Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A predictive processing model of episodic memory and time perception

View ORCID ProfileZafeirios Fountas, Anastasia Sylaidi, View ORCID ProfileKyriacos Nikiforou, View ORCID ProfileAnil K. Seth, View ORCID ProfileMurray Shanahan, View ORCID ProfileWarrick Roseboom
doi: https://doi.org/10.1101/2020.02.17.953133
Zafeirios Fountas
1Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
2Emotech Labs, London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zafeirios Fountas
  • For correspondence: fountas@outlook.com
Anastasia Sylaidi
3Spike AI Research Labs, London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kyriacos Nikiforou
4Department of Computing, Imperial College London, London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kyriacos Nikiforou
Anil K. Seth
5Department of Informatics and Sackler Centre for Consciousness Science, University of Sussex, Sussex, UK
6Canadian Institute for Advanced Research (CIFAR) Program on Brain, Mind, and Consciousness, Toronto, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Anil K. Seth
Murray Shanahan
4Department of Computing, Imperial College London, London, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Murray Shanahan
Warrick Roseboom
5Department of Informatics and Sackler Centre for Consciousness Science, University of Sussex, Sussex, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Warrick Roseboom
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Human perception and experience of time is strongly influenced by ongoing stimulation, memory of past experiences, and required task context. When paying attention to time, time experience seems to expand; when distracted, it seems to contract. When considering time based on memory, the experience may be different than in the moment, exemplified by sayings like “time flies when you’re having fun”. Experience of time also depends on the content of perceptual experience – rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions between attention, memory, and perceptual stimulation is a likely reason that an overarching theory of time perception has been difficult to achieve. Here, we introduce a model of perceptual processing and episodic memory that makes use of hierarchical predictive coding, short-term plasticity, spatio-temporal attention, and episodic memory formation and recall, and apply this model to the problem of human time perception. In an experiment with ~ 13, 000 human participants we investigated the effects of memory, cognitive load, and stimulus content on duration reports of dynamic natural scenes up to ~ 1 minute long. Using our model to generate duration estimates, we compared human and model performance. Model-based estimates replicated key qualitative biases, including differences by cognitive load (attention), scene type (stimulation), and whether the judgement was made based on current or remembered experience (memory). Our work provides a comprehensive model of human time perception and a foundation for exploring the computational basis of episodic memory within a hierarchical predictive coding framework.

Author summary Experience of the duration of present or past events is a central aspect of human experience, the underlying mechanisms of which are not yet fully understood. In this work, we combine insights from machine learning and neuroscience to propose a combination of mathematical models that replicate human perceptual processing, long-term memory, attention, and duration perception. Our computational implementation of this framework can process information from video clips of ordinary life scenes, record and recall important events, and report the duration of these clips. To assess the validity of our proposal, we conducted an experiment with ~ 13, 000 human participants. Each was shown a video between 1-64 seconds long and reported how long they believed it was. Reports of duration by our computational model qualitatively matched these human reports, made about the exact same videos. This was true regardless of the video content, whether time was actively judged or based on memory of the video, or whether the participants focused on a single task or were distracted - all factors known to influence human time perception. Our work provides the first model of human duration perception to incorporate these diverse and complex factors and provides a basis to probe the deep links between memory and time in human experience.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/zfountas/prospective-retrospective-data

  • https://github.com/zfountas/prospective-retrospective-model

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted July 09, 2021.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A predictive processing model of episodic memory and time perception
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A predictive processing model of episodic memory and time perception
Zafeirios Fountas, Anastasia Sylaidi, Kyriacos Nikiforou, Anil K. Seth, Murray Shanahan, Warrick Roseboom
bioRxiv 2020.02.17.953133; doi: https://doi.org/10.1101/2020.02.17.953133
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A predictive processing model of episodic memory and time perception
Zafeirios Fountas, Anastasia Sylaidi, Kyriacos Nikiforou, Anil K. Seth, Murray Shanahan, Warrick Roseboom
bioRxiv 2020.02.17.953133; doi: https://doi.org/10.1101/2020.02.17.953133

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4382)
  • Biochemistry (9591)
  • Bioengineering (7090)
  • Bioinformatics (24856)
  • Biophysics (12600)
  • Cancer Biology (9956)
  • Cell Biology (14349)
  • Clinical Trials (138)
  • Developmental Biology (7948)
  • Ecology (12105)
  • Epidemiology (2067)
  • Evolutionary Biology (15988)
  • Genetics (10925)
  • Genomics (14738)
  • Immunology (9869)
  • Microbiology (23659)
  • Molecular Biology (9484)
  • Neuroscience (50856)
  • Paleontology (369)
  • Pathology (1539)
  • Pharmacology and Toxicology (2681)
  • Physiology (4013)
  • Plant Biology (8657)
  • Scientific Communication and Education (1508)
  • Synthetic Biology (2394)
  • Systems Biology (6433)
  • Zoology (1346)