Uncovering visual priors in spatial memory using serial reproduction

Thomas A. Langlois, Nori Jacoby, Jordan Suchow, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Visual memory can be understood as an inferential process that combines noisy information about the world with knowledge drawn from experience. Biases can arise during encoding of information from the outside world into internal representations, or during retrieval. In this work, we use the method of serial reproduction, in which information is passed along a chain of participants who try to recreate what they observed. We apply this method to the study of visual perception in the context of spatial memory biases for the remembered position of dots inside different geometric shapes. We present the results of non-parametric kernel density estimation of the end result of serial reproduction to model visual biases. We confirm previous findings, and show that memory biases revealed with our method are often more intricate and complex than what had previously been reported, suggesting that serial reproduction can be effective for studying perceptual priors.

Original languageEnglish (US)
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages712-717
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Externally publishedYes
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: Jul 26 2017Jul 29 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period7/26/177/29/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Vision
  • inductive biases
  • iterated learning
  • serial reproduction
  • spatial memory

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