Explicitly Acquired Interrelations Among Mental Schema Reduces Cognitive Load and Facilitates Emergence of Novel Responses in Mice and Artificial Neural Networks.

Abstract

Mammalian brain has evolved to infer from past experiences and elicit context relevant novel behavioural responses hitherto unexpressed by the animal. However, little is known about how prior knowledge influences the emergence of such responses. Remarkably, the brain not only arrives at these responses through logical inferences based on previous leanings, but also acquire new related information, without causing catastrophic interference. Mental schemas have often been proposed as the framework for this phenomenon. In this study, using mice as a model animal, we show that schematic networks not only enhance the cognitive load handling capacity (CLHC) and prevent catastrophic interference, but also facilitate the generation of novel, contextually relevant responses. Interestingly, when the animals were trained in a paradigm that did not invoke the pre-formed mental schema, we observed neither an enhancement to CLHC nor a generation of novel context relevant responses. Based on the principles of mental schemas discovered in our animal experiments, we developed a biologically plausible artificial neural network (ANN) that avoids catastrophic interference and captures the learning properties observed in our experiments. The custom architecture of this ANN enables it to generate responses similar to those of animals in novel scenarios. Significance StatementLittle is known about the role of mental schemas in preventing memory interference--a process in which overlapping or similar memories hinder the acquisition and retention of related information. In this study, we demonstrate that mental schemas enhance cognitive load handling capacity and improve the ability to solve novel but related problems. Using mice as a model, we show that the mere existence of a mental schema is not enough for improved cognitive load handling; instead, the relationship between existing and new information must be explicitly established during the learning process. Based on these findings, we developed a minimalistic artificial neural network (ANN) that effectively mimics this behaviour. These insights pave the way for developing more efficient learning and teaching strategies.