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Heightened risk taking in adolescence has long been attributed to valuation systems overwhelming the deployment of cognitive control. However, this explanation of why adolescents engage in risk taking is insufficient given increasing evidence that risk-taking behavior can be strategic and involve elevated cognitive control. We argue that applying the expected-value-of-control computational model to adolescent risk taking can clarify under what conditions control is elevated or diminished during risky decision-making. Through this lens, we review research examining when adolescent risk taking might be due to—rather than a failure of—effective cognitive control and suggest compelling ways to test such hypotheses. This effort can resolve when risk taking arises from an immaturity of the control system itself, as opposed to arising from differences in what adolescents value relative to adults. It can also identify promising avenues for channeling cognitive control toward adaptive outcomes in adolescence.

Although mindfulness meditation is known to provide a wealth of psychological benefits, the neural mechanisms involved in these effects remain to be well characterized. A central question is whether the observed benefits of mindfulness training derive from specific changes in the structural brain connectome that do not result from alternative forms of experimental intervention. Measures of whole-brain and node-level structural connectome changes induced by mindfulness training were compared with those induced by cognitive and physical fitness training within a large, multi-group intervention protocol (n = 86). Whole-brain analyses examined global graph-theoretical changes in structural network topology. A hypothesis-driven approach was taken to investigate connectivity changes within the insula, which was predicted here to mediate interoceptive awareness skills that have been shown to improve through mindfulness training. No global changes were observed in whole-brain network topology. However, node-level results confirmed a priori hypotheses, demonstrating significant increases in mean connection strength in right insula across all of its connections. Present findings suggest that mindfulness strengthens interoception, operationalized here as the mean insula connection strength within the overall connectome. This finding further elucidates the neural mechanisms of mindfulness meditation and motivates new perspectives about the unique benefits of mindfulness training compared to contemporary cognitive and physical fitness interventions.

Computational approaches to understanding the algorithms of the mind are just beginning to pervade the field of clinical psychology. In the present article, we seek to explain in simple terms why this approach is indispensable to pursuing explanations of psychological phenomena broadly, and we review nascent efforts to use this lens to understand anxiety. We conclude with future directions that will be required to advance algorithmic accounts of anxiety. Ultimately, the surplus explanatory value of computational models of anxiety, above and beyond existing neurobiological models of anxiety, impugns the naively reductionist claim that neurobiological models are sufficient to explain anxiety.

Background Disorders involving compulsivity, fear, and anxiety are linked to beliefs that the world is less predictable. We lack a mechanistic explanation for how such beliefs arise. Here, we test a hypothesis that in people with compulsivity, fear, and anxiety, learning a probabilistic mapping between actions and environmental states is compromised. Methods In Study 1 (n = 174), we designed a novel online task that isolated state transition learning from other facets of learning and planning. To determine whether this impairment is due to learning that is too fast or too slow, we estimated state transition learning rates by fitting computational models to two independent datasets, which tested learning in environments in which state transitions were either stable (Study 2: n = 1413) or changing (Study 3: n = 192). Results Study 1 established that individuals with higher levels of compulsivity are more likely to demonstrate an impairment in state transition learning. Preliminary evidence here linked this impairment to a common factor comprising compulsivity and fear. Studies 2 and 3 showed that compulsivity is associated with learning that is too fast when it should be slow (i.e. when state transition are stable) and too slow when it should be fast (i.e. when state transitions change). Conclusions Together, these findings indicate that compulsivity is associated with a dysregulation of state transition learning, wherein the rate of learning is not well adapted to the task environment. Thus, dysregulated state transition learning might provide a key target for therapeutic intervention in compulsivity.

Efforts to understand the causes of psychopathology have remained stifled in part because current practices do not clearly describe how psychological constructs differ from biological phenomena and how to integrate them in unified explanations. The present article extends recent work in philosophy of science by proposing a framework called mechanistic science as a promising way forward. This approach maintains that integrating psychological and biological phenomena involves demonstrating how psychological functions are implemented in biological structures. Successful early attempts to advance mechanistic explanations of psychological phenomena are reviewed, and lessons are derived to show how the framework can be applied to a range of clinical psychological phenomena including gene by environment findings, computational models of reward processing in schizophrenia, and self-related processes in personality pathology. Pursuing a mechanistic approach can ultimately facilitate more productive and successful collaborations across a range of disciplines.

Efforts to understand the causes of psychopathology have remained stifled in part because current practices do not clearly describe how psychological constructs differ from biological phenomena and how to integrate them in unified explanations. The present article extends recent work in philosophy of science by proposing a framework called mechanistic science as a promising way forward. This approach maintains that integrating psychological and biological phenomena involves demonstrating how psychological functions are implemented in biological structures. Successful early attempts to advance mechanistic explanations of psychological phenomena are reviewed, and lessons are derived to show how the framework can be applied to a range of clinical psychological phenomena, including gene by environment findings, computational models of reward processing in schizophrenia, and self-related processes in personality pathology. Pursuing a mechanistic approach can ultimately facilitate more productive and successful collaborations across a range of disciplines.

Background A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depressed mood). These dimensions are then mapped to biological measures and computational parameters. Researchers pursuing this approach tend to equate a symptom inventory score (plus noise) with some latent psychological trait. Main text We argue this approach implies weak, tacit, models of traits that provide fixed predictions of individual symptoms, and thus cannot account for symptom trajectories within individuals. This problem persists because (1) researchers are not familiarized with formal models that relate internal traits to within-subject symptom variation and (2) rely on an assumption that trait self-report inventories accurately indicate latent traits. To address these concerns, we offer a computational model of trait depression that demonstrates how parameters instantiating a given trait remain stable while manifest symptom expression varies predictably. We simulate patterns of mood variation from both the computational model and the standard self-report model and describe how to quantify the relative validity of each model using a Bayesian procedure. Conclusions Ultimately, we would urge a tempering of a reliance on self-report inventories and recommend a shift towards developing mechanistic trait models that can explain within-subject symptom dynamics.