Extending a Formal Model of Predictive Coding to Human Spoken Word Recognition

There is a general consensus in theories of human speech recognition that humans engage in predictive processing during online speech processing. There are also claims that predictive processing is indicative of the operation of a predictive coding (PC) mechanism. PC is a generative, hierarchical feedback framework where feedback signals consist of input predictions, while feedforward signals consist primarily of prediction errors (PE). Some researchers have taken decreased neural signals when inputs conform to expectations as evidence for PC and claim that other possible explanatory frameworks (e.g., interactive activation) are incompatible with reported reductions in PE. However, these claims have been advanced using narrow-scope computational implementations of PC without known abilities to adequately (i.e., plausibly) simulate broader human spoken word recognition (SWR) behavioral phenomena. Here, we present the first known neurally and behaviorally adequate, mathematically formal neural network PC model of time-dependent human SWR. After demonstrating that the new model is able to emulate a fundamental, empirical signature of human SWR behavior (time course of lexical activation and competition), we compare model dynamics to neural performance targets that have been touted as hallmarks of PC in SWR. While the new model readily exhibits predictive processing (anticipatory activation of phonemes consistent with lexical knowledge) and reduced neural activity when inputs match expectations (a necessary component of PC) more subtle patterns of neural activity under priming and noise conditions, previously proposed as diagnostic of PC do not emerge appreciably. This suggests that such patterns may not be hallmarks of PC. We discuss implications for PC-based theories of SWR.