Xie Xin-Yu, Yu Cong
Psychology, McGovern Brain Research, and Center for Life Sciences, Peking University, China.
Psychology, McGovern Brain Research, and Center for Life Sciences, Peking University, China.
Vision Res. 2019 Mar;156:39-45. doi: 10.1016/j.visres.2019.01.007. Epub 2019 Feb 2.
Perceptual learning is often interpreted as learning of fine stimulus templates. However, we have proposed that perceptual learning is more than template learning, in that more abstract statistical rules may have been learned, so that learning can transfer to stimuli at different precisions. Here we provide new evidence to support this view: Perceptual learning of Vernier discrimination at high noise, which has thresholds approximately 10 times as much as those at zero noise, is initially non-transferrable to zero noise. However, additional exposure to a noise-free Vernier-forming Gabor, which is ineffective alone, not only maximizes zero-noise fine Vernier discrimination, but also further enhances high-noise Vernier performance. Such high-threshold coarse Vernier training cannot impact the fine stimulus template directly. One plausible explanation is that the observers have learned the statistical rules that can apply to standardized input distributions to improve discrimination, regardless of the original precision of these distributions.
知觉学习通常被解释为对精细刺激模板的学习。然而，我们已经提出，知觉学习不仅仅是模板学习，因为可能已经学习了更抽象的统计规则，这样学习就可以转移到不同精度的刺激上。在这里，我们提供新的证据来支持这一观点：在高噪声下对游标辨别力的知觉学习，其阈值大约是零噪声下阈值的10倍，最初是不能转移到零噪声情况的。然而，额外接触单独无效的无噪声游标形成的Gabor，不仅能使零噪声下精细游标辨别力最大化，还能进一步提高高噪声下的游标性能。这种高阈值的粗游标训练不能直接影响精细刺激模板。一个合理的解释是，观察者已经学习了可以应用于标准化输入分布以提高辨别的统计规则，而不管这些分布的原始精度如何。
