The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots

Francesco Bossi, Cesco Willemse, Jacopo Cavazza, Serena Marchesi, Vittorio Murino, Agnieszka Wykowska

Research output: Contribution to journalArticlepeer-review


The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in explaining robots’ behavior as stemming from operations of the mind (intentional interpretation) or as a result of mechanistic design (mechanistic interpretation). Here, we examined whether individual attitudes toward robots can be differentiated on the basis of default neural activity pattern during resting state, measured with electroencephalogram (EEG). Participants observed scenarios in which a humanoid robot was depicted performing various actions embedded in daily contexts. Before they were introduced to the task, we measured their resting state EEG activity. We found that resting state EEG beta activity differentiated people who were later inclined toward interpreting robot behaviors as either mechanistic or intentional. This pattern is similar to the pattern of activity in the default mode network, which was previously demonstrated to have a social role. In addition, gamma activity observed when participants were making decisions about a robot’s behavior indicates a relationship between theory of mind and said attitudes. Thus, we provide evidence that individual biases toward treating robots as either intentional agents or mechanistic artifacts can be detected at the neural level, already in a resting state EEG signal.
Original languageEnglish
JournalScience Robotics
Issue number46
Publication statusPublished - 30 Sept 2020


Dive into the research topics of 'The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots'. Together they form a unique fingerprint.

Cite this