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A cognitive framework for becoming deliberately conscious

Sucharit Katyal

November 18, 2024 preprint DOI: 10.31234/osf.io/ufwvh (opens in new tab) via OpenAlex

Summary

AI-generated from the abstract

Meditation training improves mental well-being by engaging a metacognitive process. During meditation, a person tries to maintain a target content in consciousness, requiring monitoring and regulation of conscious contents. This involves a metacognitive template of the target; comparing this template to actual conscious content generates a "metacognitive prediction error" (MPE). MPE helps fine-tune the frequency of monitoring, unconscious processing, and the template itself, enabling efficient regulation. The framework explains existing neuroscientific findings on meditation and generates testable hypotheses.

Study at a glance

Characteristics Theoretical or philosophical paper
Intervention Meditation training
Keywords Cognition Cognitive science Computer science Cognitive psychology Neuroscience
Key finding Proposes that meditation involves a metacognitive template of a target content, and the comparison of this template with actual conscious content generates a "metacognitive prediction error" that enables efficient regulation of conscious content.

Abstract

Meditation training is widely used to enhance mental well-being. However, a mechanistic cognitive science framework for understanding how it transforms mental processing is still needed. The act of practicing meditation involves trying to be deliberately conscious of a target content. This requires monitoring the contents of one’s consciousness and regulating them in favor of the target. I propose, this involves a metacognitive template of the target being present in consciousness, which when compared to the actual conscious content generates a “metacognitive prediction error” (MPE). MPE leads to efficient regulation of conscious content by helping fine-tune the frequency of monitoring, downstream unconscious processing, and the metacognitive template itself. The proposed framework parsimoniously explains existing neuroscientific findings on meditation and generates testable hypotheses.

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