  For the complex human brain that enables us to communicate in natural
language, we gathered good understandings of principles underlying language
acquisition and processing, knowledge about socio-cultural conditions, and
insights about activity patterns in the brain. However, we were not yet able to
understand the behavioural and mechanistic characteristics for natural language
and how mechanisms in the brain allow to acquire and process language. In
bridging the insights from behavioural psychology and neuroscience, the goal of
this paper is to contribute a computational understanding of appropriate
characteristics that favour language acquisition. Accordingly, we provide
concepts and refinements in cognitive modelling regarding principles and
mechanisms in the brain and propose a neurocognitively plausible model for
embodied language acquisition from real world interaction of a humanoid robot
with its environment. In particular, the architecture consists of a continuous
time recurrent neural network, where parts have different leakage
characteristics and thus operate on multiple timescales for every modality and
the association of the higher level nodes of all modalities into cell
assemblies. The model is capable of learning language production grounded in
both, temporal dynamic somatosensation and vision, and features hierarchical
concept abstraction, concept decomposition, multi-modal integration, and
self-organisation of latent representations.
