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

Mammalian brain has evolved to infer from past experiences and elicit context relevant novel behavioural responses hitherto unexpressed by the animal. Little is known about if and how prior knowledge affect emergence of such novel responses. Remarkably, our brain not only arrives at these responses through logical inferences but can also preserve these related memories distinctly without catastrophic interference. Often mental schema has been proposed to be the framework for such phenomenon. In this study, using mice as a model animal, we show that schematic networks not only enhance the cognitive load handling capacity (CLHC) preventing catastrophic interference, but also enable the generation of novel responses that are context relevant. 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. Using these principles of mental schema that we discovered from our animal experiments, we develop a neurologically relevant ANN that avoids catastrophic interference and captures all the properties associated with different modes of learning tested in our experiments. The custom architecture of the ANN enables it to generate responses similar to animals in novel scenario.