Speech perception requires inferring category membership from varied acoustic cues, with listeners adeptly adjusting cue utilization upon encountering novel speech inputs. This adaptivity has been examined through the dimension-based statistical learning (DBSL) paradigm, which reveals that listeners can quickly de-emphasize secondary cues when cue correlations deviate from long-term expectations, resulting in cue-reweighting. Although multiple accounts of cue-reweighting have been proposed, direct comparisons of these accounts against human perceptual data are scarce. This study evaluates three computational models–cue normalization, Bayesian ideal adaptor, and error-driven learning–against classic DBSL findings to elucidate how cue reweighting supports adaptation to new speech patterns. These models differ in how they map cues onto categories for categorization and in how recent exposure to atypical input patterns influences this mapping. Our results show that both the error-driven learning and ideal adaptor models effectively capture the key patterns of cue-reweighting phenomena, whereas prelinguistic cue normalization does not. This comparison not only highlights the models' relative efficacy but also advances our understanding of the dynamic processes underlying speech perception adaptation.