Traditional studies of human categorization often treat the processes of encoding features and cues as peripheral to the question of how stimuli are categorized. However, in domains where the features and cues are less transparent, how information is encoded prior to categorization may constrain our understanding of the architecture of categorization. This is particularly true in speech perception, where acoustic cues to phonological categories are ambiguous and influenced by multiple factors. Here, it is crucial to consider the joint contributions of the information in the input and the categorization architecture. We contrasted accounts that argue for raw acoustic information encoding with accounts that posit that cues are encoded relative to expectations, and investigated how two categorization architectures—exemplar models and back-propagation parallel distributed processing models—deal with each kind of information. Relative encoding, akin to predictive coding, is a form of noise reduction, so it can be expected to improve model accuracy; however, like predictive coding, the use of relative encoding in speech perception by humans is controversial, so results are compared to patterns of human performance, rather than on the basis of overall accuracy. We found that, for both classes of models, in the vast majority of parameter settings, relative cues greatly helped the models approximate human performance. This suggests that expectation-relative processing is a crucial precursor step in phoneme categorization, and that understanding the information content is essential to understanding categorization processes.
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