Are Humans Evolved Instruction Followers? An Underlying Inductive Bias Enables Rapid Instructed Task Learning
2026-06-29 • Computation and Language
Computation and Language
AI summaryⓘ
The authors explain that people can quickly learn new tasks just by reading or hearing instructions, a skill called rapid instructed task learning (RITL). They suggest this ability comes from an inborn bias shaped by evolution to understand and follow language instructions. This bias helps humans generalize behavior fast, similar to how AI language models perform new tasks without extra training by using instructions. The paper compares human and AI learning and encourages more research to connect these findings across fields.
rapid instructed task learninginductive biascognitive flexibilityinstruction tuninglarge language modelszero-shot learningnatural neural networksartificial neural networkscognitive architecturegeneralization
Authors
Anjishnu Kumar
Abstract
Human adults can often perform a novel task correctly on the first attempt after only receiving verbal or written instructions. This rapid instructed task learning (RITL) is a hallmark of human cognitive flexibility, yet its mechanisms and parallels in artificial systems remain under-explored across disciplines. In this position paper, we argue that humans possess an evolved instruction-following bias -- an inductive bias shaped by evolution to interpret and execute linguistic instructions which critically enables fast generalization of behavior from language. This bias functions analogously to the way large language models (LLMs) leverage instruction tuning to achieve zero-shot task performance. We synthesize evidence from cognitive science, neuroscience, and machine learning research to support this hypothesis. While instruction-following in AI is currently achieved via specialized training protocols, we posit that in humans it arises as an innate cognitive architecture feature. We outline testable predictions and call for more interdisciplinary research to investigate Instruction-Following as a unifying mechanism enabling rapid task learning in both natural and artificial neural networks.