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Author Cousineau, Denis ♦ Hélie, Sébastien ♦ Lefebvre, Christine
Source SpringerLink
Content type Text
Publisher Springer-Verlag
File Format PDF
Copyright Year ©2003
Language English
Subject Domain (in DDC) Philosophy & psychology ♦ Psychology
Subject Keyword Cognitive Psychology
Abstract 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. Theblock 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.
ISSN 07433808
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2003-01-01
Publisher Place New York
e-ISSN 15325970
Journal Behavior Research Methods
Volume Number 35
Issue Number 4
Page Count 11
Starting Page 493
Ending Page 503


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Source: SpringerLink