October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Intention beyond Desire: Commitment in Human Action
Author Affiliations
  • Shaozhe Cheng
    Zhejiang University
  • Ning Tang
    UCLA
  • Wei An
    Zhejiang University
  • Yang Zhao
    Zhejiang University
  • Jifan Zhou
    Zhejiang University
  • Mowei Shen
    Zhejiang University
  • Tao Gao
    UCLA
Journal of Vision October 2020, Vol.20, 1723. doi:https://doi.org/10.1167/jov.20.11.1723
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      Shaozhe Cheng, Ning Tang, Wei An, Yang Zhao, Jifan Zhou, Mowei Shen, Tao Gao; Intention beyond Desire: Commitment in Human Action. Journal of Vision 2020;20(11):1723. doi: https://doi.org/10.1167/jov.20.11.1723.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Recent success of artificial intelligence is largely based on reinforcement learning (RL), in which an agent acts to maximize expected rewards. RL has deep roots in Psychology for modeling animal behavior and captures the fact that human actions are driven by desires. Nevertheless, it misses one mental representation highlighted by more recent cognitive Theory-of-Mind (ToM) models: Intention. Unlike desires, intentions form stable, partial plans of action concerning the future, demand “commitment” (Bratman, 1987). While having conflicting desires is part of human nature (e.g. losing weight and enjoying food), intentions should always be coherent, stable and admissible. Here we tested predictions of RL and ToM in a visual navigation task involving conflicting desires. A human or RL model controls an agent to reach one of two equally desirable restaurants in a 2D map. With a low-probability, random action noises can cause the agent to drift slightly. To test “commitment to an intention”, we created a special trial: once the agent clearly moves towards one restaurant, noises will push the agent away so that the alternative restaurant becomes a better “rational” choice. As predicted, the RL agent showed no commitment, with close to 0% still pursuing the original restaurant. In contrast, in 70% of trials, humans fought the noise and pursued the original restaurant persistently. In addition, humans form a commitment with deliberation. In the same task, we performed online prediction of the agent’s destination through well-established Bayesian ToM. The results demonstrated that while RL quickly displayed a preference, humans avoided showing any preference early on. In conclusion, humans are unlike RL in that they appreciate the gravity of commitment, preferring not to rush forming an intention, but once committed, remain so despite setbacks. These results collectively demonstrate intention is an intrinsic mental representation that can forcefully regulate human actions through commitment.

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