ArticleeLife2024
A dynamic neural resource model bridges sensory and working memory.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Intrinsic rewards guide visual resource allocation via reinforcement learning.Nature human behaviour · 2026Article
- Regulating Memory Use: Individual Differences in Working Memory Capacity in Natural Tasks.Behavioral sciences (Basel, Switzerland) · 2026Article
- Double-decision Response Time Models of Recall and Recognition Support Resource Accounts of Visual Working Memory.Computational brain & behavior · 2026Article
- The hierarchical levels of information activities in the subject's psychology and cognition.Frontiers in psychology · 2026Article
- Temporal dynamics and readout latency in perception and iconic memory.Journal of vision · 2025Article
- Facial feature representations in visual working memory: A reverse correlation study.Journal of vision · 2025Article
- Unexpected higher resilience to distraction during visual working memory in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Dissecting the components of error in analogue report tasks.Behavior research methods · 2024Article
- Sensory-memory interactions via modular structure explain errors in visual working memory.eLife · 2024Article
- Article
- Memorable first impressions.eLife · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or 'iconic' memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.