Abstract
Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity. Reward contingencies are defined in real-time by fMRI multivoxel patterns; optimal action policies thereby depend on multidimensional brain activity taking place below the threshold of consciousness, by design. We find that subjects can solve the task within two hundred trials and errors, as their reinforcement learning processes interact with metacognitive functions (quantified as the meaningfulness of their decision confidence). Computational modelling and multivariate analyses identify a frontostriatal neural mechanism by which the brain may untangle the ‘curse of dimensionality’: synchronization of confidence representations in prefrontal cortex with reward prediction errors in basal ganglia support exploration of latent task representations. These results may provide an alternative starting point for future investigations into unconscious learning and functions of metacognition.
Highlights
Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity
Empirical optimal action-selection rates were compared to a chance level of 0.5, the rate attained if actions were randomly selected at every trial
Two main questions were addressed in this study: Can human subjects learn to make use of latent, high-dimensional brain activity? What is the putative vehicle and neural substrate of this ability? The closed-loop design adopted here granted a unique opportunity to investigate the ability of the human brain to learn to use unconscious, high-dimensional internal representations
Summary
Can humans be trained to make strategic use of latent representations in their own brains? We investigate how human subjects can derive reward-maximizing choices from intrinsic high-dimensional information represented stochastically in neural activity. Computational modelling and multivariate analyses identify a frontostriatal neural mechanism by which the brain may untangle the ‘curse of dimensionality’: synchronization of confidence representations in prefrontal cortex with reward prediction errors in basal ganglia support exploration of latent task representations These results may provide an alternative starting point for future investigations into unconscious learning and functions of metacognition. Because the time of decoding is pre-stimulus and the ensuing stimulus itself carries no direction information, the decoder alone defines the latent state from stochastic brain activity, along a predetermined classification boundary Such multidimensional patterns are known to represent information that is generally below consciousness[1,16,17,18,19]
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