  This article introduces a three-axis framework indicating how AI can be
informed by biological examples of social learning mechanisms. We 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. However, the
field of AI has mostly embraced a solipsistic perspective on intelligence. We
thus argue that social interactions not only are largely unexplored in this
field but also are an essential element of advanced cognitive ability, and
therefore constitute metaphorically the dark matter of AI. In the first
section, we discuss how social learning plays a key role in the development of
intelligence. We do so by discussing social and cultural learning theories and
empirical findings from social neuroscience. Then, we discuss three 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 dynamical systems. Third,
embodiment has been demonstrated to provide more sophisticated array of
communicative signals. To conclude, we discuss the example of active inference,
which offers powerful insights for developing agents that possess biological
realism, can self-organize in time, and are socially embodied.
