A Developmental Cognitive Architecture for Trust and Theory of Mind in Humanoid Robots

Massimiliano Patacchiola, Angelo Cangelosi

Research output: Contribution to journalArticlepeer-review


As artificial systems are starting to be widely deployed
in real-world settings, it becomes critical to provide them
with the ability to discriminate between different informants
and to learn from reliable sources. Moreover, equipping an
artificial agent to infer beliefs may improve the collaboration
between humans and machines in several ways. In this article we
propose a hybrid cognitive architecture, called Thrive, with the
purpose of unifying in a computational model recent discoveries
regarding the underlying mechanism involved in trust. The model
is based on biological observations that confirmed the role of
the midbrain in trial-and-error learning, and on developmental
studies that indicate how essential is a theory of mind in order
to build empathetic trust. Thrive is build on top of an actorcritic
framework that is used to stabilize the weights of two selforganizing
maps. A Bayesian network embeds prior knowledge
into an intrinsic environment, providing a measure of cost that
is used to boostrap learning without an external reward signal.
Following a developmental robotics approach we embodied the
model in the iCub humanoid robot and we replicated two
psychological experiments. The results are in line with real data,
and shed some light on the mechanisms involved in trust-based
learning in children and robots.
Original languageEnglish
JournalIEEE Transactions on Cybernetics
Publication statusAccepted/In press - 13 Jun 2020


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