Monkey plays Pac-Man with compositional strategies and hierarchical decision-making

Abstract
Data availability
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Abstract

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals’ advanced cognition.

Data availability

The data and codes that support the findings of this study are provided at: https://github.com/superr90/Monkey_PacMan.

The following data sets were generated

1. Yang Q
(2022) Hierarchical Decision-Making Analysis for Behavior Data
GitHub.

https://github.com/superr90/Monkey_PacMan

Article and author information

Author details

Qianli Yang

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4226-2319
Zhongqiao Lin

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.
Wenyi Zhang

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.
Jianshu Li

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.
Xiyuan Chen

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

Competing interests
The authors declare that no competing interests exist.
Jiaqi Zhang

Brown University, Providence, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-1649-3378
Tianming Yang

Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China

For correspondence
tyang@ion.ac.cn

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6976-9246

Funding

Chinese Academy of Sciences (XDB32070100)

Tianming Yang

Shanghai Municipal Science and Technology Major Project (2018SHZDZX05)

Tianming Yang

National Natural Science Foundation of China (32100832)

Qianli Yang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All procedures followed the protocol approved by the Animal Care Committee of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CEBSIT-2021004).

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.