HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 93/3/1776    most recent 00765.2004v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Smith, A. C. Articles by Brown, E. N. Search for Related Content PubMed PubMed Citation Articles by Smith, A. C. Articles by Brown, E. N.

INNOVATIVE METHODOLOGY

Analysis and Design of Behavioral Experiments to Characterize Population Learning

¹Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston; ²Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Cambridge, Massachusetts; and ³Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania

In population learning studies, between-subject response differences are an important source of variance that must be characterized to identify accurately the features of the learning process common to the population. Although learning is a dynamic process, current population analyses do not use dynamic estimation methods, do not compute both population and individual learning curves, and use learning criteria that are less than optimal. We develop a state-space random effects (SSRE) model to estimate population and individual learning curves, ideal observer curves, and learning trials, and to make dynamic assessments of learning between two populations and within the same population that avoid multiple hypothesis tests. In an 80-trial study of an NMDA antagonist's effect on the ability of rats to execute a set-shift task, our dynamic assessments of learning demonstrated that both the treatment and control groups learned, yet, by trial 35, the treatment group learning was significantly impaired relative to control. We used our SSRE model in a theoretical study to evaluate the design efficiency of learning experiments in terms of the number of animals per group and number of trials per animal required to characterize learning differences between two populations. Our results demonstrated that a maximum difference in the probability of a correct response between the treatment and control group learning curves of 0.07 (0.20) would require 15 to 20 (5 to 7) animals per group in an 80 (60)-trial experiment. The SSRE model offers a practical approach to dynamic analysis of population learning and a theoretical framework for optimal design of learning experiments.

This article has been cited by other articles:

				A. C. Smith, S. Wirth, W. A. Suzuki, and E. N. Brown Bayesian Analysis of Interleaved Learning and Response Bias in Behavioral Experiments J Neurophysiol, March 1, 2007; 97(3): 2516 - 2524. [Abstract] [Full Text] [PDF]

				W. A. Suzuki and E. N. Brown Behavioral and Neurophysiological Analyses of Dynamic Learning Processes Behav Cogn Neurosci Rev, June 1, 2005; 4(2): 67 - 95. [Abstract] [PDF]