Abstract
Background: The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. New method: We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. Results: We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. Comparison with existing method(s): In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Conclusions: Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups.
| Original language | English (US) |
|---|---|
| Journal | Journal of Neuroscience Methods |
| DOIs | |
| State | Accepted/In press - Jan 1 2018 |
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Keywords
- Aging
- Bayesian inference
- Behavior
- Change-point test
- EM algorithm
- Gibbs sampling
- Laplace prior
- Learning
- MAP estimation
- Markov Chain Monte Carlo
- Reversal learning task
- Separable random field model
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
A separable two-dimensional random field model of binary response data from multi-day behavioral experiments. / Malem-Shinitski, Noa; Zhang, Yingzhuo; Gray, Daniel T.; Burke, Sara N.; Smith, Anne C.; Barnes, Carol A; Ba, Demba.
In: Journal of Neuroscience Methods, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A separable two-dimensional random field model of binary response data from multi-day behavioral experiments
AU - Malem-Shinitski, Noa
AU - Zhang, Yingzhuo
AU - Gray, Daniel T.
AU - Burke, Sara N.
AU - Smith, Anne C.
AU - Barnes, Carol A
AU - Ba, Demba
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Background: The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. New method: We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. Results: We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. Comparison with existing method(s): In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Conclusions: Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups.
AB - Background: The study of learning in populations of subjects can provide insights into the changes that occur in the brain with aging, drug intervention, and psychiatric disease. New method: We introduce a separable two-dimensional (2D) random field (RF) model for analyzing binary response data acquired during the learning of object-reward associations across multiple days. The method can quantify the variability of performance within a day and across days, and can capture abrupt changes in learning. Results: We apply the method to data from young and aged macaque monkeys performing a reversal-learning task. The method provides an estimate of performance within a day for each age group, and a learning rate across days for each monkey. We find that, as a group, the older monkeys require more trials to learn the object discriminations than do the young monkeys, and that the cognitive flexibility of the younger group is higher. We also use the model estimates of performance as features for clustering the monkeys into two groups. The clustering results in two groups that, for the most part, coincide with those formed by the age groups. Simulation studies suggest that clustering captures inter-individual differences in performance levels. Comparison with existing method(s): In comparison with generalized linear models, this method is better able to capture the inherent two-dimensional nature of the data and find between group differences. Conclusions: Applied to binary response data from groups of individuals performing multi-day behavioral experiments, the model discriminates between-group differences and identifies subgroups.
KW - Aging
KW - Bayesian inference
KW - Behavior
KW - Change-point test
KW - EM algorithm
KW - Gibbs sampling
KW - Laplace prior
KW - Learning
KW - MAP estimation
KW - Markov Chain Monte Carlo
KW - Reversal learning task
KW - Separable random field model
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UR - http://www.scopus.com/inward/citedby.url?scp=85049610929&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2018.04.006
DO - 10.1016/j.jneumeth.2018.04.006
M3 - Article
C2 - 29679704
AN - SCOPUS:85049610929
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -