Human listeners rapidly adapt to unfamiliar talkers, but the underlying computational mechanisms remain contested. Three candidate hypotheses—pre-linguistic normalization, changes in phonetic category representations, and changing decision biases—have largely been pursued in separation, using subfield-specific paradigms. Researchers working in these paradigms often assume that adaptivity observed in their particular paradigm can only be explained by one of the three mechanisms. We test this assumption for one of the most popular experimental paradigms (lexically-guided perceptual learning or LGPL) using a unified computational framework (ASP). We apply ASP to the largest existing LGPL data: 89,600 categorization responses from over 1000 listeners after lexically-guided exposure to 32 different stimulus sets. Despite the unprecedented scale of these data, we find that behavioral data are equally compatible with all three candidate mechanisms. We discuss how model-guided stimulus selection can increase the diagnosticity of future LGPL experiments. Our simulation code can easily be adapted to other experimental paradigms.