Social Neuro AI: Social Interaction as the "dark matter" of AI
- URL: http://arxiv.org/abs/2112.15459v2
- Date: Mon, 3 Jan 2022 14:15:21 GMT
- Title: Social Neuro AI: Social Interaction as the "dark matter" of AI
- Authors: Samuele Bolotta and Guillaume Dumas
- Abstract summary: We argue that empirical results from social psychology and social neuroscience along with the framework of dynamics can be of inspiration to the development of more intelligent artificial agents.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We are making the case that empirical results from social psychology and
social neuroscience along with the framework of dynamics can be of inspiration
to the development of more intelligent artificial agents. We specifically argue
that the complex human cognitive architecture owes a large portion of its
expressive power to its ability to engage in social and cultural learning. In
the first section, we aim at demonstrating that social learning plays a key
role in the development of intelligence. We do so by discussing social and
cultural learning theories and investigating the abilities that various animals
have at learning from others; we also explore findings from social neuroscience
that examine human brains during social interaction and learning. Then, we
discuss three proposed lines of research that fall under the umbrella of Social
NeuroAI and can contribute to developing socially intelligent embodied agents
in complex environments. First, neuroscientific theories of cognitive
architecture, such as the global workspace theory and the attention schema
theory, can enhance biological plausibility and help us understand how we could
bridge individual and social theories of intelligence. Second, intelligence
occurs in time as opposed to over time, and this is naturally incorporated by
the powerful framework offered by dynamics. Third, social embodiment has been
demonstrated to provide social interactions between virtual agents and humans
with a more sophisticated array of communicative signals. To conclude, we
provide a new perspective on the field of multiagent robot systems, exploring
how it can advance by following the aforementioned three axes.
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