Comparative Computational Modeling of Approach-Avoidance Biases in Suicidal Populations via Hierarchical Bayesian Inference

Abstract

Pavlovian "approach or avoid" impulses are critical behavioral biases that, in excess, are linked to multiple psychiatric conditions. To investigate how such biases contribute to suicidal thoughts and behaviors, we analyzed data from two clinical populations completing an aversive Go/NoGo task. This task disentangles motor action (Go or NoGo) from outcome valence (escape from, or avoidance of, an aversive stimulus), enabling the isolation of Pavlovian biases from instrumental learning processes. We compared multiple computational models that had previously been proposed to explain Pavlovian tendencies, including reinforcement learning, active inference, and drift diffusion-based approaches. We employed a hierarchical Bayesian inference procedure that treats model identity as a random factor at the individual level, allowing an unbiased determination of which mechanisms most accurately captured participants behavior. Across both datasets, models featuring Pavlovian context biases plus a value-decay mechanism best accounted for performance. By contrast, policy-based Pavlovian models and more complex approaches, such as those integrating working memory or active inference, were supported by fewer study participants. These findings suggest that reflexive biases exert a persistent influence on decision-making, and that value decay plays a critical role in shaping behavior over time. Our results demonstrate the importance of systematically comparing and accounting for relevant cognitive processes to explain observed task behaviors. Understanding the factors contributing to task performance may help clarify how Pavlovian tendencies relate to psychopathology, including, in our case, elevated suicide risk. Finally, we illustrate how a complete hierarchical model selection framework can be applied to identify the most plausible mechanisms underlying Pavlovian biases, offering a robust approach for advancing our understanding of task behaviors and establishing clinical utility in future studies. Author summaryAutomatic "approach or avoid" reactions shape behavior, particularly in stressful or negative situations. In this study, we explored how these reflex-like tendencies might contribute to suicidal thoughts and behaviors. Two clinical groups completed a computerized task measuring responses to unpleasant sounds. Participants made either active responses (pressing a button to stop a sound) or passive responses (refraining from pressing to avoid starting a sound), allowing us to examine the interplay of automatic impulses and learning from past experiences. Our analysis showed that behavior was best explained by a model combining stable "approach or avoid" impulses with a forgetting process that reduced reliance on past experiences over time. More complex models involving strategies or memory-based control were less effective. These findings suggest that individuals with suicidal tendencies may rely on persistent reflex-like behaviors and over-index recent outcomes, compromising their ability to learn in uncertain environmental conditions. Understanding these cognitive processes provides insights into why some individuals feel trapped in harmful patterns of thought and behavior. Our work highlights how identifying shared traits in clinical populations using model-based methods can inform targeted mental health interventions and improve our understanding of cognitive functioning across disorders.