Assessing the functions underlying learning using by-trial and by-participant models: Evidence from two visual perceptual learning paradigms.
Assessing the functions underlying learning using by-trial and by-participant models: Evidence from two visual perceptual learning paradigms.
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DOI：
10.1167/jov.21.13.5
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发表时间：
2021-12-01
影响因子：
1.8
通讯作者：
Green CS
中科院分区：
文献类型：
--
作者：
Cochrane A;Green CS
学习的推断机制，例如那些涉及到由感知训练引起的改进的机制，依赖于（并反映）学习模型的功能形式。然而，先前对感知学习的功能形式的研究在与已知的学习机制不相容的方式上受到限制。例如，以前的工作绝大多数是在学习参与者、学习试验或两者之间汇总学习数据。在这里，我们将在个人和试验层面上研究感知学习的功能形式，在这些层面上，学习机制有望发挥作用。每个参与者在两天的过程中完成两个视觉感知学习任务中的一个，任务的前75%使用单一参考刺激（即“训练”），最后25%使用正交参考刺激（以测试泛化）。五个学习函数，来自指数或幂族，拟合每个参与者的数据。该指数族得到贝叶斯信息准则（BIC）模型比较的一致支持。最简单的指数函数最适合纹理奇球检测任务的学习，而威布尔（增广指数）函数更适合点运动识别任务的学习。对指数族的支持证实了以前对功能形式学习的个人调查，而支持威布尔学习模型的新证据对学习的分析和机制基础都有影响。
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.
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影响因子：
64.8
作者：
Ahissar, M;Hochstein, S
通讯作者：
Hochstein, S
影响因子：
1.8
作者：
Leibowitz, Nathaniel;Baum, Barak;Karniel, Amir
通讯作者：
Karniel, Amir
DOI：
10.1073/pnas.96.24.14085
发表时间：
1999-11-23
影响因子：
11.1
作者：
Liu, ZL
通讯作者：
Liu, ZL
DOI：
10.1073/pnas.0404965101
发表时间：
2004-09-07
影响因子：
11.1
作者：
Gallistel, CR;Fairhurst, S;Balsam, P
通讯作者：
Balsam, P
影响因子：
64.8
作者：
FIORENTINI, A;BERARDI, N
通讯作者：
BERARDI, N