Natural language instructions induce compositional generalization in networks of neurons

This study leverages natural language processing advancements to model the human ability to understand instructions for novel tasks and to verbally describe a learned task. Neural network models, particularly RNNs augmented with pretrained language models like SBERT, demonstrated the capability to interpret and generate natural language instructions, thus allowing these networks to generalise learning across tasks with 83% accuracy. This modeling approach predicts neural representations that might be observed in human brains when integrating linguistic and sensorimotor skills. It also highlights how language can structure knowledge in the brain, facilitating general cognition.

Visit Original Article →