Data from: Predictions about reward outcomes in rhesus monkeys
Data files
Oct 05, 2023 version files 62.15 KB
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DATA_Huang_et_al_Rhesus_Prediction_Study_1.xlsx
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DATA_Huang_et_al_Rhesus_Prediction_Study_2.xlsx
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README.md
Abstract
Human infants and nonhuman animals respond to surprising events by looking longer at unexpected-than-expected situations. These looking responses provide core cognitive evidence that nonverbal minds make predictions about possible outcomes and detect when these predictions fail to match reality. We propose that this phenomenon has crucial parallels with the processes of reward prediction error, indexing the difference between expected and actual reward outcomes. Most work on reward prediction errors to date involves neurobiological techniques that cannot be implemented in many relevant populations, so we developed a novel behavioral task to assess monkeys’ predictions about reward outcomes using looking time responses. In Study 1, we tested how semi-free-ranging monkeys (n = 210) responded to positive error (more rewards than expected), negative error (less rewards than expected), and number control. We found that monkeys looked longer at a given reward when it was unexpectedly large or small, compared to when the same quantity was expected. In Study 2, we compared responses in the positive error condition in monkeys ranging from infancy to old age (n = 363), to assess lifespan changes in sensitivity to reward predictions. We found that adolescent monkeys showed heightened responses to unexpected rewards, similar to patterns seen in humans, but showed no changes during aging. These results suggest that monkeys’ looking responses can be used to track their predictions about rewards and that monkeys share some developmental signatures of reward sensitivity with humans, providing a new approach to accessing cognitive processes underlying reward-based decision-making.
README: READ ME
Data and analysis scripts from: Predictions about reward outcomes in rhesus monkeys
Study Authors: Y. Huang, H. Chang, L.R. Santos, & A.G. Rosati
Contact: rosati@umich.edu
This data set consists of two data files (.xlsx format) for the two studies reported in this work. For each task, there is a key tab in the file defining each variation reported in the main data tab. The files are:
- DATA Huang et al Rhesus Prediction Study 1
- DATA Huang et al Rhesus Prediction Study 2
For each data file, there is a corresponding R analysis script. The analysis script files are:
- SCRIPT Huang et al Rhesus Prediction Study 1
- SCRIPT Huang et al Rhesus Prediction Study 2
Methods
See methods in the manuscript for all details.