Commentary on "Interaction in Spoken Word Recognition Models"

Disentangling the Role of Biphone Probability From Neighborhood Density in the Perception of Nonwords

In six experiments we explored how biphone probability and lexical neighborhood density influence listeners' categorization of vowels embedded in nonword sequences. We found independent effects of each. Listeners shifted categorization of a phonetic continuum to create a higher probability sequence, even when neighborhood density was controlled. Similarly, listeners shifted categorization to create a nonword from a denser neighborhood, even when biphone probability was controlled. Next, using a visual world eye-tracking task, we determined that biphone probability information is used rapidly by listeners in perception. In contrast, task complexity and irrelevant variability in the stimuli interfere with neighborhood density effects. These results support a model in which both biphone probability and neighborhood density independently affect word recognition, but only biphone probability effects are observed early in processing.

Contra assertions, feedback improves word recognition: How feedback and lateral inhibition sharpen signals over noise

Whether top-down feedback modulates perception has deep implications for cognitive theories. Debate has been vigorous in the domain of spoken word recognition, where competing computational models and agreement on at least one diagnostic experimental paradigm suggest that the debate may eventually be resolvable. Norris and Cutler (2021) revisit arguments against lexical feedback in spoken word recognition models. They also incorrectly claim that recent computational demonstrations that feedback promotes accuracy and speed under noise (Magnuson et al., 2018) were due to the use of the Luce choice rule rather than adding noise to inputs (noise was in fact added directly to inputs). They also claim that feedback cannot improve word recognition because feedback cannot distinguish signal from noise. We have two goals in this paper. First, we correct the record about the simulations of Magnuson et al. (2018). Second, we explain how interactive activation models selectively sharpen signals via joint effects of feedback and lateral inhibition that boost lexically-coherent sublexical patterns over noise. We also review a growing body of behavioral and neural results consistent with feedback and inconsistent with autonomous (non-feedback) architectures, and conclude that parsimony supports feedback. We close by discussing the potential for synergy between autonomous and interactive approaches.

Lexically Mediated Compensation for Coarticulation Still as Elusive as a White Christmash

Luthra, Peraza-Santiago, Beeson, Saltzman, Crinnion, and Magnuson (2021) present data from the lexically mediated compensation for coarticulation paradigm that they claim provides conclusive evidence in favor of top-down processing in speech perception. We argue here that this evidence does not support that conclusion. The findings are open to alternative explanations, and we give data in support of one of them (that there is an acoustic confound in the materials). Lexically mediated compensation for coarticulation thus remains elusive, while prior data from the paradigm instead challenge the idea that there is top-down processing in online speech recognition.

Probabilistic modeling of orthographic learning based on visuo-attentional dynamics

How is orthographic knowledge acquired? In line with the self-teaching hypothesis, most computational models assume that phonological recoding has a pivotal role in orthographic learning. However, these models make simplifying assumptions on the mechanisms involved in visuo-orthographic processing. Against evidence from eye movement data during orthographic learning, they assume that orthographic information on novel words is immediately available and accurately encoded after a single exposure. In this paper, we describe BRAID-Learn, a new computational model of orthographic learning. BRAID-Learn is a probabilistic and hierarchical model that incorporates the mechanisms of visual acuity, lateral interference, and visual attention involved in word recognition. Orthographic learning in the model rests on three main mechanisms: first, visual attention moves over the input string to optimize the gain of information on letter identity at each fixation; second, top-down lexical influence is modulated as a function of stimulus familiarity; third, after exploration, perceived information is used to create a new orthographic representation or stabilize a better-specified representation of the input word. BRAID-Learn was challenged on its capacity to simulate the eye movement patterns reported in humans during incidental orthographic learning. In line with the behavioral data, the model predicts a larger decline with exposures in number of fixations and processing time for novel words than for known words. For novel words, most changes occur between the first and second exposure, that is to say, after creation in memory of a new orthographic representation. Beyond phonological recoding, our results suggest that visuo-attentional exploration is an intrinsic portion of orthographic learning seldom taken into consideration by models or theoretical accounts.

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

A long-standing question in cognitive science is how high-level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top-down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes-capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. Because this phoneme-to-phoneme compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would constitute strong support for top-down interaction. However, results from previous LCfC studies have been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous context-final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for coarticulation for a subsequent target phoneme. We observed robust LCfC in a well-powered, preregistered experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40). These results provide strong evidence in favor of computational models of spoken word recognition that include top-down feedback.