How do we learn when to persist, when to let go, and when to shift gears? Gearshift Fellowship (GF) is the prototype of a new Supertask paradigm designed to model how humans and artificial agents adapt to shifting environment demands. Grounded in cognitive neuroscience, computational psychiatry, economics, and artificial intelligence, Supertasks combine computational neurocognitive modeling with serious gaming. This creates a dynamic, multi-mission environment engineered to assess mechanisms of adaptive behavior across cognitive and social contexts. Computational parameters explain behavior and probe mechanisms by controlling the game environment. Unlike traditional tasks, GF enables neurocognitive modeling of individual differences across perceptual decisions, learning, and meta-cognitive levels. This positions GF as a flexible testbed for understanding how cognitive-affective control processes, learning styles, strategy use, and motivational shifts adapt across contexts and over time. It serves as an experimental platform for scientists, a phenotype-to-mechanism intervention for clinicians, and a training tool for players aiming to strengthen self-regulated learning, mood, and stress resilience. Online study (n = 60, ongoing) results show that GF recovers effects from traditional neuropsychological tasks (construct validity), uncovers novel patterns in how learning differs across contexts and how clinical features map onto distinct adaptations. These findings pave the way for developing in-game interventions that foster self-efficacy and agency to cope with real-world stress and uncertainty. GF builds a new adaptive ecosystem designed to accelerate science, transform clinical care, and foster individual growth. It offers a mirror and training ground where humans and machines co-develop together deeper flexibility and awareness.

Bayesian models of cognition have gained considerable traction in computational neuroscience and psychiatry. Their scopes are now expected to expand rapidly to artificial intelligence, providing general inference frameworks to support embodied, adaptable, and energy-efficient autonomous agents. A central theory in this domain is predictive coding, which posits that learning and behaviour are driven by hierarchical probabilistic inferences about the causes of sensory inputs. Biological realism constrains these networks to rely on simple local computations in the form of precision-weighted predictions and prediction errors. This can make this framework highly efficient, but its implementation comes with unique challenges on the software development side. Embedding such models in standard neural network libraries often becomes limiting, as these libraries' compilation and differentiation backends can force a conceptual separation between optimization algorithms and the systems being optimized. This critically departs from other biological principles such as self-monitoring, self-organisation, cellular growth and functional plasticity. In this paper, we introduce \texttt{pyhgf}: a Python package backed by JAX and Rust for creating, manipulating and sampling dynamic networks for predictive coding. We improve over other frameworks by enclosing the network components as transparent, modular and malleable variables in the message-passing steps. The resulting graphs can implement arbitrary computational complexities as beliefs propagation. But the transparency of core variables can also translate into inference processes that leverage self-organisation principles, and express structure learning, meta-learning or causal discovery as the consequence of network structural adaptation to surprising inputs. The code, tutorials and documentation are hosted at: https://github.com/ilabcode/pyhgf.

Large language models are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also why they fail and misbehave is of great societal relevance. We propose to turn the lens of computational psychiatry, a framework used to computationally describe and modify aberrant behavior, to the outputs produced by these models. We focus on the Generative Pre-Trained Transformer 3.5 and subject it to tasks commonly studied in psychiatry. Our results show that GPT-3.5 responds robustly to a common anxiety questionnaire, producing higher anxiety scores than human subjects. Moreover, GPT-3.5's responses can be predictably changed by using emotion-inducing prompts. Emotion-induction not only influences GPT-3.5's behavior in a cognitive task measuring exploratory decision-making but also influences its behavior in a previously-established task measuring biases such as racism and ableism. Crucially, GPT-3.5 shows a strong increase in biases when prompted with anxiety-inducing text. Thus, it is likely that how prompts are communicated to large language models has a strong influence on their behavior in applied settings. These results progress our understanding of prompt engineering and demonstrate the usefulness of methods taken from computational psychiatry for studying the capable algorithms to which we increasingly delegate authority and autonomy.

Rationale: The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the ability to generate better assessments, prognoses, diagnoses, or treatment of psychiatric conditions. Objectives: To integrate the emerging findings in machine learning and computational psychiatry to address the question: what measures that are not derived from the patient's self-assessment or the assessment by a trained professional can be used to make more precise predictions about the individual's current state, the individual's future disease trajectory, or the probability to respond to a particular intervention? Results: Currently, the ability to use individual differences to predict differential outcomes is very modest possibly related to the fact that the effect sizes of interventions are small. There is emerging evidence of genetic and neuroimaging-based heterogeneity of psychiatric disorders, which contributes to imprecise predictions.

Pearl Chiu has jet black hair and a bearing of quiet confidence. She pauses to think before she speaks, and radiates a teacher's joy in discussing her work. She's the only clinically trained psychologist in the lab who has direct experience with patients in a clinical setting, and she arrived at machine learning from a distinctly human place. "As I was seeing, working with, patients, I was just frustrated with how little we knew about what is happening," Chiu says. She believes bringing in machines to detect patterns may be a solution.

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior. That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.

Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively assessing human behavior and comparing it with a well-established norm. Just as tricky, there are few well-founded models of neural circuitry or brain biochemistry, and it is difficult to link this science with real-world behavior. That has begun to change thanks to the emerging discipline of computational psychiatry, which uses powerful data analysis, machine learning, and artificial intelligence to tease apart the underlying factors behind extreme and unusual behaviors.

Brain science draws legions of eager students to the field and countless millions in dollars, euros and renminbi to fund research. These endeavors, however, have not yielded major improvements in treating patients who suffer from psychiatric disorders for decades. The languid pace of translating research into therapies stems from the inherent difficulties in understanding mental illness. "Psychiatry deals with brains interacting with the world and with other brains, so we're not just considering a brain's function but its function in complex situations," says Quentin Huys of the Swiss Federal Institute of Technology (E.T.H. Zurich) and the University of Zurich, lead author of a review of the emerging field of computational psychiatry, published this month in Nature Neuroscience. Computational psychiatry sets forth the ambitious goal of using sophisticated numerical tools to understand and treat mental illness.