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Anthropomorphism, or the attribution of human mental states and characteristics to non-human entities, has been widely demonstrated to be cued automatically by certain bottom-up appearance and behavioral features in machines. In this thesis, I argue that the potential for top-down effects to influence anthropomorphism has so far been underexplored. I motivate and then report the results of a new empirical study suggesting that top-down linguistic cues, including anthropomorphic metaphors, personal pronouns, and other grammatical constructions, increase anthropomorphism of a robot. As robots and other machines become more integrated into human society and our daily lives, more thorough understanding of the process of anthropomorphism becomes more critical: the cues that cause it, the human behaviors elicited, the underlying mechanisms in human cognition, and the implications of our influenced thought, talk, and treatment of robots for our social and ethical frameworks. In these regards, as I argue in this thesis and as the results of the new empirical study suggest, the top-down effects matter.
Scherer, Hailey Austine, "Metaphors Matter: Top-Down Effects on Anthropomorphism" (2020). Dartmouth College Undergraduate Theses. 270.