Philosopher of Cognitive Science. Working at the intersection of computational neuroscience (active inference), neurophenomenology and meditation research. Supervisors: Jakob Hohwy Address: Melbourne, Australia
Recent work has sought to explain how meditation functions by accounting for the practice within ... more Recent work has sought to explain how meditation functions by accounting for the practice within the predictive processing framework. However, current accounts are somewhat limited either in the breadth of processes covered and their relationship to each other, or in the mechanistic depth with which they are described. Here, I attempt to address such limitations. I provide a predictive processing account of how meditation can change one's capacity to learn. I do so by constructing an argument which draws upon research on neuroplasticity, meditation, perception and from the literature on predictive processing. In the paper I review the research on neuroplasticity and learning. Then I discuss the research on meditation and neuroplasticity. After doing so, I present predictive processing as a framework for understanding the brain and mind. I review the literature on accounts of meditation within the framework, and proceed to synthesize and extend their core elements in order to provide an account with increased mechanistic breadth and depth. This synthesis is then used as a lens through which I argue that the altered perception found in studies on long-term meditators can be accounted for. Such meditators benefit from increased attentional availability closer to the sensory processing stream. I further argue that this has consequences for neuroplasticity, and therefore learning, as it allows higher-level priors in the hierarchical generative model to update more easily due to increased new evidence accumulation made possible at the lower levels of the hierarchy. This essentially allows the mind to maintain a tighter fit with its environment as it is kept more up-to-date through an increased capacity to update resistant-to-change priors.
Recent work has sought to explain how meditation functions by accounting for the practice within ... more Recent work has sought to explain how meditation functions by accounting for the practice within the predictive processing framework. However, current accounts are somewhat limited either in the breadth of processes covered and their relationship to each other, or in the mechanistic depth with which they are described. Here, I attempt to address such limitations. I provide a predictive processing account of how meditation can change one's capacity to learn. I do so by constructing an argument which draws upon research on neuroplasticity, meditation, perception and from the literature on predictive processing. In the paper I review the research on neuroplasticity and learning. Then I discuss the research on meditation and neuroplasticity. After doing so, I present predictive processing as a framework for understanding the brain and mind. I review the literature on accounts of meditation within the framework, and proceed to synthesize and extend their core elements in order to provide an account with increased mechanistic breadth and depth. This synthesis is then used as a lens through which I argue that the altered perception found in studies on long-term meditators can be accounted for. Such meditators benefit from increased attentional availability closer to the sensory processing stream. I further argue that this has consequences for neuroplasticity, and therefore learning, as it allows higher-level priors in the hierarchical generative model to update more easily due to increased new evidence accumulation made possible at the lower levels of the hierarchy. This essentially allows the mind to maintain a tighter fit with its environment as it is kept more up-to-date through an increased capacity to update resistant-to-change priors.
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Papers by Shawn Prest