Conference on Cognitive Computational Neuroscience

Recent evidence suggests that readers optimize low-level visual information following the principles of predictive coding. Based on a transparent neurocognitive model, we postulated that redundant visual signals are removed, allowing readers to focus on the informative aspects of the visual percept, i.e., the orthographic prediction error (oPE). Here we test alternative oPE implementations by assuming all-or-nothing signaling units based on multi- ple thresholds (i.e., output modality of a neuron). Further, we tested if predictions are signaled from one or multiple units. For model evaluation, we compared the new oPEs with each other and against the original formulation based on behavioral and electrophysiological data (EEG at 230, 430 ms). We found the highest model fit for the oPE with a 50% threshold integrating multiple prediction units for behavior and the late EEG data. The early EEG data was still explained best by the original hypothesis. Thus, the new formulation is adequate for late but not early neuronal signals indicating that the representation, which likely implements lexical access, changes over time.