August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
PLFest: A cross-platform application to support open science in perceptual learning research
Author Affiliations & Notes
  • Samyukta Jayakumar
    University of California, Riverside
  • Marcello Maniglia
    University of California, Riverside
  • Trevor Stavropoulos
    TAS Consulting
  • Hong Guan
    University of Wisconsin-Madison
  • C. Shawn Green
    University of Wisconsin-Madison
  • Aaron Seitz
    University of California, Riverside
  • Footnotes
    Acknowledgements  NEI R01 EY031226-01
Journal of Vision August 2023, Vol.23, 5847. doi:
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      Samyukta Jayakumar, Marcello Maniglia, Trevor Stavropoulos, Hong Guan, C. Shawn Green, Aaron Seitz; PLFest: A cross-platform application to support open science in perceptual learning research. Journal of Vision 2023;23(9):5847.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Perceptual Learning (PL), a training induced improvement in sensory perception, is known to be sensitive to the stimuli, task, and training parameters that can dramatically affect learning outcomes. However, to date there is little standardization of lab methodologies, which makes comparisons across studies difficult. This limits both our basic understanding of the plasticity of the perceptual systems and the possibility of developing effective translational approaches for rehabilitation of perceptual and cognitive deficits. Here, we introduce a cross-platform application built using the UNITY engine that enables and supports data collection on several platforms including computers, tablets and even smart phones. This platform supports a variety of PL training and assessment procedures, with a current emphasis on understanding PL of contrast sensitivity, as well as a number of outcome measures ranging from measures of basic vision, to cognitive measures, and even psychoacoustic measures. We here present an overview of the PLFest platform and preliminary data showing that it produces reliable data on Apple iPad tablet computers. This platform is intended to support data collection for large scale multi-site studies and will be made public as a tool to support open science, data sharing and reproducible science.


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