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首页> 外文期刊>Behavior Research Methods, Instruments & Computers >Testing curvatures of learning functions on individual trial and block average data
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Testing curvatures of learning functions on individual trial and block average data

机译：根据单个试验和块平均数据测试学习功能的曲率

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摘要

Many models offer different explanations of learning processes, some of them predicting equal learning rates between conditions. The simplest method by which to assess this equality is to evaluate the curvature parameter for each condition, followed by a statistical test. However, this approach is highly dependent on the fitting procedure, which may come with built-in biases difficult to identify. Averaging the data per block of training would help reduce the noise present in the trial data, but averaging introduces a severe distortion on the curve, which can no longer be fitted by the original function. In this article, we first demonstrate what is the distortion resulting from block averaging. The block average learning function, once known, can be used to extract parameters when the performance is averaged over blocks or sessions. The use of averages eliminates an important part of the noise present in the data and allows good recovery of the learning curve parameters. Equality of curvatures can be tested with a test of linear hypothesis. This method can be performed on trial data or block average data, but it is more powerful with block average data.
机译：许多模型对学习过程提供了不同的解释，其中一些模型预测条件之间的学习率相等。评估该相等性的最简单方法是评估每种条件的曲率参数，然后进行统计检验。但是，此方法高度依赖于拟合过程，该过程可能附带难以识别的内置偏差。平均每个训练块的数据将有助于减少试验数据中存在的噪声，但是平均会导致曲线上的严重失真，原始功能无法再拟合该失真。在本文中，我们首先演示什么是块平均导致的失真。已知块平均学习功能，当在块或会话上对性能进行平均时，可以使用该函数提取参数。平均值的使用消除了数据中存在的噪声的重要部分，并且可以很好地恢复学习曲线参数。曲率相等性可以通过线性假设检验来检验。可以对试验数据或块平均数据执行此方法，但使用块平均数据功能更强大。

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