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Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley

OPEN The Journal of neuroscience : the official journal of the Society for Neuroscience | 3 Jul 2019

AM Rosenthal-von der Pütten, NC Krämer, S Maderwald, M Brand and F Grabenhorst
Abstract
Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The Uncanny-Valley (UV) hypothesis predicts that humans prefer anthropomorphic agents but reject them if they become too human-like-the so-called UV reaction. Using functional MRI, we investigated neural activity when subjects evaluated artificial agents and made decisions about them. Across two experimental tasks, the ventromedial prefrontal cortex (VMPFC) encoded an explicit representation of subjects' UV reactions. Specifically, VMPFC signaled the subjective likability of artificial agents as a nonlinear function of human-likeness, with selective low likability for highly humanlike agents. In exploratory across-subject analyses, these effects explained individual differences in psychophysical evaluations and preference choices. Functionally connected areas encoded critical inputs for these signals: the temporo-parietal junction encoded a linear human-likeness continuum, whereas nonlinear representations of human-likeness in dorsomedial prefrontal cortex (DMPFC) and fusiform gyrus emphasized a human-nonhuman distinction. Following principles of multisensory integration, multiplicative combination of these signals reconstructed VMPFC’s valuation function. During decision-making, separate signals in VMPFC and DMPFC encoded subjects' decision variable for choices involving humans or artificial agents, respectively. A distinct amygdala signal predicted rejection of artificial agents. Our data suggest that human reactions toward artificial agents are governed by a neural mechanism that generates a selective, nonlinear valuation in response to a specific feature combination (human-likeness in nonhuman agents). Thus, a basic principle known from sensory coding-neural feature selectivity from linear-nonlinear transformation-may also underlie human responses to artificial social partners.SIGNIFICANCE STATEMENTWould you trust a robot to make decisions for you? Autonomous artificial agents are increasingly entering our lives but how the human brain responds to these new, artificial social partners remains unclear. The Uncanny Valley hypothesis-an influential psychological framework-captures the observation that human responses to artificial agents are nonlinear: we like increasingly anthropomorphic artificial agents, but feel uncomfortable if they become too human-like. Here we investigated neural activity when humans evaluated artificial agents and made personal decisions about them. Our findings suggest a novel neurobiological conceptualization of human responses toward artificial agents: The Uncanny Valley reaction-a selective dislike of highly human-like agents-is based on nonlinear value-coding in VMPFC, a key component of the brain’s reward system.
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