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From recency to central tendency biases in working memory: a unifying network model

View ORCID ProfileVezha Boboeva, View ORCID ProfileAlberto Pezzotta, View ORCID ProfileClaudia Clopath, View ORCID ProfileAthena Akrami
doi: https://doi.org/10.1101/2022.05.16.491352
Vezha Boboeva
1Sainsbury Wellcome Centre, University College London
2Department of Bioengineering, Imperial College London
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  • ORCID record for Vezha Boboeva
Alberto Pezzotta
3Gatsby Computational Neuroscience Unit, University College London
4The Francis Crick Institute
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Claudia Clopath
2Department of Bioengineering, Imperial College London
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  • For correspondence: [email protected]
Athena Akrami
1Sainsbury Wellcome Centre, University College London
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  • For correspondence: [email protected]
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Abstract

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* These authors jointly supervised this work

  • We have added a new Section 1.5 together with Fig.5. We revised Fig. 3F & 7E since we found a minor mistake in one of our analysis codes that computed the n-trial back biases for different delay intervals; correcting the error made the effects clearer. We revised Fig. 8 to include human data We revised Fig. S2 to include phase diagram In the previous version, the colorbar reported the incorrect fraction classified in Figs 1B, 2C, 7B (new 8B), S2C, S3A, S5B. We have corrected this in this version of the manuscript.

  • https://github.com/vboboeva/ParametricWorkingMemory_Data

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 26, 2023.
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From recency to central tendency biases in working memory: a unifying network model
Vezha Boboeva, Alberto Pezzotta, Claudia Clopath, Athena Akrami
bioRxiv 2022.05.16.491352; doi: https://doi.org/10.1101/2022.05.16.491352
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From recency to central tendency biases in working memory: a unifying network model
Vezha Boboeva, Alberto Pezzotta, Claudia Clopath, Athena Akrami
bioRxiv 2022.05.16.491352; doi: https://doi.org/10.1101/2022.05.16.491352

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