The concept of social interaction is at the core of embodied and enactive approaches to social
cognitive processes, yet scientifically it remains poorly understood. Traditionally, cognitive …

Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding
improved sample efficiency, the ability to deal with long time horizons and transferability of …

Behavior involves the ongoing interaction between an organism and its environment. One
of the prevailing theories of adaptive behavior is that organisms are constantly making …

Living organisms learn on multiple time scales: evolutionary as well as individual-lifetime
learning. These two learning modes are complementary: the innate phenotypes developed …

This paper introduces \texttt{infotheory}: a package written in C++ and usable from Python
and C++, for multivariate information theoretic analyses of discrete and continuous data. This …

Artificial Life has a long tradition of studying the interaction between learning and evolution.
And, thanks to the increase in the use of individual learning techniques in Artificial …

One of the greatest research challenges of this century is to understand the neural basis for
how behavior emerges in brain-body-environment systems. To this end, research has …

The availability of unstructured text as a source of data has increased by orders of magnitude
in the last few years, triggering extensive research in the automated processing and …

The brain in conjunction with the body is able to adapt to new environments and perform
multiple behaviors through reuse of neural resources and transfer of existing behavioral traits. …

Living organisms perform multiple tasks, often using the same or shared neural networks.
Such multifunctional neural networks are composed of neurons that contribute to different …