Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior. In this paper, we apply repetition priming to 15 models across 5 families (1.5B--14B parameters) in two tasks (semantic categorization and cloze completion), with matched human experiments using identical stimuli. Our findings reveal that: (1) Base models exhibit automatic processing, showing immediate facilitation that is stable across lags, partially survives context removal, and directly correlates with attention to prior occurrences; (2) Instruct models exhibit controlled processing, showing facilitation that decays with lag, collapses without expected context, and at larger scales reverses to interference; (3) The dissociation widens monotonically with scale within the Qwen~2.5 family (1.5B--14B), suggesting that post-training progressively suppresses automatic facilitation; (4) Humans show a hybrid profile that is lag-sensitive like instruct models but uniformly facilitative like base models, never reversing to interference. Our study demonstrates that the post-training pipeline is associated with a qualitative shift in how models process repeated information, provides mechanistic evidence for where the two modes diverge, and reveals that human cognition occupies a middle ground that neither model type captures.