Evidence map›Paper›PMID 34270350›Full record

ArticleAnnual review of vision science2021

Remembering the Past to See the Future.

Nicole C Rust, Stephanie E Palmer

Abstract read
In one paragraph

Article in Annual review of vision science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. The neuron as a direct data-driven controller.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Article
  8. Article
  9. Simple synaptic modulations implement diverse novelty computations.bioRxiv : the preprint server for biology · 2024
    Article
  10. Article
  11. Review
  12. Article
  13. Scene Perception and Visuospatial Memory Converge at the Anterior Edge of Visually Responsive Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nicole C RustDepartment of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104; email: nrust@psych.upenn.edu.
Stephanie E PalmerDepartment of Organismal Biology and Anatomy, University of Chicago, Illinois 60637; email: sepalmer@uchicago.edu.

Funding

The neural mechanisms responsible for recognizing and remembering novel objects and scenesR01EY020851 · NEI · UNIVERSITY OF PENNSYLVANIA · PI RUST, NICOLE C · 2010 to 2018
$3.4M
Coarse-graining approaches to networks, learning, and behaviorR01EB026943 · NIBIB · UNIVERSITY OF CHICAGO · PI BIALEK, WILLIAM, PALMER, STEPHANIE E · 2018 to 2020
$1.1M
NEI NIH HHS R01 EY020851NIBIB NIH HHS R01 EB026943
6 · The paper itself

Abstract

In addition to the role that our visual system plays in determining what we are seeing right now, visual computations contribute in important ways to predicting what we will see next. While the role of memory in creating future predictions is often overlooked, efficient predictive computation requires the use of information about the past to estimate future events. In this article, we introduce a framework for understanding the relationship between memory and visual prediction and review the two classes of mechanisms that the visual system relies on to create future predictions. We also discuss the principles that define the mapping from predictive computations to predictive mechanisms and how downstream brain areas interpret the predictive signals computed by the visual system.

Indexed as

BrainVision, OcularBrain Mappingmemorypredictionvision

Identifiers

PMID34270350
PMCPMC9751846

What OpenQuestion holds

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Registered trials

None linked

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.