abstract

Computational Model of the User's Learning Process When Cued by a Social Versus Non-Social Agent

Authors:

Mehdi KhamassiAuthors Info & Claims

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

Pages 347 - 349

https://doi.org/10.1145/3284432.3287182

Published: 04 December 2018 Publication History

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Abstract

There are ongoing debates on whether learning involves the same mechanisms when it is mediated by social skills than when it is not [1]. Gaze cues serve as a strong communicative modality that is profoundly human. They have been shown to trigger automatic attentional orienting [2]. However, arrow cues have been shown to elicit similar effects [3]. Hence, gaze and arrow cues are often compared to investigate differences between social and non-social cognitive processes [4]. The present study sought to compare cued learning when the cue is provided by a social agent versus a nonsocial agent.

References

[1]

T. E. J. Behrens, L. T. Hunt, and M. F. S. Rushworth, "The Computation of Social Behavior," Science, vol. 324, no. 5931, pp. 1160--1164, May 2009.

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[2]

C. K. Friesen and A. Kingstone, "The eyes have it! Reflexive orienting is triggered by nonpredictive gaze," Psychonomic Bulletin & Review, vol. 5, no. 3, pp. 490--495, Sep. 1998.

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[3]

J. Tipples, "Eye gaze is not unique: Automatic orienting in response to uninformative arrows," Psychonomic Bulletin & Review, vol. 9, no. 2, pp. 314--318, Jun. 2002.

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[4]

A. Marotta, J. Lupiáñez, D. Martella, and M. Casagrande, "Eye gaze versus arrows as spatial cues: Two qualitatively different modes of attentional selection," Journal of Experimental Psychology: Human Perception and Performance, vol. 38, no. 2, pp. 326--335, 2012.

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O. Landry and S. Al-Taie, "A Meta-analysis of the Wisconsin Card Sort Task in Autism," J Autism Dev Disord, vol. 46, no. 4, pp. 1220--1235, Apr. 2016.

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Index Terms

Computational Model of the User's Learning Process When Cued by a Social Versus Non-Social Agent
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
        Sequential decision making
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Information & Contributors

Information

Published In

HAI '18: Proceedings of the 6th International Conference on Human-Agent Interaction

December 2018

402 pages

ISBN:9781450359535

DOI:10.1145/3284432

General Chairs:
Michita Imai
Keio University, Japan
,
Tim Norman
University of Southampton, UK
,
Program Chairs:
Elizabeth Sklar
King's College London, UK
,
Takanori Komatsu
Meiji University, Japan

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2018

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Centre National de la Recherche Scientifique
Sorbonne Université

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HAI '18

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SIGCHI

HAI '18: 6th International Conference on Human-Agent Interaction

December 15 - 18, 2018

Southampton, United Kingdom

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HAI '18 Paper Acceptance Rate 40 of 92 submissions, 43%;

Overall Acceptance Rate 121 of 404 submissions, 30%

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