Mind Meets Machine: Towards a Cognitive Science of Human–Machine Interactions
Authors: Emily S. Cross, Richard Ramsey
Journal: Trends in Cognitive Sciences (2020)

Although machines designed to socially interact with humans are proliferating, our understanding of the mental processes supporting such interactions remains limited.The cognitive and brain sciences can make considerable theoretical and methodological contributions to this endeavour.To date, the modal approach to cognitive science-informed human–robot interaction research has been grounded in social cognition.We widen the lens through which much of this research has been framed to incorporate diverse and interacting forms of cognition, which emphasise the complexity of understanding human–robot interaction on a mechanistic level.Our framework seeks to account for the unusual demands on (neuro)cognition presented by engaging with social machines, focusing on machine variety, diverse neurocognitive systems, and unique contextual factors such as media-skewed expectations. As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting human–machine interactions is becoming increasingly pressing. We introduce a framework for studying the cognitive and brain mechanisms that support human–machine interactions, leveraging advances made in cognitive neuroscience to link different levels of description with relevant theory and methods. We highlight unique features that make this endeavour particularly challenging (and rewarding) for brain and behavioural scientists. Overall, the framework offers a way to study the cognitive science of human–machine interactions that respects the diversity of social machines, individuals' expectations and experiences, and the structure and function of multiple cognitive and brain systems. As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting human–machine interactions is becoming increasingly pressing. We introduce a framework for studying the cognitive and brain mechanisms that support human–machine interactions, leveraging advances made in cognitive neuroscience to link different levels of description with relevant theory and methods. We highlight unique features that make this endeavour particularly challenging (and rewarding) for brain and behavioural scientists. Overall, the framework offers a way to study the cognitive science of human–machine interactions that respects the diversity of social machines, individuals' expectations and experiences, and the structure and function of multiple cognitive and brain systems. Over a decade ago, Microsoft founder Bill Gates prophesied a robotics revolution that would see staggering leaps in the progress and sophistication of robots, and predicted 'a robot in every home' in the near future [1.Gates B. A robot in every home.Sci. Am. 2008; 18: 4-11Crossref Google Scholar]. Although the ubiquity of household robots has yet to be realised, we are opening our doors to an increasing number of artificially intelligent machines (see Glossary). Concurrently, a growing number of robotics start-ups are focusing on developing companion robots for the home or assistance robots to serve in complex, human-interactive contexts, including schools, hospitals, and care homes [2.Weinberg N. Rbr report: how to start a robotics company.Robot. Bus. Rev. 2019; : 1-12Google Scholar]. As progress towards developing machines that take on increasingly sophisticated social roles continues apace, the cognitive and brain mechanisms that underpin social engagement with these machines remain largely unknown. A greater understanding of the psychological and neurobiological foundations of human interactions with artificially intelligent social machines (henceforth referred to as 'social machines') has important implications for the design and programming of socially engaging and collaborative artificial agents. It is equally critical to use this understanding of human–machine interaction to further our knowledge of the flexibility and limits of neurocognitive processes supporting human social behaviour towards both human and artificial agents. Investigating the cognitive and brain mechanisms that underpin human–machine interactions is a fledgling field that faces several unique challenges. For example, the vast range of artificially intelligent machines, from small handheld devices such as smartphones and thermostats to autonomous petlike robots, such as Paro and MiRo, all the way to life-sized humanoid robots, such as iCub or Pepper, suggests that this endeavour represents a highly variable space (Figure 1). Interactions with different robots are likely, therefore, to involve a range of different mental processes, making a 'one size fits all machines' type of cognition unlikely. Furthermore, even the most sophisticated social robots share features with inanimate objects and rudimentary machines in addition to animate beings, such as animals and humans. Consequently, the typical dividing lines of cognition that carve up the environment on the basis of clear categories are broken. This presents a particular challenge when one attempts to understand how the brain constructs mental representations that guide interactions with artificially intelligent machines. Although the work of engineers, roboticists, behavioural scientists, and philosophers has done much to inform our understanding of human–robot interaction (HRI), here we leverage discoveries made in human cognitive neuroscience to outline a framework for studying human–machine interactions that holds potential for advancing our understanding in new ways. We focus on human interactions with robots that have been designed to take on social roles. As a starting point in this endeavour, we propose an updated framework that broadens the focus of HRI research beyond social cognition and that considers (i) the important and distinct roles played by machine variation, (ii) diverse and interacting cognitive and brain systems, and (iii) the unique challenges and opportunities that research into human interactions with social machines presents (Figure 2C ). The primary value of placing HRI research into this broad framework is to better characterise the interplay between domain-general information-processing systems, which operate across a range of stimuli, tasks, and contexts, and those that are more domain-specific and tied to interactions with other agents, such as humans and social machines. In line with this, broadening the focus of HRI research to include theoretical and empirical perspectives beyond social cognition will be important because, even though considerable progress has been made in understanding social cognition, it would be a mistake to assume that our understanding of what is 'social' about social cognition is clear-cut or finalised [3.Lockwood P.L. et al.Is there a 'social' brain? Implementations and algorithms.Trends Cogn. Sci. 2020; 24: 802-813Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar,4.Caruana N. McArthur G. 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A wealth of cognitive neuroscience research has charted the human brain's response to perceiving and categorising salient stimuli in the environment, including faces, body parts, objects, and tools (e.g., [5.Chao L.L. et al.Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects.Nat. Neurosci. 1999; 2: 913-919Crossref PubMed Scopus (928) Google Scholar, 6.Chao L.L. Martin A. Representation of manipulable man-made objects in the dorsal stream.Neuroimage. 2000; 12: 478-484Crossref PubMed Scopus (935) Google Scholar, 7.Downing P.E. et al.Domain specificity in visual cortex.Cereb. Cortex. 2006; 16: 1453-1461Crossref PubMed Scopus (341) Google Scholar, 8.Epstein R. Kanwisher N. A cortical representation of the local visual environment.Nature. 1998; 392: 598-601Crossref PubMed Scopus (2017) Google Scholar, 9.Kanwisher N. et al.The fusiform face area: a module in human extrastriate cortex specialized for face perception.J. 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Second, compared with divisions between humans, houses, and animals, for example, clear-cut categories are not as easily agreed upon when it comes to machines, especially artificially intelligent social machines. Definitions of what makes a robot 'social' or 'intelligent' are slippery, may vary across people, and are generally difficult to agree upon. With this being said, coarse categorisation is possible to some extent: Machines have automated functions compared with objects and tools, and an objective boundary exists between even the most sophisticated machines and humans (Figure 1A). Nonetheless, the variety of machines within and between loosely defined categories is vast in terms of size, shape, motion profile, and intended use or purpose (Figure 1B). This variation matters because, if neglected, it poses a threat to the tractability of human–machine interaction research (Box 1).Box 1Respecting the Variety of Machines and Research GoalsVariety in HRI research is not limited to the type of machine; a wide variety of aims also exist. Such aims range from understanding different levels of description such as psychological, cognitive, and neurobiological, as well as work that aims to probe how basic systems operate when people encounter and interact with robots, right up to applications in robotics, such as in the service industry, education, healthcare, and therapy. These aims also include whether a particular robot meets the specific requirement for which it was originally built. Much like recent proposals in computational neuroscience, where a wide variety of aims and objectives also proliferate [89.Kording K.P. Blohm G. Schrater P. Kay K. Appreciating the variety of goals in computational neuroscience.in: NBDT. 3. 2020: 1-12Google Scholar], our argument is that HRI research will likewise benefit from authors being clear about the stated aims of their work. Considerable debate, disagreement, and possibly confusion exist regarding the purpose of computational modelling in neuroscience precisely because computational modellers are pursuing not one goal, but many. We see many parallels between the computational neuroscience domain and HRI research in this regard. Consequently, if HRI researchers were to routinely and explicitly justify the goal or goals of each individual study, as discussed in a recent review paper [72.Baxter P. Kennedy J. Senft E. Lemaignan S. Belpaeme T. From characterising three years of HRI to methodology and reporting recommendations. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch2016: 391-398https://doi.org/10.1109/HRI.2016.7451777Crossref Scopus (55) Google Scholar], this should facilitate comparisons across studies, methods, and robotics platforms, thus leading to more rapid progress in HRI as a field.As one example, important differences exist between research that aims to understand the psychological aspects underpinning HRIs in terms of how individuals think and act toward robots, and research that aims to investigate the underlying cognitive and brain systems supporting HRI. Indeed, understanding how one feels or behaves towards a specific robot (compared with humans or other robots, for example) may not always inform the structure of cognitive and brain function. Likewise, the study of cognitive and brain function may not always yield useful insights into how a person may feel or behave towards a robot. As such, the field of HRI stands to benefit if researchers are clearer regarding the extent to which their aims are to understand how people perceive and interact with robots in terms of psychological variables and/or to make inferences about the structure and function of underlying (neuro)cognitive systems (cf. [70.Wykowska A. Social robots to test flexibility of human social cognition.Int. J. Soc. Robot. 2020; https://doi.org/10.1007/s12369-020-00674-5Crossref Scopus (3) Google Scholar]). Such research aims can, of course, be related but are nonetheless distinct in important ways. Variety in HRI research is not limited to the type of machine; a wide variety of aims also exist. Such aims range from understanding different levels of description such as psychological, cognitive, and neurobiological, as well as work that aims to probe how basic systems operate when people encounter and interact with robots, right up to applications in robotics, such as in the service industry, education, healthcare, and therapy. These aims also include whether a particular robot meets the specific requirement for which it was originally built. Much like recent proposals in computational neuroscience, where a wide variety of aims and objectives also proliferate [89.Kording K.P. Blohm G. Schrater P. Kay K. Appreciating the variety of goals in computational neuroscience.in: NBDT. 3. 2020: 1-12Google Scholar], our argument is that HRI research will likewise benefit from authors being clear about the stated aims of their work. Considerable debate, disagreement, and possibly confusion exist regarding the purpose of computational modelling in neuroscience precisely because computational modellers are pursuing not one goal, but many. We see many parallels between the computational neuroscience domain and HRI research in this regard. Consequently, if HRI researchers were to routinely and explicitly justify the goal or goals of each individual study, as discussed in a recent review paper [72.Baxter P. Kennedy J. Senft E. Lemaignan S. Belpaeme T. From characterising three years of HRI to methodology and reporting recommendations. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Christchurch2016: 391-398https://doi.org/10.1109/HRI.2016.7451777Crossref Scopus (55) Google Scholar], this should facilitate comparisons across studies, methods, and robotics platforms, thus leading to more rapid progress in HRI as a field. As one example, important differences exist between research that aims to understand the psychological aspects underpinning HRIs in terms of how individuals think and act toward robots, and research that aims to investigate the underlying cognitive and brain systems supporting HRI. Indeed, understanding how one feels or behaves towards a specific robot (compared with humans or other robots, for example) may not always inform the structure of cognitive and brain function. Likewise, the study of cognitive and brain function may not always yield useful insights into how a person may feel or behave towards a robot. As such, the field of HRI stands to benefit if researchers are clearer regarding the extent to which their aims are to understand how people perceive and interact with robots in terms of psychological variables and/or to make inferences about the structure and function of underlying (neuro)cognitive systems (cf. [70.Wykowska A. Social robots to test flexibility of human social cognition.Int. J. Soc. Robot. 2020; https://doi.org/10.1007/s12369-020-00674-5Crossref Scopus (3) Google Scholar]). Such research aims can, of course, be related but are nonetheless distinct in important ways. A dimensional or 'feature-mapping' approach may offer a solution to the challenging, and likely fruitless, endeavour of strictly categorising the socialness and intelligence of robots (Figure 1B). A number of research teams have previously touched upon the topic of feature mapping from both HRI [20.Baraka K. et al.An extended framework for characterizing social robots.in: Jost C. Human-Robot Interaction: Evaluation Methods and Their Standardization. Springer, 2020: 21-64Crossref Google Scholar, 21.Phillips E. et al.What is human-like? Decomposing robots' human-like appearance using the Anthropomorphic roBOT (ABOT) database.in: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. 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Such feature combinations matter for psychological and brain-based research because an agent may be relatively human-like along one dimension but not at all in other dimensions, which is likely to impact mental processes in (as yet) unknown ways. Placing a greater emphasis on the variety of machines and their associated features is the first central pillar of the current proposal and has both theoretical and practical implications. In terms of theory development, under a dimensional account, it becomes unsurprising that an artificial agent could engage systems similar to those of a human. This is because such agents were explicitly designed to look or behave in a human-like manner on some level [28.Klapper A. et al.The control of automatic imitation based on bottom-up and top-down cues to animacy: insights from brain and behavior.J. Cogn. 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