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Crossing Roads with a Computer-generated Agent: Persistent Effects on Perception–Action Tuning
Journal article   Open access   Peer reviewed

Crossing Roads with a Computer-generated Agent: Persistent Effects on Perception–Action Tuning

Yuanyuan Jiang, Elizabeth E O'Neal, Shiwen Zhou, Jodie M Plumert and Joseph K Kearney
ACM transactions on applied perception, Vol.18(1), pp.1-16
01/31/2021
DOI: 10.1145/3431923
url
https://doi.org/10.1145/3431923View
Published (Version of record) Open Access

Abstract

This study investigated how people coordinate their decisions and actions with a risky or safe computer-generated agent in a humanoid or non-humanoid form and how this experience influences later behavior when acting alone. In Experiment 1, participants first repeatedly crossed continuous traffic in a virtual environment with a humanoid computer-generated agent (Figure 1). Participants were specifically instructed to cross with an agent that was programmed to be either safe (taking only large gaps) or risky (also taking relatively small gaps). Participants then repeatedly crossed the same roadway alone. We found that participants’ experiences with crossing safe vs. risky gaps with an agent persisted in later trials when the participants crossed alone, such that participants accepted tighter gaps if they were previously paired with a risky than a safe agent.  In Experiment 2 (Figure 2), we tested whether experience crossing with a risky or safe non-humanoid object (a floating box) also influenced later behavior when crossing alone. We again found that participants who crossed with the risky object partner took tighter gaps when later crossing alone than those who crossed with the safe object partner. The Discussion focuses on the impact of experiences with virtual agents on perception–action tuning and the potential of using virtual agents for training safe road-crossing behavior.

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