@inproceedings{1886d4ebb7184681a5e268708e304ffe,
title = "Uncovering visual priors in spatial memory using serial reproduction",
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.",
keywords = "Vision, inductive biases, iterated learning, serial reproduction, spatial memory",
author = "Langlois, {Thomas A.} and Nori Jacoby and Jordan Suchow and Griffiths, {Thomas L.}",
note = "Publisher Copyright: {\textcopyright} CogSci 2017.; 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 ; Conference date: 26-07-2017 Through 29-07-2017",
year = "2017",
language = "English (US)",
series = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition",
publisher = "The Cognitive Science Society",
pages = "712--717",
booktitle = "CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society",
}