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学习周边视觉中的字母识别。

Learning letter identification in peripheral vision.

作者信息

Chung Susana T L, Levi Dennis M, Tjan Bosco S

机构信息

College of Optometry and Center for Neuro-Engineering and Cognitive Science, University of Houston, 505 J Davis Armistead Bldg, Houston, TX 77204-2020, USA.

出版信息

Vision Res. 2005 May;45(11):1399-412. doi: 10.1016/j.visres.2004.11.021.

Abstract

Performance for a variety of visual tasks improves with practice. The purpose of this study was to determine the nature of the processes underlying perceptual learning of identifying letters in peripheral vision. To do so, we tracked changes in contrast thresholds for identifying single letters presented at 10 degrees in the inferior visual field, over a period of six consecutive days. The letters (26 lowercase Times-Roman letters, subtending 1.7 degrees) were embedded within static two-dimensional Gaussian luminance noise, with rms contrast ranging from 0% (no noise) to 20%. We also measured the observers' response consistency using a double-pass method on days 1, 3 and 6, by testing two additional blocks on each of these days at luminance noise of 3% and 20%. These additional blocks were the exact replicates of the corresponding block at the same noise contrast that was tested on the same day. We analyzed our results using both the linear amplifier model (LAM) and the perceptual template model (PTM). Our results showed that following six days of training, the overall reduction (improvement across all noise levels) in contrast threshold for our seven observers averaged 21.6% (range: 17.2-31%). Despite fundamental differences between LAM and PTM, both models show that learning leads to an improvement of the perceptual template (filter) such that the template is more capable of extracting the crucial information from the signal. Results from both the PTM analysis and the double-pass experiment imply that the stimulus-dependent component of the internal noise does not change with learning.

摘要

各种视觉任务的表现会随着练习而提高。本研究的目的是确定周边视觉中字母识别的知觉学习背后的过程的本质。为此,我们连续六天跟踪了在下视野中10度处呈现的单个字母识别的对比度阈值的变化。字母(26个小写的Times-Roman字母,视角为1.7度)嵌入在静态二维高斯亮度噪声中,均方根对比度范围从0%(无噪声)到20%。我们还在第1、3和6天使用双通方法测量了观察者的反应一致性,在这些日子里,在3%和20%的亮度噪声下测试另外两个块。这些额外的块是同一天在相同噪声对比度下测试的相应块的精确复制品。我们使用线性放大器模型(LAM)和知觉模板模型(PTM)分析了我们的结果。我们的结果表明,经过六天的训练,我们七名观察者的对比度阈值的总体降低(在所有噪声水平上的提高)平均为21.6%(范围:17.2-31%)。尽管LAM和PTM之间存在根本差异,但两个模型都表明学习会导致知觉模板(滤波器)的改进,从而使模板更能够从信号中提取关键信息。PTM分析和双通实验的结果都表明,内部噪声的刺激依赖成分不会随着学习而改变。

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