Modeling sensory-motor decisions in natural behavior

• Published in: PLoS computational biology Vol. 14; no. 10; p. e1006518
• Main Authors: Zhang, Ruohan, Zhang, Shun, Tong, Matthew H, Cui, Yuchen, Rothkopf, Constantin A, Ballard, Dana H, Hayhoe, Mary M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2018; Public Library of Science (PLoS)
• Subjects: Agents (artificial intelligence); Algorithms; Artificial intelligence; Biology and Life Sciences; Cognitive ability; Computational Biology; Computer and Information Sciences; Computer applications; Decision making; Decision Making - physiology; Decisions; Human behavior; Humans; Learning; Machine learning; Models, Biological; Navigation; Neurosciences; Physical Sciences; Psychomotor Performance - physiology; Reinforcement; Reinforcement (Psychology); Research and Analysis Methods; Reward; Social Sciences; Software; Task complexity; Virtual reality; United States > US
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006518

Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.