Can You Activate Me? From Robots to Human Brain

Abstract

The effectiveness of social robots has been widely recognized in different contexts of humans’ daily life, but still little is known about the brain areas activated by observing or interacting with a robot. Research combining neuroscience, cognitive science and robotics can provide new insights into both the functioning of our brain and the implementation of robots. Behavioural studies on social robots have shown that the social perception of robots is influenced by at least two factors: physical appearance and behavior (Marchetti et al., 2018). How can neuroscience explain such findings? To date, studies have been conducted through the use of both EEG and fMRI techniques to investigate the brain areas involved in human-robot interaction. These studies have mainly addressed brain activations in response to paradigms involving either action performance or charged of an emotional component (Figure 1). FIGURE 1. Robots can activate the human brain. A first set of studies analysed the effect of different types of robots varying in their level of physical anthropomorphism on the activation of the Mirror Neuron Mechanism (MNM). The neuronal activities examined through fMRI indicated that the activation of medial premotor cortex (MPFC) increased linearly over the degree of human-likeness of the robots, from the most mechanical to android ones (Krach et al., 2008). Electroencephalography (EEG) data associated with the mu wave–related to the MNM–showed a modulation of the mu rhythm as a function of the robotic agent resemblance to the human (Urgen et al., 2013; Matsuda et al., 2016). Furthermore, the fMRI findings on MNM indicated that the premotor cortex is similarly activated when actions are performed by different types of robots (more mechanical or android) (Saygin et al., 2012). These evidences support the hypothesis that the premotor cortex is “automatically” triggered in response to both simple and complex goal-directed and intentional actions, revealing a sensitivity to both the living and non-living ontological status of the agent (Gazzola et al., 2007; Saygin et al., 2012). Activation of the premotor cortex was also found in response to a human or robotic face expressing emotions (Chaminade et al., 2010). Several studies in humans have found that the premotor cortex is involved in the process of emotion recognition by encoding the motor pattern, (i.e. facial expression) that characterizes a given emotional state. The visuo-motor information processed in premotor cortex is translated into affective information by means of the insula that acts as a relay station between the cortical and subcortical areas, such as the amygdala, involved in processing emotional stimuli, (e.g. Carr et al., 2003; Wicker et al., 2003; Iacoboni, 2009). Likewise, the parieto-prefrontal network characterizing the MNM has been found to be particularly sensitive to biological movement, (e.g. Dayan et al., 2007; Casile et al., 2009; Di Dio et al., 2013). Accordingly, it was demonstrated that observing a motor or emotional behaviour performed by a human-like robotic agent, resembling the human kinematics, may be sufficient to activate MNM (Gazzola et al., 2007; Chaminade et al., 2010). Additionally, investigating the vitality forms of movement, which characterize the style of an action, (e.g. rude vs. gentle) (Stern, 1985, Stern, 2010), it was shown that, besides the activation of the MNM, vitality forms activate also the dorso-central insular cortex (Di Dio et al., 2013; Di Cesare et al., 2016), which represents the relay through which information about the action style, (i.e. action kinematics) processed in the parietal MNM is invested with an affective quality. Most importantly, very recent neuroscientist evidence has shown that the same brain areas whose activation is stimulated by human vitality forms can be also evoked by robots’ actions performed by simulating human kinematics (Di Cesare et al., 2020), thus conveying information about the robot’s “emotion state”. However, the activation of other brain areas besides the MNM, such as ventral visual areas, may be required to accommodate the robot’s inconsistent kinematics associated with simple vs. complex goal-directed actions (Gazzola et al., 2007). Similarly, fMRI data showed a greater activation of posterior occipital and temporal visual cortices in response to facial expression of robot emotions compared to human emotions, reflecting a further level of processing in response to the unfamiliar stimulus, (i.e. the face of the robot) (Chaminade et al., 2010; Jung et al., 2016). Additionally, the increase in frontal theta activity–associated with the recovery from long-term memory–measured through EEG is greater for a mechanical robot than a human or android (Urgen et al., 2013), highlighting once more the involvement of a compensation process for the analysis of robot stimuli. More specifically, this finding indicates that a lower level of physical robot anthropomorphism requires more resources from memory systems to bridge the semantic gap between the agent and its action (Urgen et al., 2013). People's sense of affiliation with a robot during interactions is at least partially explained by the emotional responses to the robot's behaviour. Still few studies have analysed the brain activation in response to the emotions expressed by robots. EEG data suggest that people can recognize the bodily emotions expressed by a robot, including joy and sadness, although not all the expressed emotions elicit a significant brain response in the viewer (Guo et al., 2019). Additionally, also fMRI data indicate that emotional expressions, (i.e. joy, anger and disgust) are perceived as more emotional when expressed by a human face than by a robot (Chaminade et al., 2010). As argued above, these differences could be explained by a non-perfect alignment between the robot...