Categorical speech content can often be perceived directlyfrom continuous auditory cues in the speech stream, buthuman-level performance on speech recognition tasksrequires compensation for contextual variables like speakeridentity. Regression modeling by McMurray and Jongman(2011) has suggested that for many fricative phonemes, acompensation scheme can substantially increasecategorization accuracy beyond even the information from 24un-compensated raw speech cues. Here, we simulate thesame dataset instead using a neurally rather than abstractlyimplemented model: a hybrid dynamic neural field model andconnectionist network. Our model achieved slightly loweraccuracy than McMurray and Jongman’s but similar accuracypatterns across most fricatives. Results also comparedsimilarly to more recent models that were also less neurallyinstantiated but somewhat closer fitting to humans inaccuracy. An even less abstracted model is an immediatefuture goal, as is expanding the present model to additionalsensory modalities and constancy/compensation effects.

The similarity between objects is judged in a wide variety of contexts from visual search to categorization to face recognition. There is a correspondingly rich history of similarity research and many known behavioral trends and models of similarity. Nevertheless, most similarity behaviors have been identified and tested only in a comparatively narrow set of unique contexts. This leaves open the question of the extent to which similarity judgments rely on common processes or resources and the specific nature of those processes if so. We tested three diverse yet well-established measures of object similarity using identical, psychometrically controlled stimuli and identical analyses across tasks. We found several consistent behavioral effects across tasks that provide clues as to the nature of task-general similarity processes and serve as diagnostic targets for computational models of similarity