Factorial comparison of working memory models.
Open accessAbstract
Three questions have been prominent in the study of visual working memory limitations: (a) What is the nature of mnemonic precision (e.g., quantized or continuous)? (b) How many items are remembered? (c) To what extent do spatial binding errors account for working memory failures? Modeling studies have typically focused on comparing possible answers to a single one of these questions, even though the result of such a comparison might depend on the assumed answers to both others. Here, we consider every possible combination of previously proposed answers to the individual questions. Each model is then a point in a 3-factor model space containing a total of 32 models, of which only 6 have been tested previously. We compare all models on data from 10 delayed-estimation experiments from 6 laboratories (for a total of 164 subjects and 131,452 trials). Consistently across experiments, we find that (a) mnemonic precision is not quantized but continuous and not equal but variable across items and trials; (b) the number of remembered items is likely to be variable across trials, with a mean of 6.4 in the best model (median across subjects); (c) spatial binding errors occur but explain only a small fraction of responses (16.5% at set size 8 in the best model). We find strong evidence against all 6 documented models. Our results demonstrate the value of factorial model comparison in working memory.
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A model recovery analysis was performed to test the ability to distinguish the 6 core models and the 2 stronger versions of the Bayesian model.
“We performed a model recovery analysis [93] to test our ability to distinguish our 6 core models, as well as the 2 stronger versions of the Bayesian model.”
From Comparing Bayesian and non-Bayesian accounts of human confidence reports (2018) · cites this paper as [93]
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Bays, P. M. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx351 · Cited by 45 · see citing works
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Self-citationStimulus-Specific Visual Working Memory Representations in Human Cerebellar Lobule VIIb/VIIIa
Brissenden, J. A., Tobyne, S. M., Halko, M. A., & Somers, D. C. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.1253-20.2020 · Cited by 45 · see citing works
Oberauer, K., Stoneking, C. J., Wabersich, D., & Lin, H.-Y. (2017) · Journal of Vision · doi:10.1167/17.5.11 · Cited by 44 · see citing works
A neural model of retrospective attention in visual working memory
Bays, P. M., & Taylor, R. T. (2017) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.12.001 · Cited by 44 · see citing works
Human online adaptation to changes in prior probability
Norton, E. H., Acerbi, L., Ma, W. J., & Landy, M. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006681 · Cited by 40 · see citing works
Self-citationAllocation of resources in working memory: Theoretical and empirical implications for visual search
Cong, S. H., & Kerzel, D. (2021) · Psychonomic Bulletin & Review · doi:10.3758/s13423-021-01881-5 · Cited by 44 · see citing works
Visual short-term memory for oriented, colored objects
Shin, H., & Ma, W. J. (2017) · Journal of Vision · doi:10.1167/17.9.12 · Cited by 43 · see citing works
Self-citationThe development of metacognitive accuracy in working memory across childhood.
Forsberg, A., Blume, C. L., & Cowan, N. (2021) · Developmental Psychology · doi:10.1037/dev0001213 · Cited by 41 · see citing works
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Bindings in working memory: The role of object-based attention
Gao, Z., Wu, F., Qiu, F., He, K., Yang, Y., & Shen, M. (2016) · Attention Perception & Psychophysics · doi:10.3758/s13414-016-1227-z · Cited by 40 · see citing works
Interference and memory capacity limitations.
Endress, A. D., & Szabó, S. (2017) · Psychological Review · doi:10.1037/rev0000071 · Cited by 40 · see citing works
Gross, S. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0343 · Cited by 38 · see citing works
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Flexible Working Memory Through Selective Gating and Attentional Tagging
Kruijne, W., Bohté, S. M., Roelfsema, P. R., & Olivers, C. N. L. (2020) · Neural Computation · doi:10.1162/neco_a_01339 · Cited by 40 · see citing works
Is working memory domain-general or domain-specific?
Nozari, N., & Martin, R. C. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.06.006 · Cited by 44 · see citing works
Superior Intraparietal Sulcus Controls the Variability of Visual Working Memory Precision
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“Plateau”-related summary statistics are uninformative for comparing working memory models
van den Berg, R., & Ma, W. J. (2014) · Attention Perception & Psychophysics · doi:10.3758/s13414-013-0618-7 · Cited by 36 · see citing works
Self-citationThe cost of misremembering: Inferring the loss function in visual working memory
Sims, C. R. (2015) · Journal of Vision · doi:10.1167/15.3.2 · Cited by 34 · see citing works
Variable precision in visual perception.
Shen, S., & Ma, W. J. (2018) · Psychological Review · doi:10.1037/rev0000128 · Cited by 34 · see citing works
Self-citationComputational neuroscience across the lifespan: Promises and pitfalls
van den Bos, W., Bruckner, R., Nassar, M. R., Mata, R., & Eppinger, B. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.09.008 · Cited by 30 · see citing works
Smith, P. L., & Corbett, E. A. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1491-0 · Cited by 32 · see citing works
Monkeys and humans take local uncertainty into account when localizing a change
Devkar, D. T., Wright, A. A., & Ma, W. J. (2017) · Journal of Vision · doi:10.1167/17.11.4 · Cited by 31 · see citing works
Self-citationMapping visual working memory models to a theoretical framework
Ngiam, W. X. Q. (2023) · Psychonomic Bulletin & Review · doi:10.3758/s13423-023-02356-5 · Cited by 31 · see citing works
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Representation and computation in working memoryPreprint
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Self-citationThe persistence of memory: how the brain encodes time in memory
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Predicting visual working memory with multimodal magnetic resonance imaging
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Uncertainty is maintained and used in working memory
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Precision requirements do not affect the allocation of visual working memory capacity
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Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach
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Neural Correlates Underlying the Precision of Visual Working Memory
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Response-time evidence for mixed memory states in a sequential-presentation change-detection task
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Modeling accuracy, response time, and bias in continuous orientation judgments.
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An interference model for visual working memory: Applications to the change detection task
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The Quest for Simplicity in Human Learning: Identifying the Constraints on Attention
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Working Memory: Flexible but Finite
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Problematic usage of the Zhang and Luck mixture modelPreprint
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- Authors
- van den Berg, R., Awh, E., & Ma, W. J.
- Affiliations
- University of Oregon (US)
- New York University (US)
- Venue
- Psychological Review
- Published
- 2014
- Volume
- 121
- Issue
- 1
- Pages
- 124-149
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- PMC
- PMC4159389
- OpenAlex
- W2131353194
- Cited by
- 311
- References
- 52
- Citations by year
- 2026: 15 · 2025: 14 · 2024: 33 · 2023: 23 · 2022: 22 · 2021: 20 · 2020: 25 · 2019: 26 · 2018: 31 · 2017: 30 · 2016: 37 · 2015: 22 · 2014: 13
- FWCI
- 19.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Funders
- National Eye Institute (R01EY020958)
- Army Research Office (W911NF-12-1-0262)
- Sponsor name not included (NIH-R01MH077105)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
- Keywords
- visual working memory, working memory models, set size, model spaces
- MeSH terms
- Attention, Data Interpretation, Statistical, Factor Analysis, Statistical, Humans, Memory, Short-Term, Models, Psychological, Neuropsychological Tests, Pattern Recognition, Visual, Psychomotor Performance, Reaction Time, Retention, Psychology, Models, Statistical, Statistical Distributions, Likelihood Functions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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