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Palimpsest Working Memory

Matthey-De-L'Endroit, Loïc; (2019) Palimpsest Working Memory. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Despite its key role in cognition, the mechanisms underlying working memory remain much debated. It has often been observed that human performance on memory tasks is severely limited, but the two main classes of theories examining these limits leave open many key issues into their underlying sources. More specifically, the question of how multiple stimuli are represented and distinguished in visual working memory is still not well understood. As a first attempt at tackling these issues, we introduce a probabilistic palimpsest model which uses the activity of a single population of neurons to encode several multi-featured items. This population is used in a probabilistic framework to store and recall visual stimuli on a trial-by-trial basis, making it possible to account for many qualitative aspects of existing experimental data. In our setting, the underlying nature of a memory item, and the interference between concurrent stimuli, depend entirely on the characteristics of the population representation. We explore how much can be explained about the patterns of errors observed in human reports purely from the representations being used, without explicitly addressing how recall mechanisms could affect it. We provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider different types of population codes, where information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. We find that a tight balance between these two types of information is required to capture the different types of error seen in human experimental data fully. Our model can also be readily extended to sequentially presented data, making full use of our palimpsest construction. This allows us to study and account for experimental data that have not previously been explored extensively. We argue that our work constitutes an important step towards mechanistic models of visual working memory that provide a more holistic account of human responses based on computational principles.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Palimpsest Working Memory
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10079329
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