Cuzzolin, Fabio

Cîrstea, Bogdan-Ionut

Langley, Christelle

Sahakian, Barbara Jacquelyn

Oxford Brookes University: RADAR

EDITORIALpublished: 12 May 2022doi: 10.3389/frai.2022.917565Frontiers in Artificial Intelligence | www.frontiersin.org 1 May 2022 | Volume 5 | Article 917565Edited and reviewed by:Julita Vassileva,University of Saskatchewan, Canada*Correspondence:Christelle Langleycl798@medschl.cam.ac.ukSpecialty section:This article was submitted toAI for Human Learning and BehaviorChange,a section of the journalFrontiers in Artificial IntelligenceReceived: 11 April 2022Accepted: 20 April 2022Published: 12 May 2022Citation:Langley C, Cîrstea B-I, Cuzzolin F andSahakian BJ (2022) Editorial: Theoryof Mind in Humans and in Machines.Front. Artif. Intell. 5:917565.doi: 10.3389/frai.2022.917565Editorial: Theory of Mind in Humansand in MachinesChristelle Langley 1*, Bogdan-Ionuţ Cîrstea 2, Fabio Cuzzolin 2 andBarbara Jacquelyn Sahakian 11Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom, 2 School ofEngineering, Computing and Mathematics, Oxford Brookes University, Oxford, United KingdomKeywords: human theory of mind, machine theory of mind, human-robot interaction, cognitive science andneuroscience, artificial intelligenceEditorial on the Research TopicTheory of Mind in Humans and in MachinesHumans can think flexibly and creatively, whereas current artificial intelligence (AI) systems mayfail to recognize instances where they would need to change the solution approach, when thecurrent approach is unsuccessful. For a long time, AI systems used to be incapable of matchinghuman capabilities for the vast majority of tasks. However, this has recently started to change.For example, AlphaZero is able to generate new solutions in chess and successfully outperformeven Chess Grandmasters (Sadler and Regan, 2019). Similarly, initially robotics were bulky andunrefined and unable to perform even basic human actions, such as picking up a glass. Morerecently there has been significant progress. The Shadow Robot Company, together with theHuman Brain Project, have now created the world’s first haptic telerobot hand that has very similarmovements to those of humans (Sahakian et al., 2021). Similarly to computers, which used to takeup a lot of space and were cumbersome to use but are now in our mobile phones and usableby anyone, AI systems are likely to become much more widely used and more impactful on asocietal level. For example, some of the more recent AI breakthroughs could, in the long-run,have significant impacts in fields like drug design (e.g., AlphaFold; Jumper et al., 2021), softwareengineering (e.g., Codex; Chen et al., 2021) or graphic design, through image generation (e.g.,DALL-E 2; Ramesh et al., 2022). Even in the near future, we can expect at least some types of AIsystems (e.g., autonomous cars) to become commonplace in society. The expected wider societalimpacts of AI systems makes it even more important to ensure that they will be used to promotewellbeing in individuals, enhance innovation and to benefit society. Therefore, it is important toconsider ethical issues associated with human-AI interactions, including components of socialcognition, such as empathy and Theory of Mind (ToM) and their integration into AI systems(Cuzzolin et al., 2020).ToM is the ability of the humanmind to attribute mental states to others and is a key componentof human cognition. ToM encompasses inferring others’ beliefs, desires, goals, and preferences. Thesame capability of inferring humanmental states is a prerequisite for AI to be integrated into humansociety. Aspects related to ToM have been an area of interest in User Modeling, Student / LearnerModeling, User-Adaptive Systems Design, and Personalization. All of these related areas have beenusing AI techniques for determining users’ goals and interests to adapt interactive software systemsto their human users’ needs.Langley et al. Editorial: Theory of Mind in Humans and in MachinesThis Research Topic aimed to span across the fields ofartificial intelligence, cognitive science, and neuroscience withthe intention to formulate computational proposals of cognitivescience and neuroscience-inspired ToM. The intention was toallow for the comparison of the strengths and limitations of ToM,Inverse Reinforcement Learning, and other reward specificationmethods and to establish common baselines, metrics, andbenchmarks, and to identify open questions.Each of the following five articles made a novel andcomplementary contribution to this Research Topic.Schellen et al. investigated deception in human-robotinteraction, focusing on the effect that eye contact with a robothas on honesty toward a robot. Their results showed thathumans are more honest after a robot establishes eye contactwith them. However, this is only true in response to deceptive,but not honest behavior. These results suggest that robots canbe perceived and treated as social agents in a similar way tohuman-human interactions.Nakahashi and Yamada showed that, in a human-AIcollaborative setting, the AI providing implicit guidance to thehuman can create a balance between improving the human’splans and maintaining the human’s autonomy in a way thatpreserves their autonomy better than explicit guidance. Theimplicit guidance provided is based on the ToM capability thathumans can infer others’ intentions based on their behavior.Gros proposed that emotions, rather than being a leftover ofa more primitive heritage, serve as a mechanism for attributingvalues to behavioral options. He introduced a framework inwhich an agent’s timeline of experienced emotions is comparedwith the agent’s “character”, defined as a preferred distributionover emotional states. In this framework, the agent’s long-term goal is to choose individual tasks so that their emotionalexperience is aligned with their character.Williams et al. discussed how ToM in AI is crucial for anagent’s ability to interact with human team members. Theysuggested that, for an artificial agent to effectively engage inhuman-robot interactions, it must possess elements of ToM toperceive, interpret, and generate combinations of social cues.Interestingly, the authors suggested that an interdisciplinaryapproach is required to achieve such a model.Langley et al. provided a review which aimed to synthesize thecurrent knowledge about human ToM, from cognitive scienceand neuroscience, and machine ToM, from AI. They suggestedthat as yet AI (unlike human ToM) has not provided a trulyholistic approach to ToM, and has rather focused on separatecomponents. The authors stated that they intend to stimulatediscussion to better integrate the fields of cognitive neuroscienceand AI, particularly with regards to integrating ToM inmachines.Predictions about the future are inherently uncertain, so it ishard to make very confident statements about what AI systemswill ultimately be capable of or what kind of AI approachesmight help bridge the gap to human capabilities. Despite this,we think it is likely that a better integration of ToM findingsfrom cognitive neuroscience and AI will be useful, even if itwere only to help improve Human-AI interaction. If we wantAI systems to be capable of inferring and motivated to respecthuman preferences, how humans do ToM seems an obviousplace to look for ideas. We think the stakes are likely to onlyget higher.AUTHOR CONTRIBUTIONSCL and BC contributed equally to the manuscript. FC and BJScontributed equally to the manuscript. All authors listed havemade a substantial, direct, and intellectual contribution to thework and approved it for publication.FUNDINGThis work was funded by a Leverhulme Trust grant to FC and BJSunder the Research Programme Grant (RPG-2019-243). CL andBC were funded by the Leverhulme Trust (RPG-2019-243).REFERENCESChen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. O., Kaplan, J.,et al. (2021). Evaluating large language models trained on code. [preprint].doi: 10.48550/arXiv.2107.03374Cuzzolin, F., Morelli, A., Cirstea, B., and Sahakian, B. J. (2020). Knowingme, knowing you: theory of mind in AI. Psychol. Med. 50, 1057–1061.doi: 10.1017/S0033291720000835Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al.(2021). Highly accurate protein structure prediction with AlphaFold. Nature596, 583–589. doi: 10.1038/s41586-021-03819-2Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022). HierarchicalText-Conditional Image Generation with CLIP Latents. Available onlineat: https://cdn.openai.com/papers/dall-e-2.pdf (accessed April 8, 2022).Sadler, M., and Regan, N. (2019). Game Changer. AlphaZero’s GroundbreakingChess Strategies and the Promise of AI. Alkmaar: New in Chess.Sahakian, BJ., Langley, C., and Amunts, K. (2021). The Human Brain Project: SixAchievements of Europe’s Largest Neuroscience Programme. The Conversation.Available online at: https://theconversation.com/the-human-brain-project-six-achievements-of-europes-largest-neuroscience-programme-169184(accessed October 11, 2021).Conflict of Interest: FC is the Director of Oxford AI Consulting, and doesconsultancy for Huawei Canada. BJS consults for Cambridge Cognition.The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potentialconflict of interest.Publisher’s Note: All claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations, or those ofthe publisher, the editors and the reviewers. Any product that may be evaluated inthis article, or claim that may be made by its manufacturer, is not guaranteed orendorsed by the publisher.Copyright © 2022 Langley, Cîrstea, Cuzzolin and Sahakian. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) and the copyright owner(s) are credited and that the originalpublication in this journal is cited, in accordance with accepted academic practice.No use, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Artificial Intelligence | www.frontiersin.org 2 May 2022 | Volume 5 | Article 917565

Theory of mind in humans and in machines

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Theory of mind in humans and in machines

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