- Title Information
- Title
- Developing a Multi-Dimensional Model and Measure of Human-Robot Trust
- Type of Resource (primo)
- dissertations
- Name:
Personal
- Name Part
- Ullman, Daniel
- Role
- Role Term:
Text
- creator
- Name:
Personal
- Name Part
- Malle, Bertram
- Role
- Role Term:
Text
- Advisor
- Name:
Personal
- Name Part
- FeldmanHall, Oriel
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Tellex, Stefanie
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Department of Cognitive, Linguistic, and Psychological Sciences
- Role
- Role Term:
Text
- sponsor
- Origin Information
- Copyright Date
- 2021
- Physical Description
- Extent
- xvii, 153 p.
- digitalOrigin
- born digital
- Note:
thesis
- Thesis (Ph. D.)--Brown University, 2021
- Genre (aat)
- theses
- Abstract
- Trust, and specifically appropriate trust, is essential to beneficial interaction between agents. Robots are becoming increasingly present in everyday life and offer a multitude of potential benefits to people, from physically assistive robotics technology for people who have experienced a stroke to socially assistive robots designed for children with autism. Just as trust is essential to people interacting with other people, so too is trust essential to people interacting with robots. The purpose of this dissertation work is threefold: (1) To accurately conceptualize and model trust and its constituent components; (2) To design a measurement tool to capture the nuance of trust in an agent, especially for human-robot trust; and (3) To demonstrate the validity of this measurement tool for human-robot interaction. The empirical findings from this work support a two-factor superordinate conception of trust, as well as reveal a more nuanced five-dimensional structure of trust: the Performance Trust factor consists of Reliable and Competent dimensions, and the Moral Trust factor consists of Ethical, Transparent, and Benevolent dimensions. This research resulted in the creation of the Multi-Dimensional Measure of Trust (MDMT), which is a model and measurement tool that captures the identified differentiable dimensions of trust in an agent; the MDMT is publicly available for researcher use. The use of the MDMT was tested in studies that measured changes in trust in an agent resulting from changes in salient evidence about the agent along the theorized dimensions of trust; the MDMT was validated for both robot agents and human agents. This dissertation documents the process underlying this research effort, integrating papers published as part of this effort together with additional detail and new work.
- Subject (fast)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/00866547")
- Topic
- Cognitive science
- Subject
- Topic
- human-robot interaction
- Subject
- Topic
- Human-robot trust
- Subject
- Topic
- Trust
- Language
- Language Term (ISO639-2B)
- English
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20211004