interpretations of and future research on emotion dysregulation. Thus, pitting the perspectives against one other is counterproductive.

As early as infancy, caregivers’ facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal—core Research Domain Criteria constructs—as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother–daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.

Whether we are making life-or-death decisions or thinking about the best way to phrase an email, counterfactual emotions including regret and disappointment play an ever-present role in how we make decisions. Functional theories of counterfactual thinking suggest that the experience and future expectation of counterfactual emotions should promote goal-oriented behavioral change. Although many studies find empirical support for such functional theories, the generative cognitive mechanisms through which counterfactual thinking facilitates changes in behavior are underexplored. Here, we develop generative models of risky decision-making that extend regret and disappointment theory to experience-based tasks, which we use to examine how people incorporate counterfactual information into their decisions across time. Further, we use computer-vision to detect positive and negative affect (valence) intensity from participants’ faces in response to feedback, which we use to explore how experienced emotion may correspond to cognitive mechanisms of learning, outcome valuation, or exploration/exploitation—any of which could result in functional changes in behavior. Using hierarchical Bayesian modeling and Bayesian model comparison methods, we found that a model assuming: (1) people learn to explicitly represent and subjectively weight counterfactual outcomes with increasing experience, and (2) people update their counterfactual expectations more rapidly as they experience increasingly intense negative affect best characterized empirical data. Our findings support functional accounts of regret and disappointment and demonstrate the potential for generative modeling and model-based facial expression analysis to enhance our understanding of cognition-emotion interactions.

Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positive and negative affect intensity. This is likely, in part, because achieving inter-rater reliability for facial action and affect intensity ratings is painstaking and labor-intensive. We use computer-vision and machine learning (CVML) to identify patterns of facial actions in 4,648 video recordings of 125 human participants, which show strong correspondences to positive and negative affect intensity ratings obtained from highly trained coders. Our results show that CVML can both (1) determine the importance of different facial actions that human coders use to derive positive and negative affective ratings when combined with interpretable machine learning methods, and (2) efficiently automate positive and negative affect intensity coding on large facial expression databases. Further, we show that CVML can be applied to individual human judges to infer which facial actions they use to generate perceptual emotion ratings from facial expressions.

Sexual discounting, which describes delay discounting of later protected sex vs. immediate unprotected sex (e.g., sex now without a condom vs. waiting an hour to have sex with a condom), is consistently linked to sexual risk behavior. Estimates suggest that over two-thirds of HIV transmissions occur between individuals in committed relationships, but current sexual discounting tasks examine sexual discounting only with hypothetical strangers, leaving a gap in our understanding of sexual discounting with committed sexual partners. We used the Sexual Discounting Task (SDT) to compare discounting rates between men who have sex with men (MSM; n = 99) and heterosexual men (n = 144) and tested a new SDT condition evaluating sexual discounting with main partners. MSM in committed relationships discounted protected sex with their main partner at higher rates than heterosexual men, and discounting rates correlated with self-report measures of condom use, impulsivity/sensation seeking, and substance use. These findings suggest that sexual discounting is a critical factor potentially related to increased HIV transmission between MSM in committed relationships and may be an important target for intervention and prevention.

The easyml (easy machine learning) package lowers the barrier to entry to machine learning and is ideal for undergraduate/graduate students, and practitioners who want to quickly apply machine learning algorithms to their research without having to worry about the best practices of implementing each algorithm. The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework. This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to each one. Importantly, users can run and evaluate each machine learning algorithm with a single line of coding. Each recipe is robust, implements best practices specific to each algorithm, and generates a report with details about the model, its performance, as well as journal-quality visualizations. The package’s functional, modular, and extensible framework also allows researchers and more advanced users to easily implement new recipes for other algorithms.

In the original publication of the article, the corresponding author was processed incorrectly. The corresponding author for this article should be: Woo-Young Ahn.

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.