Chronometric studies of language and memory processing typically emphasize changes in mean response time (RT) performance across conditions. However, changes in mean performance (or the lack thereof) may reflect distinct patterns at the level of underlying RT distributions. In seven experiments, RT distributional analyses were used to better understand how distributions change across related and unrelated conditions in standard semantic priming paradigms. In contrast to most other lexical variables, semantic priming in standard conditions simply shifts the RT distribution, implicating a headstart mechanism. However, when targets are degraded, the priming effect increases across the RT distribution, a pattern more consistent with current computational models of semantic priming. Interestingly, priming effects also increase across the RT distribution when targets are degraded and primes are highly masked, supporting a memory retrieval account of priming under degraded conditions. Finally, strengths and limitations of alternative approaches for modeling RT distributions are discussed. (Contains 13 figures and 8 tables.)