Abstract:
This paper introduces the Predictive Adaptation through Occupational History (PATH) theory, a novel framework designed to bridge the conceptual divide between occupational science and computational neuroscience. PATH theory reconceptualizes occupational engagement as a form of active inference, where the brain minimizes prediction errors between a generative self-model, constructed from autobiographical memory, and the sensory realities of the environment. We posit that enduring individual differences in occupational performance arise from distinct, history-dependent strategies for managing these predictions. To formalize this, we introduce a taxonomy of six fundamental “Occupational Processing Archetypes,” each defined by a unique configuration of predictive modeling, temporal focus, and neurobiological underpinnings. The PATH framework provides a mechanistic, first-principles explanation for core concepts within established models like the Model of Human Occupation (MOHO), the Person-Environment-Occupation (PEO) model, and the Canadian Model of Occupational Performance and Engagement (CMOP-E), offering a new, quantifiable, and neurobiologically plausible foundation for assessment and intervention in occupational therapy.
Yıldırım, E. (2025). Predictive Adaptation through Occupational History (PATH): A Neuro-Computational Framework for Occupational Science. Zenodo. https://doi.org/10.5281/zenodo.17057596
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