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This paper reframes neural decoding as a cognitive inference problem, moving beyond stimulus-response mappings to leverage brain-intrinsic priors for extracting stable cognitive states from variable neural activity. By applying a Bayesian brain theory-inspired approach across five neural recording modalities and three cognitive domains, the method reorganizes neural observation representations into meta-neural semantic representations. These representations exhibit consistent geometric relationships across tasks, enabling robust recovery of cognitive states and offering an explanation for how the brain maintains cognitive stability amidst fluctuating neural signals.
Stable cognitive states can be recovered from variable neural activity by treating decoding as Bayesian inference, revealing meta-neural semantic representations with consistent geometric structure across diverse tasks and modalities.
The brain maintains stable cognition despite continuously changing neural activity. How to extract stable cognitive states from variable neural observations remains a central problem in neural decoding. Existing neural decoding methods map neural observations to predefined external labels based on the stimulus-response principle, often capturing recording-specific spurious correlations. Inspired by how the brain infers the world, and specifically by Bayesian brain theory, we recast neural decoding as cognitive inference constrained by brain-intrinsic priors, yielding high-level meta-neural semantic representations. In decoding experiments spanning five neural recording modalities and three cognitive domains (motor, perception and internal mentation), our cognitive inference method reorganized the geometry of neural observation representations, yielding meta-neural semantic representations that exhibited consistent geometric relationships across cognitive tasks and enabled the recovery of stable cognitive states from variable neural observations. Our work provides an account of how the brain maintains relatively stable cognition despite continual changes in the external environment. Cognitive stability is sustained through cognitive inference from changing neural activity, without requiring fixed neural activity patterns.