The Episodic Flanker Effect: Memory Retrieval as Attention ...
Optimal Timing for Episodic Retrieval and Encoding …Optimal Timing for Episodic Retrieval and...
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Optimal Timing for Episodic Retrieval and Encoding for Event Understanding Qihong Lu 1 , Zi Ying Fan 2 , Uri Hasson 1 , Kenneth A. Norman 1 1. Department of Psychology and Princeton Neuroscience Institute, Princeton University; 2. Department of Computer Science, Princeton University Summary • We present a neural network that learns to use episodic memory for event prediction. • The learned memory-retrieval policy shows a speed-accuracy trade-off that is sensitive to the cost of incorrect predictions. • Models that selectively encode at event boundaries had fewer retrieval errors. • Collectively, this model provides insights about why, in real data, episodic retrieval seems to be sensitive to prediction demand and uncertainty [1] and why episodic encoding seems to happen selectively at event boundaries [3, 4]. Model detail Figure 1: A) The model architecture; B) Memories are a set of laterally competing evidence accumulators. Cortex is a recurrent neural network (LSTM) that predicts the upcoming state. Hippocampus represents memories as a set of ev- idence accumulators. Each memory is a previously saved cortical pattern. • Retrieval is an evidence-accumulation process that determines which memory to retrieve. Evidence is proportional to the similarity between the current state and stored memory. • The retrieval control layer controls the feed-forward weights and the level of lateral competition between different memories. • Encoding a new memory corresponds to adding a new accumulator. The model is trained with reinforcement learning. The reward is positive/negative if the prediction is correct/incorrect. The model can say “don’t know”, in which case the reward is zero. A context-dependent event prediction task An event sequence is a sample path from a event schema conditioned on a situation (fig 2). An event schema is a graph, where each transition is con- trolled by a particular feature of the situation (2 A). Thus, knowing the features of the situation is useful for event prediction. Figure 2: A) Sample an event sequence from the schema; B) An example trial. Use episodic memory to predict upcoming events There are three conditions (inspired by [1]): during test, the ongoing situation is ... • recently observed (RM; recent memory) • observed in distant past (DM; distant memory) • new (NM; no memory) In the DM condition, relevant information is not in working memory, so it needs to be recalled from episodic memory. DM prediction accuracy starts low, and increases after episodic recall takes place. Figure 3: Event prediction performance across the three conditions. Properties of the learned recall policy Recall is sensitive to whether information is already in working memory (fig 4: lower recall in RM con- dition than DM; see also fig 6). We also manipu- lated the penalty associated with incorrect predic- tions. When penalty is high, recall is delayed (fig 4 A vs. B) and false recall is low (fig 5), similar to a well-established model of hippocampus [2]. A B Figure 4: Memory activation in the A) low; and B) high penalty environment. A B Figure 5: ROC analysis for the memory activation in the A) low; and B) high penalty environment. Figure 6: Recall is suppressed when the queried information is in working memory (i.e. when uncertainty is low). Encoding at event boundaries reduces subsequent memory errors We found models that encode at event bound- aries performed better at subsequent recall (fig 7), compared to models that also encode episodic mem- ories within an event sequence (i.e. cumulative encoding), because encoding within an event se- quence leads to more confusable memories (fig 8). Figure 7: Event prediction accuracy for models that encode at event boundaries ; vs. models that also encode within an event sequence. Figure 8: A) The resulting memory chunks under the two encoding regimes; B) Encoding within an event sequence might cause subsequent false recall. Connecting all information make lures easier to reject. References & Acknowledgement [1] Chen, J. et al. (2016) Cereb Cortex. [2] Norman, K. (2010) Hippocampus. [2] Baldassano, C., et al. (2017) Neuron. [3] Ben-Yakov A., & Henson, N. (2018). J Neuro. Acknowledgement: This work was supported by a Multi-University Research Initiative grant to KAN and UH (ONR/DoD N00014-17-1-2961). Download the poster: https://tinyurl.com/CCN19-QL
Transcript of Optimal Timing for Episodic Retrieval and Encoding …Optimal Timing for Episodic Retrieval and...
Optimal Timing for Episodic Retrieval and Encoding for Event UnderstandingQihong Lu1, Zi Ying Fan2, Uri Hasson1, Kenneth A. Norman1
1. Department of Psychology and Princeton Neuroscience Institute, Princeton University; 2. Department of Computer Science, Princeton University
Summary•We present a neural network that learns to useepisodic memory for event prediction.
•The learned memory-retrieval policy shows aspeed-accuracy trade-off that is sensitive to thecost of incorrect predictions.
•Models that selectively encode at eventboundaries had fewer retrieval errors.
•Collectively, this model provides insights aboutwhy, in real data, episodic retrieval seems to besensitive to prediction demand and uncertainty[1] and why episodic encoding seems to happenselectively at event boundaries [3, 4].
Model detail
Figure 1: A) The model architecture; B) Memories are a setof laterally competing evidence accumulators.
Cortex is a recurrent neural network (LSTM) thatpredicts the upcoming state.Hippocampus represents memories as a set of ev-idence accumulators. Each memory is a previouslysaved cortical pattern.• Retrieval is an evidence-accumulation processthat determines which memory to retrieve.Evidence is proportional to the similarity betweenthe current state and stored memory.
•The retrieval control layer controls thefeed-forward weights and the level of lateralcompetition between different memories.
• Encoding a new memory corresponds to addinga new accumulator.
The model is trained with reinforcement learning.The reward is positive/negative if the prediction iscorrect/incorrect. The model can say “don’t know”,in which case the reward is zero.
A context-dependent eventprediction task
An event sequence is a sample path from a eventschema conditioned on a situation (fig 2). An eventschema is a graph, where each transition is con-trolled by a particular feature of the situation (2 A).Thus, knowing the features of the situation is usefulfor event prediction.
Figure 2: A) Sample an event sequence from the schema; B)An example trial.
Use episodic memory to predictupcoming events
There are three conditions (inspired by [1]): duringtest, the ongoing situation is ...• recently observed (RM; recent memory)• observed in distant past (DM; distant memory)•new (NM; no memory)In the DM condition, relevant information is not inworking memory, so it needs to be recalled fromepisodic memory. DM prediction accuracy startslow, and increases after episodic recall takes place.
Figure 3: Event prediction performance across the threeconditions.
Properties of the learned recallpolicy
Recall is sensitive to whether information is alreadyin working memory (fig 4: lower recall in RM con-dition than DM; see also fig 6). We also manipu-lated the penalty associated with incorrect predic-tions. When penalty is high, recall is delayed (fig 4A vs. B) and false recall is low (fig 5), similar to awell-established model of hippocampus [2].A
B
Figure 4: Memory activation in the A) low; and B) highpenalty environment.
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B
Figure 5: ROC analysis for the memory activation in the A)low; and B) high penalty environment.
Figure 6: Recall is suppressed when the queried information isin working memory (i.e. when uncertainty is low).
Encoding at event boundariesreduces subsequent memory errors
We found models that encode at event bound-aries performed better at subsequent recall (fig 7),compared to models that also encode episodic mem-ories within an event sequence (i.e. cumulativeencoding), because encoding within an event se-quence leads to more confusable memories (fig 8).
Figure 7: Event prediction accuracy for models that encode atevent boundaries ; vs. models that also encode within anevent sequence.
Figure 8: A) The resulting memory chunks under the twoencoding regimes; B) Encoding within an event sequencemight cause subsequent false recall. Connecting allinformation make lures easier to reject.
References & Acknowledgement
[1] Chen, J. et al. (2016) Cereb Cortex.[2] Norman, K. (2010) Hippocampus.[2] Baldassano, C., et al. (2017) Neuron.[3] Ben-Yakov A., & Henson, N. (2018). J Neuro.Acknowledgement: This work was supported bya Multi-University Research Initiative grant to KANand UH (ONR/DoD N00014-17-1-2961).Download the poster:https://tinyurl.com/CCN19-QL
