What is Human-like?

Phillips, Elizabeth; Zhao, Xuan; Ullman, Daniel; Malle, Bertram F.

doi:10.1145/3171221.3171268

Cited by 166 publications

(77 citation statements)

References 32 publications

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“…While previous morph studies have generalized to real-life interactions ( Kompatsiari et al, 2018 ), it is important to acknowledge that this may not be the case with all paradigms. Therefore, future studies should examine real robot face stimuli that vary in physical human likeness (see the ABOT database for robot faces; Phillips et al, 2018 ). Another possible limitation is the independence of samples across participants.…”

Section: Discussionmentioning

confidence: 99%

Pre-exposure to Ambiguous Faces Modulates Top-Down Control of Attentional Orienting to Counterpredictive Gaze Cues

2020

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Section: Discussionmentioning

confidence: 99%

Pre-exposure to Ambiguous Faces Modulates Top-Down Control of Attentional Orienting to Counterpredictive Gaze Cues

2020

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“…Another thrust in this direction is already underway, with the recently added "reproducability" track in a leading HRI technical conference. Yet another avenue would be to create our own research tools to facilitate rigorous research practices in the HRI field [42], which may range from creating validated psychological instruments (e.g., the Robotic Social Attributes Scale (RoSAS), [12]) to creating community resources that enables systematic comparisons across different robots (e.g., the Anthropomorphic roBOT (ABOT) Database, [65]). Finally, we should also conduct more meta-analysis studies on key topics in HRI.…”

Section: Concluding Commentsmentioning

confidence: 99%

A Primer for Conducting Experiments in Human–Robot Interaction

Hoffman

Zhao

2020

J. Hum.-Robot Interact.

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We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human-Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results. CCS Concepts: • Human-centered computing → Empirical studies in HCI; • Computer systems organization → Embedded systems; Robotics;

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“…When Arcimboldo originally painted his whimsical portraits in the late 16 th century, little did he know that machines today would be endowed with facial features to evoke illusory socialness -a simple, yet effective trick, corroborated by data that show that mechanical and screen-based robot faces are rated as humanlike, friendly, intelligent or in some cases, as uncanny (Chesher & Andreallo, 2020;Kalegina et al, 2018;Phillips et al, 2018;Vallverdú & Trovato, 2016). As our surroundings become increasingly populated by a variety of artificial agents (including robots and virtual agents), an important aim will be to probe how different types of faces are processed, and what we might learn about humans' intrinsic social motivation toward artificial agents' faces (Geiger & Balas, 2020).…”

Section: Human Faces Do Not Inadvertently Capture Attentionmentioning

confidence: 99%

“…This is in line with a study by Omer and colleagues, which mapped the features that contributed to the global gestalt of pareidolia faces, identifying the eyes and the mouth (2019). Robots' facial cues are viewed as one of the crucial four dimensions in driving human-likeness ratings, and in a survey of humanoid robots, 87.5% had at least some facial features (DiSalvo et al, 2002;Phillips, Zhao, Ullman, & Malle, 2018). It is of note that when establishing an impression of animacy, viewing the face as a whole is crucial, with participants being more hesitant to make judgements about the presence of mind in an agent when viewing cropped facial cues in isolation (Looser & Wheatley, 2010).…”

Section: Introductionmentioning

confidence: 99%

Faces do not attract more attention than non-social distractors in the Stroop task

Henschel¹,

Bargel²,

Cross³

2020

Preprint

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As robots begin to receive citizenship, be treated as beloved pets, and are given a place at Japanese family tables, it is becoming clear that these machines are taking on increasingly social roles. While human robot interaction research relies heavily on self-report measures for assessing people’s perception of robots, a distinct lack of robust cognitive and behavioural measures to gage the scope and limits of social motivation towards artificial agents exists. Here we adapted Conty and colleagues’ (2010) social version of the classic Stroop paradigm, in which we showed four kinds of distractor images above incongruent and neutral words: human faces, robot faces, object faces (for example, a cloud with facial features) and flowers (control). We predicted that social stimuli, like human faces, would be extremely salient and draw attention away from the to-be-processed words. A repeated-measures ANOVA indicated that the task worked (the Stroop effect was observed), and a distractor-dependent enhancement of Stroop interference emerged. Planned contrasts indicated that specifically human faces presented above incongruent words significantly slowed participants’ reaction times. To investigate this small effect further, we conducted a second study (N=51) with a larger stimulus set. While the main effect of the incongruent condition slowing down the reaction time of the participants replicated, we did not observe an interaction effect of the social distractors (human faces) drawing more attention than the other distractor types. We question the suitability of this task as a robust measure for social motivation and discuss our findings in the light of the conflicting results of Hietanen and colleagues (2016).

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What is Human-like?

Cited by 166 publications

References 32 publications

Pre-exposure to Ambiguous Faces Modulates Top-Down Control of Attentional Orienting to Counterpredictive Gaze Cues

Pre-exposure to Ambiguous Faces Modulates Top-Down Control of Attentional Orienting to Counterpredictive Gaze Cues

A Primer for Conducting Experiments in Human–Robot Interaction

Faces do not attract more attention than non-social distractors in the Stroop task

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