Analysis and design of behavioral experiments to characterize population learning.

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.

[1]  Piet de Jong,et al.  Covariances for smoothed estimates in state space models , 1988 .

[2]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[3]  H Eichenbaum,et al.  Normal olfactory discrimination learning set and facilitation of reversal learning after medial-temporal damage in rats: implications for an account of preserved learning abilities in amnesia , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  S. Tonegawa,et al.  CA1-specific N-methyl-d-aspartate receptor knockout mice are deficient in solving a nonspatial transverse patterning task , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Roger Sauter,et al.  In All Likelihood , 2002, Technometrics.

[7]  E N Brown,et al.  A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.

[8]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[9]  S. Kakade,et al.  Acquisition and extinction in autoshaping. , 2002, Psychological review.

[10]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[11]  Pia Veldt Larsen,et al.  In All Likelihood: Statistical Modelling and Inference Using Likelihood , 2003 .

[12]  Harry F. Baker,et al.  Visual discrimination learning impairments produced by combined transections of the anterior temporal stem, amygdala and fornix in marmoset monkeys , 2001, Brain Research.

[13]  Emery N. Brown,et al.  Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering , 2004, Neural Computation.

[14]  I Q Whishaw,et al.  Acquisition and retention by hippocampal rats of simple, conditional, and configural tasks using tactile and olfactory cues: implications for hippocampal function. , 1991, Behavioral neuroscience.

[15]  Patrick Suppes.  On deriving models in the social sciences , 1990 .

[16]  Peter Dayan,et al.  Expected and Unexpected Uncertainty: ACh and NE in the Neocortex , 2002, NIPS.

[17]  Emery N Brown,et al.  Dynamic Analysis of Learning in Behavioral Experiments , 2004, The Journal of Neuroscience.

[18]  R. Shumway,et al.  AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM , 1982 .

[19]  Eugene Galanter,et al.  Handbook of mathematical psychology: I. , 1963 .

[20]  L. Frank,et al.  Single Neurons in the Monkey Hippocampus and Learning of New Associations , 2003, Science.

[21]  F. Roman,et al.  Learning and memory of odor-reward association: selective impairment following horizontal diagonal band lesions. , 1993, Behavioral neuroscience.

[22]  Emery N. Brown,et al.  Estimating a State-space Model from Point Process Observations Emery N. Brown , 2022 .

[23]  T. Robbins,et al.  Dissociable Forms of Inhibitory Control within Prefrontal Cortex with an Analog of the Wisconsin Card Sort Test: Restriction to Novel Situations and Independence from “On-Line” Processing , 1997, The Journal of Neuroscience.

[24]  M. Baxter,et al.  Preserved anterograde and retrograde memory of rapidly acquired olfactory discrminations after neurotoxic hippocampal lesions , 2004, Hippocampus.

[25]  Richard H. Jones,et al.  Longitudinal Data with Serial Correlation : A State-Space Approach , 1994 .

[26]  J. Ware,et al.  Random-effects models for serial observations with binary response. , 1984, Biometrics.

[27]  Bita Moghaddam,et al.  Glutamate receptors in the rat medial prefrontal cortex regulate set-shifting ability. , 2003, Behavioral neuroscience.

[28]  Morgan D Barense,et al.  Perceptual Attentional Set-Shifting Is Impaired in Rats with Neurotoxic Lesions of Posterior Parietal Cortex , 2003, The Journal of Neuroscience.

[29]  L. Fahrmeir,et al.  Multivariate statistical modelling based on generalized linear models , 1994 .