Literature DB >> 32502415

Supervised Learning Occurs in Visual Perceptual Learning of Complex Natural Images.

Sebastian M Frank1, Andrea Qi2, Daniela Ravasio2, Yuka Sasaki2, Eric L Rosen3, Takeo Watanabe4.   

Abstract

There have been long-standing debates regarding whether supervised or unsupervised learning mechanisms are involved in visual perceptual learning (VPL) [1-14]. However, these debates have been based on the effects of simple feedback only about response accuracy in detection or discrimination tasks of low-level visual features such as orientation [15-22]. Here, we examined whether the content of response feedback plays a critical role for the acquisition and long-term retention of VPL of complex natural images. We trained three groups of human subjects (n = 72 in total) to better detect "grouped microcalcifications" or "architectural distortion" lesions (referred to as calcification and distortion in the following) in mammograms either with no trial-by-trial feedback, partial trial-by-trial feedback (response correctness only), or detailed trial-by-trial feedback (response correctness and target location). Distortion lesions consist of more complex visual structures than calcification lesions [23-26]. We found that partial feedback is necessary for VPL of calcifications, whereas detailed feedback is required for VPL of distortions. Furthermore, detailed feedback during training is necessary for VPL of distortion and calcification lesions to be retained for 6 months. These results show that although supervised learning is heavily involved in VPL of complex natural images, the extent of supervision for VPL varies across different types of complex natural images. Such differential requirements for VPL to improve the detectability of lesions in mammograms are potentially informative for the professional training of radiologists.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  breast cancer; feedback; high-level vision; mammogram; natural stimuli; radiology; supervised learning; unsupervised learning; visual perceptual learning

Year:  2020        PMID: 32502415      PMCID: PMC7415644          DOI: 10.1016/j.cub.2020.05.050

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  40 in total

1.  Perceptual learning without perception.

Authors:  T Watanabe; J E Náñez; Y Sasaki
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3.  Perceptual learning without feedback in non-stationary contexts: data and model.

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5.  Modeling perceptual learning: difficulties and how they can be overcome.

Authors:  M H Herzog; M Fahle
Journal:  Biol Cybern       Date:  1998-02       Impact factor: 2.086

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7.  Long-term learning in vernier acuity: effects of stimulus orientation, range and of feedback.

Authors:  M Fahle; S Edelman
Journal:  Vision Res       Date:  1993-02       Impact factor: 1.886

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Authors:  A Karni; D Sagi
Journal:  Nature       Date:  1993-09-16       Impact factor: 49.962

Review 9.  Visual Perceptual Learning and Models.

Authors:  Barbara Dosher; Zhong-Lin Lu
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Journal:  Cancer       Date:  2018-11-08       Impact factor: 6.860

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