4.8 Article

Hippocampal representations switch from errors to predictions during acquisition of predictive associations

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NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-31040-w

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资金

  1. Wellcome/Royal Society Sir Henry Dale Fellowship [218535/Z/19/Z]
  2. European Research Council (ERC) Starting Grant [948548]
  3. Wellcome Trust [203147/Z/16/Z]
  4. European Research Council (ERC) [948548] Funding Source: European Research Council (ERC)
  5. Wellcome Trust [218535/Z/19/Z] Funding Source: Wellcome Trust

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The hippocampus is involved in both acquiring and exploiting predictive associations, and switches from representing errors to representing predictions as learning proceeds.
We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete. Successfully exploiting the regularities in our environment requires balancing the encoding of new information with the retrieval of stored associations. Here, the authors show that the hippocampus switches from representing novel information (errors) to representing predictions as learning proceeds.

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