Contra assertions, feedback improves word recognition

Abstract

Norris and Cutler (in press) revisit their arguments that (lexical-to-sublexical) feedback cannot improve word recognition performance, based on the assumption that feedback must boost signal and noise equally. They also argue that demonstrations that feedback improves performance (Magnuson, Mirman, Luthra, Strauss, & Harris, 2018) in the TRACE model of spoken word recognition (McClelland & Elman, 1986) were artifacts of converting activations to response probabilities. We first evaluate their claim that feedback in an interactive activation model must boost noise and signal equally. This is not true in a fully interactive activation model such as TRACE, where the feedback signal does not simply mirror the feedforward signal; it is instead shaped by joint probabilities over lexical patterns, and the dynamics of lateral inhibition. Thus, even under high levels of noise, lexical feedback will selectively boost signal more than noise. We demonstrate that feedback promotes faster word recognition and preserves accuracy under noise whether one uses raw activations or response probabilities. We then document that lexical feedback selectively boosts signal (i.e., lexically-coherent series of phonemes) more than noise by tracking sublexical (phoneme) activations under noise with and without feedback. Thus, feedback in a model like TRACE does improve word recognition, exactly by selective reinforcement of lexically-coherent signal. We conclude that whether lexical feedback is integral to human speech processing is an empirical question, and briefly review a growing body of work at behavioral and neural levels that is consistent with feedback and inconsistent with autonomous (non-feedback) architectures.

Morphology in a Parallel, Distributed, Interactive Architecture of Language Production

“…Another point of comparison are classic interactive activation models of lexical processing (McClelland, 1981;Dell, 1985Dell, , 1986McClelland and Elman, 1986). These models share the notion of feedback with the present proposal, and remain useful today for modeling the consequences of feedback for processing (e.g., Martin, 2007;Nozari et al, 2011;Pinet and Nozari, 2018;Nozari, 2020;Falandays et al, 2021;Magnuson et al, 2021). Because many of these models share a particular architecture, these architectural properties have become associated with the term interactive activation.…”