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<p>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 learnings, 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.</p>

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Explicitly acquired interrelations among mental schema reduces cognitive load and facilitates emergence of novel responses in mice and artificial neural networks

  • 期刊论文

  • 发表日期：

    2026年3月22日

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  • Vikram Pal Singh,
  • Shruti Shridhar,
  • Shankanava Kundu,
  • Richa Bhatt,
  • Balaji Jayaprakash

摘要

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 learnings, 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.