Reward associations do not explain transitive inference performance in monkeys
- Published
- Accepted
- Subject Areas
- Animal Behavior, Psychiatry and Psychology
- Keywords
- Transitive inference, Expected value, Reinforcement learning, Rhesus Macaques
- Copyright
- © 2018 Jensen et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Reward associations do not explain transitive inference performance in monkeys. PeerJ Preprints 6:e26889v3 https://doi.org/10.7287/peerj.preprints.26889v3
Abstract
The observation that monkeys appear to make transitive inferences has been taken as evidence of their ability to form and manipulate mental representations. However, alternative explanations have been proposed arguing that transitive inference performance based on expected or experienced reward value. To test the contribution of reward value to monkeys’ behavior in TI paradigms, we performed two experiments in which we manipulated the amount of reward associated with each item in an ordered list. In these experiments, monkeys were presented with pairs of items drawn from the list, and delivered rewards if subjects selected the item with the earlier list rank. When reward magnitude was biased to favor later list items, correct responding was reduced. However, monkeys eventually learned to make correct rule-based choices despite countervailing incentives. The results demonstrate that monkeys’ performance in TI paradigms is not driven solely by expected reward, but that they are able to make appropriate inferences in the face of discordant reward associations.
Author Comment
Addition of six new cycles of training to Experiment 2, a Bayesian model of stimulus position, and a direct comparison to model-free Q-learning algorithms.
Supplemental Information
Data & Analysis Script
Event counts for Experiments 1 and 2, as well as the R script that analyzes the data using Stan.