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Conference Paper

Simulating the Scaling of Long-Term Memory Retrieval

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Haridi,  S       
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Thalmann,  M
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schulz,  E
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Haridi, S., Thalmann, M., & Schulz, E. (2023). Simulating the Scaling of Long-Term Memory Retrieval. In 2023 Conference on Cognitive Computational Neuroscience (pp. 784-787). doi:10.32470/CCN.2023.1066-0.


Cite as: https://hdl.handle.net/21.11116/0000-000D-4DC2-2
Abstract
The average human lives about 70 years, during which a growing number of memories are accumulated. But how are we still able to quickly retrieve currently relevant memories? We explore how the number of items (i.e., set size) affects retrieval time (RT), treating retrieval as a search process. Our study simulates the RTs using a sample-based retrieval process inspired by the search of associative memory model (Raaijmakers & Shiffrin, 1981). Our simulations show: 1. RTs increase with set size. 2. improved memory cues mitigate this effect and may alleviate retrieval failures. Specifically, increasing the similarity between cue and target improved recall performance, especially for larger set sizes. 3. the specificity of the cue was crucial for improving performance, and unspecific cues even increased RTs compared to baseline. These results suggest that effective memory retrieval crucially depends on the quality of the cue.