Pruning of Memories by Context-Based Prediction Error. Kim, G., Lewis-Peacock, J. A., Norman, K. A., & Turk-Browne, N. B. 111(24):8997–9002.
Pruning of Memories by Context-Based Prediction Error [link]Paper  doi  abstract   bibtex   
[Significance] Forgetting is often considered to be bad, but selective forgetting of unreliable information can have the positive side effect of reducing mental clutter, thereby making it easier to access our most important memories. Prior studies of forgetting have focused on passive mechanisms (decay, interference) or on effortful inhibition by cognitive control. Here we report the discovery of an active mechanism for forgetting that weakens memories selectively and without burdening the conscious mind. Specifically, we show that the brain automatically generates predictions about which items should appear in familiar contexts; if these items fail to appear, their memories are weakened. This process is adaptive, because such memories may have been encoded incorrectly or may represent unstable aspects of the world. [Abstract] The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report the discovery of a context-based mechanism for determining which memories to prune. Specifically, when a previously experienced context is reencountered, the brain automatically generates predictions about which items should appear in that context. If an item fails to appear when strongly expected, its representation in memory is weakened, and it is more likely to be forgotten. We find robust support for this mechanism using multivariate pattern classification and pattern similarity analyses. The results are explained by a model in which context-based predictions activate item representations just enough for them to be weakened during a misprediction. These findings reveal an ongoing and adaptive process for pruning unreliable memories.
@article{kimPruningMemoriesContextbased2014,
  title = {Pruning of Memories by Context-Based Prediction Error},
  author = {Kim, Ghootae and Lewis-Peacock, Jarrod A. and Norman, Kenneth A. and Turk-Browne, Nicholas B.},
  date = {2014-06},
  journaltitle = {Proceedings of the National Academy of Sciences},
  volume = {111},
  pages = {8997--9002},
  issn = {1091-6490},
  doi = {10.1073/pnas.1319438111},
  url = {https://doi.org/10.1073/pnas.1319438111},
  abstract = {[Significance] 

Forgetting is often considered to be bad, but selective forgetting of unreliable information can have the positive side effect of reducing mental clutter, thereby making it easier to access our most important memories. Prior studies of forgetting have focused on passive mechanisms (decay, interference) or on effortful inhibition by cognitive control. Here we report the discovery of an active mechanism for forgetting that weakens memories selectively and without burdening the conscious mind. Specifically, we show that the brain automatically generates predictions about which items should appear in familiar contexts; if these items fail to appear, their memories are weakened. This process is adaptive, because such memories may have been encoded incorrectly or may represent unstable aspects of the world.

[Abstract] 

The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report the discovery of a context-based mechanism for determining which memories to prune. Specifically, when a previously experienced context is reencountered, the brain automatically generates predictions about which items should appear in that context. If an item fails to appear when strongly expected, its representation in memory is weakened, and it is more likely to be forgotten. We find robust support for this mechanism using multivariate pattern classification and pattern similarity analyses. The results are explained by a model in which context-based predictions activate item representations just enough for them to be weakened during a misprediction. These findings reveal an ongoing and adaptive process for pruning unreliable memories.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13231389,bias-correction,context-aware,memory,prediction-bias,reinforcement-learning,similarity,weighting},
  number = {24}
}
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