Abstract：
Inferred mechanisms of learning, such as those involved in improvements resulting from perceptual training, are reliant on (and reflect) the functional forms that models of learning take. However, previous investigations of the functional forms of perceptual learning have been limited in ways that are incompatible with the known mechanisms of learning. For instance, previous work has overwhelmingly aggregated learning data across learning participants, learning trials, or both. Here we approach the study of the functional form of perceptual learning on the by-person and by-trial levels at which the mechanisms of learning are expected to act. Each participant completed one of two visual perceptual learning tasks over the course of two days, with the first 75% of task performance using a single reference stimulus (i.e., \"training\") and the last 25% using an orthogonal reference stimulus (to test generalization). Five learning functions, coming from either the exponential or the power family, were fit to each participant\'s data. The exponential family was uniformly supported by Bayesian Information Criteria (BIC) model comparisons. The simplest exponential function was the best fit to learning on a texture oddball detection task, while a Weibull (augmented exponential) function tended to be the best fit to learning on a dot-motion discrimination task. The support for the exponential family corroborated previous by-person investigations of the functional form of learning, while the novel evidence supporting the Weibull learning model has implications for both the analysis and the mechanistic bases of the learning.
摘要：
推断的学习机制，比如那些参与感知训练带来的改进的人，依赖于(并反映)学习模型采取的功能形式。然而,以前对知觉学习的功能形式的研究仅限于与已知的学习机制不兼容的方式。例如,以前的工作压倒性地汇总了学习参与者的学习数据，学习试验，或者两者兼而有之。在这里，我们研究了在个人和试验水平上对感知学习的功能形式的研究，在这些水平上，学习机制有望发挥作用。每个参与者在两天的时间里完成了两个视觉感知学习任务之一，前75%的任务性能使用单个参考刺激(即，\“training\”)和最后25%使用正交参考刺激(测试泛化)。五个学习功能，来自指数家族或权力家族，符合每个参与者的数据。指数族得到贝叶斯信息标准(BIC)模型比较的一致支持。最简单的指数函数是最适合学习纹理怪球检测任务，而Weibull（增强指数）函数往往是最适合在点运动辨别任务上学习的方法。对指数家族的支持证实了以前对学习功能形式的个人调查，而支持Weibull学习模型的新证据对学习的分析和机制基础都有影响。
