When categorization-based stranger avoidance explains the uncanny valley: A comment on MacDorman & Chattopadhyay (2016)

TL;DR

This paper argues that categorization-based stranger avoidance can explain the uncanny valley phenomenon, challenging the recent emphasis on realism inconsistency and offering a broader understanding of eeriness in artificial objects.

Contribution

It reinterprets existing experimental data to support categorization-based stranger avoidance as a key explanation for the uncanny valley, despite recent claims favoring realism inconsistency.

Findings

Categorization-based stranger avoidance remains a viable explanation.

The explanation offers a more inclusive account of eeriness.

It challenges recent evidence favoring realism inconsistency.

Abstract

Artificial objects often subjectively look eerie when their appearance to some extent resembles a human, which is known as the uncanny valley phenomenon. From a cognitive psychology perspective, several explanations of the phenomenon have been put forth, two of which are object categorization and realism inconsistency. Recently, MacDorman and Chattopadhyay (2016) reported experimental data as evidence in support of the latter. In our estimation, however, their results are still consistent with categorization-based stranger avoidance. In this Discussions paper, we try to describe why categorization-based stranger avoidance remains a viable explanation, despite the evidence of MacDorman and Chattopadhyay, and how it offers a more inclusive explanation of the impression of eeriness in the uncanny valley phenomenon.