The Audio Implicit Association Test: Human Preferences and Implicit Associations Concerning Machine Voices

Abstract

Auditory human-machine interfaces are becoming ubiquitous. Interactive voice response systems, navigation systems, socially assistive robots, and smart houses are just a few examples of technologies that support auditory interactions. This study uses the implicit association test (IAT) to measure participants’ associative strength between human and machine voices and pleasant or unpleasant attributes. To accomplish this, the IAT needed to be validated using audio stimuli and the associative strength of secondary features of stimuli, that is, features other than their semantic content. Six IAT experiments were conducted to test the ability of the IAT to measure association strengths of the target concepts of audio stimuli and an attribute dimension in addition to target concepts of secondary features and an attribute dimension. Results support the effectiveness of an audio IAT, an IAT for secondary features, and an IAT that combines audio with secondary features. Results also show that participants had a stronger association between human voices and pleasant attributes than machine voices and pleasant attributes.