A neurocognitive investigation of the impact of socializing with a robot on empathy for pain

Abstract

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing intervention to probe the flexibility of empathy, a core component of social relationships, towards robots. Twenty-six individuals underwent identical fMRI sessions before and after being issued a social robot to take home and interact with over the course of a week. While undergoing fMRI, participants observed videos of a human actor or a robot experiencing pain or pleasure in response to electrical stimulation. Repetition suppression of activity in the pain network, a collection of brain regions associated with empathy and emotional responding, was measured to test whether socializing with a social robot leads to greater overlap in neural mechanisms when observing human and robotic agents experiencing pain or pleasure. In contrast to our hypothesis, functional region-of-interest analyses revealed no change in neural overlap for agents after the socializing intervention. Similarly, no increase in activation when observing a robot experiencing pain emerged post-socializing. Whole-brain analysis showed that, before the socializing intervention, superior parietal and early visual regions are sensitive to novel agents, while after socializing, medial temporal regions show agent sensitivity. A region of the inferior parietal lobule was sensitive to novel emotions, but only during the pre-socializing scan session. Together, these findings suggest that a short socialization intervention with a social robot does not lead to discernible differences in empathy towards the robot, as measured by behavioural or brain responses. We discuss the extent to which long-term socialization with robots might shape social cognitive processes and ultimately our relationships with these machines. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

From social brains to social robots: applying neurocognitive insights to human–robot interaction

“…The second piece in this section is also an fMRI study, conducted by Cross et al [20]. In this ambitious training study, the authors were interested in the effects of socializing with a socially engaging robot (the palm-sized Cozmo robot by Anki, Inc.) on participants' empathy for the robot's emotions (compared to a human's emotions).…”

Social Robots on a Global Stage: Establishing a Role for Culture During Human–Robot Interaction

“…This flexibility and creativity in cognition suggests that human behavior toward, and social interactions with, robots are hardly static or fixed, and the longer we spend with robots, the more our attitudes and behaviors toward them might evolve. For example, a recent study by Cross et al [112] sought to map behavioral and brain-based responses of participants' empathy toward robots before and after a prolonged socializing intervention wherein participants took home a palm-sized Cozmo robot and played games with it each day across a five-day period. While this study did not find compelling evidence that spending more time socializing with a robot shaped empathic responses toward that robot, it nonetheless raises many interesting questions regarding what the boundary conditions are for shaping our social perceptions of and behaviors toward robots, as well as role of culture in relationship building with robots.…”

Tracking Human Interactions with a Commercially-available Robot over Multiple Days: A Tutorial

“…First, we extracted all the animations from the application version 1.5.0 and used the animation group name (e.g., 'ask', 'badword', 'turtleroll', 'hiccup') to subjectively classify the animation as either social or not social, and subsequently quantify the social animations as either a positive or negative emotion. We used this in a first study on the impact of socialising with the Cozmo robot on a distinct aspect of social cognition (Cross, Riddoch, et al, 2019). Here, we built on this step by using a data-driven approach to classify the emotional content of these animations.…”

“…{"$app":"1.5.0.353.170501.1700.p.5062a7c", "$apprun":"453bd1f1-5e1d-426f-a20c-e87cccdecf79", "$data":"NoneBehavior", "$game":"4da09c2f-2184-489c-ad9b-2ba9af78a40a", "$group":"0xbeef000006f02499", "$level":"event", "$messv":"2", "$phone":"Android OS-Lenovo TB3-710F-5.0.1-Lenovo_TB3-710F_S000026", "$phys":"0xbeef00000004024092ee", "$platform":"android", "$product":"cozmo", "$seq":"547", "$ts":"1530904042619", "$unit":"5ac77e18-fa54-4415-aebe-ed9c322d270e", "robot.behavior_transition":"reactToPickup"} 033265,0,0,-0.3,0,0,0.262434,0,0, robot.mood_values 0.0328382,0,0,-0.3,0,0,0.338734,0,0, robot Initially, we were interested in the general quality and quantity of the interaction with Cozmo robot over a 5 days period. In our first long-term interaction study (Cross, Riddoch, et al, 2019), we studied empathy towards a robot before and after the 5 days of socialisation. For this, the log extraction was meant to verify sufficient socialisation.…”

“…It has been successfully used in laboratories, schools, and public events, and with children and adult samples. Researchers have used it in experimental studies to measure perception and behaviour during one-off interactions (Abubshait et al, 2020;Chatterji et al, 2019;Ciardo et al, 2020;Currie & Wiese, 2019;Druga et al, 2017Druga et al, , 2018Gonnot et al, 2019;Hinz et al, 2019;Hsieh et al, 2020aHsieh et al, , 2020bJoshi & Šabanović, 2019;Kumar Singh et al, 2020;Lefkeli et al, 2020;Roselli et al, 2019;Tan et al, 2018) and unrestricted and repeated interactions outside of the laboratory (Cross, Riddoch, et al, 2019;Pelikan et al, 2020). Together, these studies have investigated a variety of human behaviours, from empathy to emotion perception, and from social decision making and learning to the sense of agency in the human user.…”