How Human Am I?

Abstract

There is a continuous effort by animation experts to create increasingly realistic and more human-like digital characters. However, as virtual characters become more human they risk evoking a sense of unease in their audience. This sensation, called the Uncanny Valley effect, is widely acknowledged both in the popular media and scientific research but empirical evidence for the hypothesis has remained inconsistent. In this paper, we investigate the neural responses to computer-generated faces in a cognitive neuroscience study. We record brain activity from participants (N = 40) using electroencephalography (EEG) while they watch videos of real humans and computer-generated virtual characters. Our results show distinct differences in neural responses for highly realistic computer-generated faces such as Digital Emily compared with real humans. These differences are unique only to agents that are highly photorealistic, i.e. the 'uncanny' response. Based on these specific neural correlates we train a support vector machine (SVM) to measure the probability of an uncanny response for any given computer-generated character from EEG data. This allows the ordering of animated characters based on their level of 'uncanniness'.

Embracing Deepfakes and AI-generated images in Neuroscience Research

“…Manipulating dynamic stimuli realistically is also important, as face realism (Mustafa et al, 2017;Urgen et al, 2018) and realistic facial motion (Skiba & Vuilleumier, 2020) AI's capacity to generate entirely new static images has also attracted the attention of the public. Recently, text-to-image models like Midjourney (Midjourney Inc., 2022) and DALL-E 2 (Marcus et al, 2022) have advanced to the point where they can produce hyper-realistic images from simple sentences, enabling individuals without coding expertise to create visually impressive content.…”

Embracing Deepfakes and AI-generated images in Neuroscience Research

“…Before deepfakes, manipulation of facial dynamism was only possible using dynamic 3D models. These clearly artificial faces are not ideal, as face realism (Mustafa et al, 2017;Urgen et al, 2018) and realistic facial motion (Skiba & Vuilleumier, 2020) has been shown to elicit distinct neural responses.…”

Embracing deepfakes and AI‐generated images in neuroscience research

“…Before deepfakes, manipulation of facial dynamism was only possible using dynamic 3D models. These clearly artificial faces are not ideal, as face realism (Mustafa et al, 2017; Urgen et al, 2018) and realistic facial motion (Skiba & Vuilleumier, 2020) have been shown to elicit distinct neural responses.…”