Recent years have seen unprecedented increases in compulsive behaviors, even in previously undiagnosed populations. Yet, despite their rising prevalence and the fact that compulsions—notably associated with obsessive-compulsive disorder (OCD), but also a key feature of other disorders such a hoarding or body dysmorphic disorder—significantly hamper daily function, the specific mechanisms of their formation remain unknown. Previous research holds mixed findings on compulsivity-linked cognitive deficits, and, while many accounts assume that compulsions aim to reduce anxiety, most individuals with anxiety do not develop compulsions—suggesting that the anxiety-reduction account is incomplete. We propose here a broader, uncertainty-resolving account of compulsivity, that bypasses the commonly-used, single-goal structure of many cognitive tasks to leverage the real-world hierarchical structure of goals—with “local”, short-term subgoals (e.g., “how do I clean my hands?”) in the service of long-term, “global” goals (“how do I stay healthy?”). In that framework, we suggest that compulsivity may reflect a deficit in integrating uncertainty from local to global goals, which still spares the basic circuitry of reward and learning. We tested 20 OCD patients and 20 healthy volunteers in a predictive inference task with a hierarchical structure of local uncertainty reduction in the service of global uncertainty reduction. Both groups learned the reward structure of the local environment based on observed data; however, only the healthy volunteers showed evidence of integrating learned knowledge at the local level to reduce uncertainty about the higher level of the task hierarchy. This indicates a potential mechanism for compulsions that relies on the inability to adaptively integrate, prioritize, and “toggle” among different local goals in the service of global goals.
The exploration-exploitation trade-off is ubiquitous in our everyday lives, and individuals display considerable variability in their preferred decision-making strategies. Most previous work pertaining to neural signatures of exploration is restricted to functional pathways. However, the specific contributions of cortical microarchitectures to high-level cognitive processes such as decision-making are as yet unknown. Here, we investigated the neuroanatomical foundations of inter-individual variability in decision-making strategies. To this end, 122 healthy participants completed a gamified multi-armed bandit paradigm aimed at teasing apart distinct exploration-exploitation decision strategies. We also collected whole-brain quantitative MRI maps indexing microstructural features of cortical myelination and iron content. Through computational modelling, we disentangled individual-specific exploration strategies, including value-free random exploration. Whole-brain regression analyses identified significant associations between value-free exploration and increased cortical myelination in right frontal brain areas with reported links to impulsivity. By elucidating the brain microstructural correlates of distinct exploration-exploitation strategies, we aimed to further our understanding of why individuals differ in their decision-making capabilities, and how decision-making may become aberrant in mental health conditions.
Reward valuation and reward and punishment learning are key constructs relevant for understanding and intervening on mental illness. These constructs are frequently measured with behavioral tasks such as intertemporal choice tasks, measuring delay discounting, and the Iowa Gambling Task (IGT), measuring reward and punishment learning. Delay discounting refers to how people value delayed outcomes, with steep discounting (i.e., delayed outcomes holding little value) considered a form of impulsive decision-making. Reward and punishment learning refers to how people adjust their decision behavior in response to rewards and punishments, with deficits indicating atypical reward and punishment processing related to clinical outcomes such as substance abuse. In this study, we tested the association between delay discounting and reward and punishment learning among adults (N = 299) from the United States that completed an intertemporal choice task and the play-or-pass IGT. We fit a joint model to the task data to simultaneously estimate parameters from the hyperbolic discounting model (for the intertemporal choice task) with parameters from a reinforcement learning model (for the play-or-pass IGT). The joint model also included parameters capturing cross-task associations between model parameters. Our results indicate that steep delay discounting is negatively associated with punishment learning. That is, people who do not value delayed outcomes also show less learning from punishment. Implications of these findings are discussed in the context of improving our assessment of reward valuation and reward and punishment learning as constructs relevant to mental illness.
Dynamic social interactions and feedback are crucial for understanding others’ emotions, particularly when confronted with contradictory emotional cues. Alexithymia, a condition that co-occurs with many psychiatric disorders, is characterized by impairment in emotional processing. However, computational mechanisms by which it alters social inferences based on feedback cues remain unexplored. To examine this, 60 participants with low and high levels of alexithymia completed an emotional learning task involving contradictory social (verbal and visual) cues to infer targets’ emotions. Computational analyses, including bin-based, reinforcement learning, and drift-diffusion modeling, revealed how alexithymia alters latent parameters that govern value updating and choice. Individuals with high alexithymia demonstrated lower accuracy in learning from social feedback. Drift diffusion analysis revealed a perceptual bias toward the visual cue, higher drift rates in the visual-correct condition, and greater evidence accumulation to infer others' emotions. These findings suggest that individuals with high alexithymia exhibit impaired social learning and difficulty with decision-making in situations with conflicting social information, with computational modeling quantifying the latent processes involved and advancing mechanistic targets for computational psychiatry.
Processing uncertainty may be pathognomonic (characteristic of a disease) for some psychiatric conditions. Some people expect the world to change, even when it doesn’t. This tendency is central to paranoia, where individuals often anticipate threat or change without clear evidence. But what determines whether these beliefs translate into behavior? One possibility is that metacognitive structure – the coherence and depth with which one articulates their own thinking – acts as a buffer. An agent may endorse a belief but have sufficient accessory hypotheses to insulate it from action. To test this, we used metacognitive prompting in GPT-4 to score individual reflections on open-ended questions (e.g., did you use any particular strategy?) after completing a probabilistic reversal learning task. Individuals with higher paranoia demonstrate lower metacognitive structure (t = 5.98, p < 0.001), with metacognition attenuating the relationship between volatility belief and switching behavior (Δ = –15 pp, p < 0.001) even after controlling for reflection verbosity and general cognitive ability. These findings suggest that metacognition protects against uncertainty-driven instability, pointing to a key mechanism by which reflection protects against cognition under change. This work provides a novel framework to measure metacognition from behavioral task debrief questions.
