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  Simulating the Scaling of Long-Term Memory Retrieval

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.

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 Creators:
Haridi, S1, Author                 
Thalmann, M1, Author           
Schulz, E1, Author           
Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              

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 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.

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 Dates: 2023-08
 Publication Status: Published online
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 Identifiers: DOI: 10.32470/CCN.2023.1066-0
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Title: Conference on Cognitive Computational Neuroscience (CCN 2023)
Place of Event: Oxford, UK
Start-/End Date: 2023-08-24 - 2023-08-27

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Title: 2023 Conference on Cognitive Computational Neuroscience
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: P-2.101 Start / End Page: 784 - 787 Identifier: -