Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process

Abstract

We model the vague-to-crisp dynamics of forming percepts in the brain by combining two methodologies: dynamic logic (DL) and operant learning process. Forming percepts upon the presentation of visual inputs is likened to model selection based on sampled evidence. Our framework utilizes the DL in selecting the correct "percept" among competing ones, but uses an intrinsic reward mechanism to allow stochastic online update in lieu of performing the optimization step of the DL framework. We discuss the connection of our framework with cognitive processing and the intentional neurodynamic cycle.

Combined linguistic and sensor models for machine learning

“…Linguistic and sensory inputs usually are processed one at a time. The transformation of the current dynamic logic framework into an online learning framework has been the subject of recent work [16]. That work may be developed further by incorporating the concept of dual models.…”