A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

Reading acquisition primarily relies on orthographic learning. Behavioral studies show that familiarity with a novel word's pronunciation facilitates learning, particularly in semantically meaningful contexts. Two main components of orthographic learning are commonly described: perceptual processing of the visual stimulus, to infer corresponding phonological rep- resentations, and “pronunciation correction”, to correct errors from perceptual processing. Currently, pronunciation correc- tion has not been featured in reading acquisition computa- tional models. This study uses BRAID-Acq, a reading ac- quisition model, to implement and compare two pronunciation correction mechanisms (an “online” and a “post-processing” variant). We simulated learning of words with and without prior phonological knowledge and explored the impact of con- text strength and size on learning. Results indicate that both mechanisms improve decoding. However, the post-processing mechanism induced implausible lexicalization for words with- out prior phonological knowledge, while the online mecha- nism did not. Overall, our simulation results suggest that pro- nunciation correction could be construed as an online process.