Toward a Unified Theory of Learned Trust in Interpersonal and Human-Machine Interactions
| Autor: | Ion Juvina, Othalia Larue, Celso M. de Melo, Michael G. Collins, Ewart J. de Visser, William G. Kennedy |
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| Rok vydání: | 2019 |
| Předmět: |
Cognitive model
media_common.quotation_subject 05 social sciences Cognition Interpersonal communication Cognitive architecture 050105 experimental psychology Variety (cybernetics) Human-Computer Interaction Artificial Intelligence 0501 psychology and cognitive sciences Human–machine system Psychological resilience Unified field theory Psychology 050107 human factors Cognitive psychology media_common |
| Zdroj: | ACM Transactions on Interactive Intelligent Systems. 9:1-33 |
| ISSN: | 2160-6463 2160-6455 |
| Popis: | A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. The theory is instantiated as a computational cognitive model of learned trust that integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The model relies on a combination of learning mechanisms to explain a variety of phenomena such as trust asymmetry, the higher impact of early trust breaches, the black-hat/white-hat effect, the correlation between trust and cognitive ability, and the higher resilience of interpersonal as compared to human-machine trust. In addition, the model predicts that trust decays in the absence of evidence of trustworthiness or untrustworthiness. The implications of the model for the advancement of the theory on trust are discussed. Specifically, this work suggests two more trust antecedents on the trustor's side: perceived trust necessity and cognitive ability to detect cues of trustworthiness. |
| Databáze: | OpenAIRE |
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