Contextualizing predictive minds.

Abstract

The structure of human memory seems to be optimized for efficient prediction, planning, and behavior. We propose that these capacities rely on a tripartite structure of memory that includes concepts, events, and contexts-three layers that constitute the mental world model. We suggest that the mechanism that critically increases adaptivity and flexibility is the tendency to contextualize. This tendency promotes local, context-encoding abstractions, which focus event- and concept-based planning and inference processes on the task and situation at hand. As a result, cognitive contextualization offers a solution to the frame problem-the need to select relevant features of the environment from the rich stream of sensorimotor signals. We draw evidence for our proposal from developmental psychology and neuroscience. Adopting a computational stance, we present evidence from cognitive modeling research which suggests that context sensitivity is a feature that is critical for maximizing the efficiency of cognitive processes. Finally, we turn to recent deep-learning architectures which independently demonstrate how context-sensitive memory can emerge in a self-organized learning system constrained with cognitively-inspired inductive biases.