LINEAR-NONLINEAR-POISSON MODELS OF PRIMATE CHOICE DYNAMICS
Open accessAbstract
The equilibrium phenomenon of matching behavior traditionally has been studied in stationary environments. Here we attempt to uncover the local mechanism of choice that gives rise to matching by studying behavior in a highly dynamic foraging environment. In our experiments, 2 rhesus monkeys (Macacca mulatta) foraged for juice rewards by making eye movements to one of two colored icons presented on a computer monitor, each rewarded on dynamic variable-interval schedules. Using a generalization of Wiener kernel analysis, we recover a compact mechanistic description of the impact of past reward on future choice in the form of a Linear-Nonlinear-Poisson model. We validate this model through rigorous predictive and generative testing. Compared to our earlier work with this same data set, this model proves to be a better description of choice behavior and is more tightly correlated with putative neural value signals. Refinements over previous models include hyperbolic (as opposed to exponential) temporal discounting of past rewards, and differential (as opposed to fractional) comparisons of option value. Through numerical simulation we find that within this class of strategies, the model parameters employed by animals are very close to those that maximize reward harvesting efficiency.
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A few studies have estimated the time constant of the reward filter from behavior, and assessed how past rewards affect choice selection.
“So far a few studies have estimated the time constant of this filter from behavior, and assessed how past rewards affect choice selection8-12”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [9]
Monkey's choice behavior is fit well by a softmax function of the difference in the two incomes.
“Interestingly, the monkey’s choice behavior is fit well by a softmax function of the difference in the two incomes (Corrado et al., 2005) (Figure 5C).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Corrado et al., 2005
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References 60 · Cited by 206
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- Vaughan, W., Jr. (1982). Choice and the Rescorla-Wagner model. In M. L. Commons, R. J. Herrnstein, & H. Rachlin (Eds.), Quantitative analyses of behavior. Vol. II: Matching and maximizing accounts (pp. 263-279). Cambridge, MA: Ballinger.
- Authors
- Corrado, G. S., Sugrue, L. P., Seung, H. S., & Newsome, W. T.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Corrado, Greg S.
- Venue
- Journal of the Experimental Analysis of Behavior
- Publisher
- Wiley
- Published
- 2005-11
- Volume
- 84
- Issue
- 3
- Pages
- 581-617
- ISSN
- 0022-5002 (ISSN-L)
- PMC
- PMC1389782
- OpenAlex
- W2146760691
- Cited by
- 206
- References
- 60
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 10 · 2023: 8 · 2022: 6 · 2021: 12 · 2020: 9 · 2019: 7 · 2018: 12 · 2017: 5 · 2016: 6 · 2015: 7 · 2014: 12 · 2013: 9 · 2012: 12
- FWCI
- 7.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the Experimental Analysis of Behavior (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- matching behavior, rhesus monkeys, foraging, variable-interval schedules, delay discounting, hyperbolic discounting, decision-making
- MeSH terms
- Animals, Choice Behavior, Color Perception, Conditioning, Operant, Decision Theory, Discrimination Learning, Eye Movements, Fixation, Ocular, Macaca mulatta, Male, Models, Neurological, Orientation, Pattern Recognition, Visual, Probability Learning, Reaction Time, Reinforcement Schedule, Reward, Poisson Distribution, Linear Models, Nonlinear Dynamics
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