Neural Differentiation of Incorrectly Predicted Memories

When an item is predicted in a particular context but the prediction is violated, memory for that item is weakened (Kim et al., 2014). Here, we explore what happens when such previously mispredicted items are later reencountered. According to prior neural network simulations, this sequence of events—misprediction and subsequent restudy—should lead to differentiation of the item's neural representation from the previous context (on which the misprediction was based). Specifically, misprediction weakens connections in the representation to features shared with the previous context and restudy allows new features to be incorporated into the representation that are not shared with the previous context. This cycle of misprediction and restudy should have the net effect of moving the item's neural representation away from the neural representation of the previous context. We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity patterns in the hippocampus, a key structure for representing memories and generating predictions. In left CA2/3/DG, we found greater neural differentiation for items that were repeatedly mispredicted and restudied compared with items from a control condition that was identical except without misprediction. We also measured prediction strength in a trial-by-trial fashion and found that greater misprediction for an item led to more differentiation, further supporting our hypothesis. Therefore, the consequences of prediction error go beyond memory weakening. If the mispredicted item is restudied, the brain adaptively differentiates its memory representation to improve the accuracy of subsequent predictions and to shield it from further weakening. SIGNIFICANCE STATEMENT Competition between overlapping memories leads to weakening of nontarget memories over time, making it easier to access target memories. However, a nontarget memory in one context might become a target memory in another context. How do such memories get restrengthened without increasing competition again? Computational models suggest that the brain handles this by reducing neural connections to the previous context and adding connections to new features that were not part of the previous context. The result is neural differentiation away from the previous context. Here, we provide support for this theory, using fMRI to track neural representations of individual memories in the hippocampus and how they change based on learning.

[1]  Adler J. Perotte,et al.  Methods for reducing interference in the Complementary Learning Systems model: Oscillating inhibition and autonomous memory rehearsal , 2005, Neural Networks.

[2]  Ehren L. Newman,et al.  A neural network model of retrieval-induced forgetting. , 2007, Psychological review.

[3]  R. Poldrack,et al.  Measuring neural representations with fMRI: practices and pitfalls , 2013, Annals of the New York Academy of Sciences.

[4]  Jarrod A. Lewis-Peacock,et al.  Pruning of memories by context-based prediction error , 2014, Proceedings of the National Academy of Sciences.

[5]  Kenneth A. Norman,et al.  How Inhibitory Oscillations Can Train Neural Networks and Punish Competitors , 2006, Neural Computation.

[6]  Brice A. Kuhl,et al.  Experience-dependent hippocampal pattern differentiation prevents interference during subsequent learning , 2016, Nature Communications.

[7]  N. Turk-Browne,et al.  Attention promotes episodic encoding by stabilizing hippocampal representations , 2016, Proceedings of the National Academy of Sciences.

[8]  K. Norman.  How hippocampus and cortex contribute to recognition memory: Revisiting the complementary learning systems model , 2010, Hippocampus.

[9]  Russell A. Poldrack,et al.  What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis , 2014, NeuroImage.

[10]  M. Botvinick,et al.  Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning , 2016, bioRxiv.

[11]  Valerie A. Carr,et al.  Global Similarity and Pattern Separation in the Human Medial Temporal Lobe Predict Subsequent Memory , 2013, The Journal of Neuroscience.

[12]  N. Turk-Browne,et al.  Attention Stabilizes Representations in the Human Hippocampus. , 2015, Cerebral cortex.

[13]  Felicia Y. Ng,et al.  Linking pattern completion in the hippocampus to predictive coding in visual cortex , 2016, Nature Neuroscience.

[14]  Ehren L. Newman,et al.  Moderate excitation leads to weakening of perceptual representations. , 2010, Cerebral cortex.

[15]  R. O’Reilly,et al.  Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. , 2003, Psychological review.

[16]  L. Davachi.  Item, context and relational episodic encoding in humans , 2006, Current Opinion in Neurobiology.

[17]  S. Gershman,et al.  Moderate levels of activation lead to forgetting in the think/no-think paradigm , 2013, Neuropsychologia.

[18]  H. Duvernoy,et al.  The Human Hippocampus: Functional Anatomy, Vascularization and Serial Sections with MRI , 1997 .

[19]  B. Everitt,et al.  Enhancing cognition by affecting memory reconsolidation , 2015, Current Opinion in Behavioral Sciences.

[20]  M. Jenkinson.  Non-linear registration aka Spatial normalisation , 2007 .

[21]  Lauren V. Kustner,et al.  Shaping of Object Representations in the Human Medial Temporal Lobe Based on Temporal Regularities , 2012, Current Biology.

[22]  M. Kindt,et al.  Prediction Error Governs Pharmacologically Induced Amnesia for Learned Fear , 2013, Science.

[23]  Marc N. Coutanche,et al.  Distinguishing Multi-voxel Patterns and Mean Activation: Why, How, and What Does It Tell Us? a Question of Spatial Frequency , 2022 .

[24]  Valerie A. Carr,et al.  Imaging the Human Medial Temporal Lobe with High-Resolution fMRI , 2010, Neuron.

[25]  Margaret L. Schlichting,et al.  Learning-related representational changes reveal dissociable integration and separation signatures in the hippocampus and prefrontal cortex , 2015, Nature Communications.

[26]  Jordan Poppenk,et al.  Briefly Cuing Memories Leads to Suppression of Their Neural Representations , 2014, The Journal of Neuroscience.

[27]  C. Stark,et al.  Pattern separation in the hippocampus , 2011, Trends in Neurosciences.

[28]  Justin C. Hulbert,et al.  Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice. , 2015, Cerebral cortex.