The emergence of compositionality in a brain-inspired cognitive architecture

Abstract

Compositionality can be considered as finding (or creating) the correct meaning of the constituents of a non-simple language expression or visual image. The Causal Cognitive Architecture is a brain-inspired cognitive architecture (BICA). It is not a traditional artificial neural network architecture, nor a traditional symbolic AI system but instead uses spatial navigation maps as its fundamental circuits. In previously described versions of the architecture, sensory inputs are compared in each existing sensory system against previous stored navigation maps for that sensory system, and the best navigation map is chosen and then updated with the new sensory inputs and a best multisensory navigation map is similarly created and used as the working navigation map. Instinctive and learned small procedures are triggered by input sensory inputs as well as matched navigation maps, and in the Navigation Module operate on the working navigation map and produce an output signal. By feeding back intermediate results in the Navigation Module it has been shown previously how causal and analogical behaviors emerge from the architecture. In new work, the Navigation Module is duplicated in a biologically plausible manner. It becomes possible to compositionally process information in the duplicated Navigation Module, and as a result compositional language comprehension and behavior readily emerge. A formalization and simulation of the architecture is presented. A demonstration example, and its negation, are explored of solving a compositional problem requiring the placement of an object in a specific location with regard to other objects. Future work is discussed using large language models to create navigation maps. Given the mammalian brain inspiration of the architecture, it suggests that it is indeed feasible for modest genetic changes to have allowed the emergence of compositional language in humans.