Human but not robotic gaze facilitates action prediction

Tidoni, Emmanuele; Holle, Henning; Scandola, Michele; Schindler, Igor; Hill, Loron; Cross, Emily S.

doi:10.1016/j.isci.2022.104462

Cited by 5 publications

(11 citation statements)

References 100 publications

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“…We replicated the findings from Experiment 1 with faster RTs when participants attribute intentions to humans rather than the triangle and robots. Moreover, these results expand previous reports where online participants were asked to infer the agents' intentions, and the graspable objects and text bubble were in a fixed position (Experiment 4 in [18]). The findings from Experiment 3 also enable us to exclude the possibility that the results in Experiment 2 were driven by a difference between laboratory and online samples.…”

Section: Discussion Experimentssupporting

confidence: 87%

“…Experiments 4, 5, and 6 were not pre-registered as we tried to replicate results obtained in Experiment 2 with a smaller sample size and slightly different task instructions. We adopted the same statistical approach of our previous 25.44 ± 0.75, Descriptive measures of the sample for each experiment work [18] to facilitate comparison across all experiments and studies.…”

Section: General Methodologymentioning

confidence: 99%

“…We recently have suggested that detecting where a humanoid robot is gazing may rely on visually processing the direction of the observed gaze (e.g., right) rather than engaging the observer's the motor system responsible for anticipating other's actions [18]. Furthermore, although participants were slower in attributing mental states to humanoid robots than to humans, we showed that the human-like appearance of non-human agents may engage processes responsible for ascribing intentions to others.…”

Section: Introductionmentioning

confidence: 99%

“…If the physical form of the agent is not relevant for attributing intentions to others, then changing contextual information (i.e., scenarios and task instructions) should not affect participants' ability to interpret non-human gaze behaviour. Hence, we should expect people to always be faster in attributing intentions to a human than a robot actor [18]. In contrast, if the physical form of the agent does indeed interact with contextual information, we should expect people being equally fast to attribute an intention to robots and humans in a new scenario, and an integrative account taking into consideration how an agent's bodily form may support human ability to infer others mental states through gaze observation should be considered.…”

Section: Introductionmentioning

confidence: 99%

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Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation

Scandola

Cross

Caruana

et al. 2023

Int J of Soc Robotics

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The future of human–robot collaboration relies on people’s ability to understand and predict robots' actions. The machine-like appearance of robots, as well as contextual information, may influence people’s ability to anticipate the behaviour of robots. We conducted six separate experiments to investigate how spatial cues and task instructions modulate people’s ability to understand what a robot is doing. Participants observed goal-directed and non-goal directed gaze shifts made by human and robot agents, as well as directional cues displayed by a triangle. We report that biasing an observer's attention, by showing just one object an agent can interact with, can improve people’s ability to understand what humanoid robots will do. Crucially, this cue had no impact on people’s ability to predict the upcoming behaviour of the triangle. Moreover, task instructions that focus on the visual and motor consequences of the observed gaze were found to influence mentalising abilities. We suggest that the human-like shape of an agent and its physical capabilities facilitate the prediction of an upcoming action. The reported findings expand current models of gaze perception and may have important implications for human–human and human–robot collaboration.

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Section: Discussion Experimentssupporting

confidence: 87%

Section: General Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation

Scandola

Cross

Caruana

et al. 2023

Int J of Soc Robotics

Self Cite

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“…This understanding would allow the robot partner to control its speed, trajectory, and action planning. In HRCom, similar to human communication, multiple input channels called modes exist such as gaze ( Tidoni et al, 2022 ), hand gestures ( Liu and Wang 2018 ), natural language interfaces ( Fogli et al, 2022 ), voice commands ( Bingol and Aydogmus 2020 ), and facial expressions ( Spezialetti et al, 2020 ; Chiurco et al, 2022 ) and much research has been carried out to detect and classify them. These multiple modes theoretically lend redundancy to the systems but are advantageous for industrial settings that are full of noises and disturbances.…”

Section: Recent Advances In Industrial Human-robot Communicationmentioning

confidence: 99%

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

2022

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Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.

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The Ethics of Terminology: Can We Use Human Terms to Describe AI?

Deroy¹

2023

Topoi

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Despite facing significant criticism for assigning human-like characteristics to artificial intelligence, phrases like “trustworthy AI” are still commonly used in official documents and ethical guidelines. It is essential to consider why institutions continue to use these phrases, even though they are controversial. This article critically evaluates various reasons for using these terms, including ontological, legal, communicative, and psychological arguments. All these justifications share the common feature of trying to justify the official use of terms like “trustworthy AI” by appealing to the need to reflect pre-existing facts, be it the ontological status, ways of representing AI or legal categories. The article challenges the justifications for these linguistic practices observed in the field of AI ethics and AI science communication. In particular, it takes aim at two main arguments. The first is the notion that ethical discourse can move forward without the need for philosophical clarification, bypassing existing debates. The second justification argues that it’s acceptable to use anthropomorphic terms because they are consistent with the common concepts of AI held by non-experts—exaggerating this time the existing evidence and ignoring the possibility that folk beliefs about AI are not consistent and come closer to semi-propositional beliefs. The article sounds a strong warning against the use of human-centric language when discussing AI, both in terms of principle and the potential consequences. It argues that the use of such terminology risks shaping public opinion in ways that could have negative outcomes.

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Human but not robotic gaze facilitates action prediction

Cited by 5 publications

References 100 publications

Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation

Body Form Modulates the Prediction of Human and Artificial Behaviour from Gaze Observation

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

The Ethics of Terminology: Can We Use Human Terms to Describe AI?

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