Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

Abstract

Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user's needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human-robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human-human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human-robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human-robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles.

Keywords: attribution of intentionality; human–robot interaction; mind perception; social neuroscience; social robotics.

FIGURE 1

Investigation of human-robot interaction with the use of neuroscientific methods. The image on the left illustrates the setup of an fMRI experiment measuring changes in blood flow in social brain areas during a joint attention task with the robot EDDIE (designed by Technical University of Munich). Participants are asked to respond as fast and accurately as possible to the identity of a target letter (“F” vs. “T”) that is either looked at nor not looked at by EDDIE. Changes in activation in social brain areas can be captured with a high spatial resolution, but no natural interactivity with the robot can be achieved (i.e., interaction needs to be imagined: offline social cognition). The image on the right shows a setup where neural processes associated with joint attention are examined using EEG and eye-tracking during interactions with the robot iCub (designed at the Istituto Italiano di Technologia by Metta et al., 2008). Similar to the previous example, participants are asked to react to the identity of a target letter (“T” vs. “V”) that is either looked at or not looked at by iCub. Mechanisms of joint attention can be captured with high temporal resolution during relatively natural interactions (i.e., online social cognition). Written informed consent has been obtained for publication of the identifiable image on the right.

FIGURE 2

Social brain network consisting of the action perception system (APS, mainly brodmann areas 6, 44, but also 4 and 40), superior temporal sulcus (STS; brodmann areas 21, 22), temporo-parietal junction (TPJ; brodmann areas 39,40), medial prefrontal cortex (mPFC; brodmann areas 8, 9, 10, 32) and anterior cingulate cortex (ACC; brodmann area 24). APS and STS detect biological motion and make inferences about low-level action goals from observed behavior. TPJ and mPFC are involved in mentalizing about high-level action goals and stable person features. ACC is associated with the attribution of mental states to non-human entities. The image has been modified (the original image was retrieved from: http://2.bp.blogspot.com/-SE4Yb_SRjdw/T6rNRgvRedI/AAAAAAAAAA0/FaU50ZemOCY/s1600/brodmann.png).

FIGURE 3

Effect of mind perception on the social relevance of observed behavior. (A) Participants are asked to perform a joint attention task (react as fast and accurately as possible to the identity of a target letter: “F” vs. “T”) with the robot EDDIE (designed by Technical University of Munich). Results show that changes in gaze direction are followed more strongly when the eye movements are believed to be intentional versus pre-programmed. (B) Grand average ERP waveforms time-locked to the onset of the target for the pool of O1/O2/PO7/PO8 electrodes show that the belief that eye movements are intentional enhances sensory gain control mechanisms, with larger P1 validity effects (i.e., difference between valid and invalid trials) for changes in gaze direction that are believed to be intentional (i.e., human-controlled, red lines) versus non-intentional (i.e., pre-programmed, green lines). (C) Topographical maps of voltage distribution (posterior view) show that observing an intentional agent (left panel) versus a pre-programmed agent (right panel) modulates mechanisms of joint attention in occipital and parietal areas (suggesting that attribution of mental states affects visual and early attentional processes). The time interval of the P1 component (100–140 ms) is presented in the upper panel. The time interval of the N1 component (170–210 ms) is presented in the lower panel. For more details see: Wykowska et al. (2014b).

FIGURE 4

Social facilitation effects in Human–Robot Interaction. Perceiving robot agents as having a mind can induce social facilitation effects on human performance (i.e., presence of a robot agent facilitates performance on simple tasks, but worsens performance on difficult tasks) and foster learning via social reinforcement (i.e., robot provides social cues like smiling for wanted behaviors). The facilitating and reinforcing abilities of companion robots can be used in the classroom to improve learning (left image) or during driving to verbally and non-verbally encourage wanted driving behaviors (right image), for example. Written informed consent has been obtained for publication of the identifiable image on the left. The image on the right was modified (the original image of the driving simulator was retrieved from http://stevevolk.com; the original image of the robot was retrieved from: http://newsroom.toyota.co.jp/).

FIGURE 5

Mind perception can induce a cognitive conflict for agents with ambiguous physical appearance. (A) Effects of mind perception triggered by physical appearance can be measured using a morphing procedure (e.g., image of a robot is morphed into image of a human or a dog in steps of 5%). (B) Mind perception follows a qualitative (i.e., significant changes in mind ratings occur only after a critical level of physical humanness is reached) rather than a quantitative pattern (i.e., likelihood for perceiving mind increases in a linear fashion with physical humanness). The significant change in mind ratings occurs when the category boundary between human and non-human is crossed (upper panel). Agents located at the category boundary are ambiguous in terms of their mind status, and trying to categorize them as human versus non-human causes a cognitive conflict, which takes cognitive resources to resolve. The degree of cognitive conflict that is induced by a categorical decision can be measured using mouse-tracking (i.e., the more curved the mouse movement, the larger the cognitive conflict; see Freeman and Ambady, 2010). For more details regarding the experiment on cognitive conflict in HRI: Weis and Wiese (2017).