Assessment of Human-Likeness and Anthropomorphism of Robots: A Literature Review

Rothstein, Nina; Kounios, John; Ayaz, Hasan; Visser, Ewart de

doi:10.1007/978-3-030-51041-1_26

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Significant R&D investment continues to fuel the development of socially interactive robots with whom human users can intuitively and effectively collaborate, which often attempt to capture as much human-likeness as possible while also avoiding the uncanny valley. 64 , 65 , 66 , 67 , 68 , 69 However, the extent to which an agent is perceived as “like-me” extends beyond physical form, capabilities, and movement, and growing evidence supports that prior knowledge about and the perceived socialness of a robot may more strongly influence their reception (and people’s ability to collaborate or cooperate with them in an intuitive manner) in social settings. 41 , 43 , 70 , 71 , 72 , 73 , 74 , 75 …”

Section: Discussionmentioning

confidence: 99%

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

Jastrzab,

Chaudhury,

Ashley

et al. 2024

iScience

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Section: Discussionmentioning

confidence: 99%

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

Jastrzab,

Chaudhury,

Ashley

et al. 2024

iScience

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“…In our case, it is an ethical and social question of whether it poses a danger or what feelings it may evoke if an employee's voice is used as a model for the operation of the system. The uncanny valley phenomenon [76] can be a social dilemma when we send messages through our tested system as a real-time warning in direct response to a given situation.…”

Section: Discussionmentioning

confidence: 99%

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

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Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model.

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“…Significant R&D investment continues to fuel the development of socially interactive robots with whom human users can intuitively and effectively collaborate, which often attempt to capture as much human-likeness as possible while also avoiding the uncanny valley. [62][63][64][65][66][67] However, the extent to which an agent is perceived as "like-me" extends beyond physical form, capabilities, and movement, and growing evidence supports that prior knowledge about and the perceived socialness of a robot may more strongly influence their reception (and people's ability to collaborate or cooperate with them in an intuitive manner) in social settings. 41,44,[68][69][70][71][72][73] A few neuroimaging studies have investigated how these top-down knowledge cues and bottom-up stimulus cues influence perceptions of animacy and the flexibility of our social cognitive system.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

Jastrzab,

Chaudhury,

Ashley

et al. 2023

Preprint

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SummaryWe sought to replicate and expand previous work showing that the more human-like a robot appears, the more willing people are to attribute mind-like capabilities and socially engage with it. Forty-two participants played games against a human, a humanoid robot, a mechanoid robot, and a computer algorithm while undergoing functional neuroimaging. Replicating previous studies, we confirmed that the more human-like the agent, the more participants attributed a mind to them. However, exploratory analyses revealed that beyond humanness, the perceivedsocialnessof an agent appeared to be as important, if not more so, for mind attribution. Our findings suggest that top-down knowledge cues are just as important, if not more so, than bottom-up stimulus cues when exploring mind attribution in non-human agents. While further work is now required to test this hypothesis directly, these preliminary findings hold important implications for robotic design and to understand and test the flexibility of human social cognition when people engage with artificial agents.

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Assessment of Human-Likeness and Anthropomorphism of Robots: A Literature Review

Cited by 9 publications

References 24 publications

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

Beyond human-likeness: Socialness is more influential when attributing mental states to robots

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