Semantic Primes-Inspired Tacit Knowledge Dataset for Simulating Basic Perception Capabilities of Cognitive Architectures

In this paper we present a novel dataset of tacit knowledge represented in natural language (Japanese) inspired by semantic primes categories. The main goals of this data is to a) allow investigations regarding influence of perception data in various cognitive tasks, b) mimic signals for cognitive processes of an artificial agent to extend the understanding of the world and c) testing cognitive capabilities of intelligent instances like foundation models. We describe the dataset and share results of preliminary experiments showing that the tacit knowledge recognition is still hard for language models. We also discuss how such redirecting neural approaches to cognition only and then perform reasoning in a symbolic realms could become beneficial for new type of simulations before AGIs are equipped with more sophisticated sensory apparatus.