Do You See what I See? Tracking the Perceptual Beliefs of Robots

Purpose of Review We provide an outlook on the definitions, laboratory research, and applications of social robots, with an aim to understand what makes a robot social—in the eyes of science and the general public. Recent Findings Social robots demonstrate their potential when deployed within contexts appropriate to their form and functions. Some examples include companions for the elderly and cognitively impaired individuals, robots within educational settings, and as tools to support cognitive and behavioural change interventions. Summary Science fiction has inspired us to conceive of a future with autonomous robots helping with every aspect of our daily lives, although the robots we are familiar with through film and literature remain a vision of the distant future. While there are still miles to go before robots become a regular feature within our social spaces, rapid progress in social robotics research, aided by the social sciences, is helping to move us closer to this reality.

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“…[ 42 – 45 ]), intention attribution (e.g. [ 40 , 46 , 47 ]), and decision making (e.g. [ 48 , 49 ]).…”

Section: What Can Social Robotics Learn From the Social Behaviouralmentioning

confidence: 99%

What Makes a Robot Social? A Review of Social Robots from Science Fiction to a Home or Hospital Near You

2021

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“…As an alternative to using verbal measures to study belief attribution, our own approach to investigating how people deal with the perceptual belief problem in human-robot interactions uses non-verbal false-belief tasks [71]. False-belief tasks [3,23,79] are predicated on a basic insight: Some beliefs have highly specific and predictable behavioral consequences in particular situations.…”

Section: Understanding the Problemmentioning

confidence: 99%

“…In recent empirical work [71] our goal is to study people's assumptions about the perceptual and cognitive capabilities of robots by observing their attributions of perceptual beliefs, including when and why such attributions succeed or fail. This can be accomplished by modifying the standard false-belief task in such a way that subjects have to predict the behavior a robot based on what they think it can or cannot perceive in the environment.…”

Section: Understanding the Problemmentioning

confidence: 99%

“…This can be accomplished by modifying the standard false-belief task in such a way that subjects have to predict the behavior a robot based on what they think it can or cannot perceive in the environment. In a recent study [71], we used a non-verbal anticipatory-looking paradigm, which helps to circumvent the methodological issues discussed above. The experiment involved three types of false-belief task scenarios, in which the behavior of observed humans is easy to predict, because subjects understand what other people can and cannot see or hear, but much harder to predict in the case of a robot, whose visual and auditory capacities subjects know very little about.…”

Section: Understanding the Problemmentioning

confidence: 99%

“…The experiment involved three types of false-belief task scenarios, in which the behavior of observed humans is easy to predict, because subjects understand what other people can and cannot see or hear, but much harder to predict in the case of a robot, whose visual and auditory capacities subjects know very little about. The experimental results (see Thellman & Ziemke [71] for details) indicate three types of circumstances in which people's assumptions about the perceptual capabilities of robots are inadequate. More specifically, our results indicate that one of the reasons why people form incorrect mental models of the perceptual and cognitive capabilities of robots is that they-unsurprisingly-assume these capabilities to be more or less humanlike until they are presented with evidence to the contrary, i.e., they default to an "anthropocentric bias" (cf.…”

Section: Understanding the Problemmentioning

confidence: 99%

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The Perceptual Belief Problem

Thellman

Ziemke

2021

J. Hum.-Robot Interact.

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The explainability of robotic systems depends on people’s ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human–robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots’ perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem.

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Understanding Sophia? On human interaction with artificial agents

Fuchs

2022

Phenom Cogn Sci

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Advances in artificial intelligence (AI) create an increasing similarity between the performance of AI systems or AI-based robots and human communication. They raise the questions: whether it is possible to communicate with, understand, and even empathically perceive artificial agents; whether we should ascribe actual subjectivity and thus quasi-personal status to them beyond a certain level of simulation; what will be the impact of an increasing dissolution of the distinction between simulated and real encounters. (1) To answer these questions, the paper argues that the precondition for actually understanding others consists in the implicit assumption of the subjectivity of our counterpart, which makes shared feelings and a „we-intentionality” possible. This assumption is ultimately based on the presupposition of a shared form of life, conceived here as „conviviality.”(2) The possibility that future artificial agents could meet these preconditions is refuted on the basis of embodied and enactive cognition, which links subjectivity and consciousness to the aliveness of an organism.(3) Even if subjectivity is in principle impossible for artificial agents, the distinction between simulated and real subjectivity might nevertheless become increasingly blurred. Here, possible consequences are discussed, especially using the example of virtual psychotherapy. Finally, the paper makes case for a mindful appproach to the language we use to talk about artificial systems and pleads for preventing a systematic pretense of subjectivity.

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Do You See what I See? Tracking the Perceptual Beliefs of Robots

Cited by 17 publications

References 24 publications

What Makes a Robot Social? A Review of Social Robots from Science Fiction to a Home or Hospital Near You

What Makes a Robot Social? A Review of Social Robots from Science Fiction to a Home or Hospital Near You

The Perceptual Belief Problem

Understanding Sophia? On human interaction with artificial agents

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