Search for: All records

We recently reported strong, replicable (i.e., replicated) evidence forlexically mediated compensation for coarticulation(LCfC; Luthra et al., 2021), whereby lexical knowledge influences a prelexical process. Critically, evidence for LCfC provides robust support forinteractivemodels of cognition that includetop‐down feedbackand is inconsistent withautonomousmodels that allow only feedforward processing. McQueen, Jesse, and Mitterer (2023) offer five counter‐arguments against our interpretation; we respond to each of those arguments here and conclude that top‐down feedback provides the most parsimonious explanation of extant data.

 

Listeners have many sources of information available in interpreting speech. Numerous theoretical frameworks and paradigms have established that various constraints impact the processing of speech sounds, but it remains unclear how listeners might simultane-ously consider multiple cues, especially those that differ qualitatively (i.e., with respect to timing and/or modality) or quantita-tively (i.e., with respect to cue reliability). Here, we establish that cross-modal identity priming can influence the interpretation of ambiguous phonemes (Exp. 1, N = 40) and show that two qualitatively distinct cues – namely, cross-modal identity priming and auditory co-articulatory context – have additive effects on phoneme identification (Exp. 2, N = 40). However, we find no effect of quantitative variation in a cue – specifically, changes in the reliability of the priming cue did not influence phoneme identification (Exp. 3a, N = 40; Exp. 3b, N = 40). Overall, we find that qualitatively distinct cues can additively influence phoneme identifica-tion. While many existing theoretical frameworks address constraint integration to some degree, our results provide a step towards understanding how information that differs in both timing and modality is integrated in online speech perception. 

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. 

Over the past two years, face masks have been a critical tool for preventing the spread of COVID-19. While previous studies have examined the effects of masks on speech recognition, much of this work was conducted early in the pandemic. Given that human listeners are able to adapt to a wide variety of novel contexts in speech perception, an open question concerns the extent to which listeners have adapted to masked speech during the pandemic. In order to evaluate this, we replicated Toscano and Toscano (PLOS ONE 16(2):e0246842, 2021), looking at the effects of several types of face masks on speech recognition in different levels of multi-talker babble noise. We also examined the effects of listeners’ self-reported frequency of encounters with masked speech and the effects of the implementation of public mask mandates on speech recognition. Overall, we found that listeners’ performance in the current experiment (with data collected in 2021) was similar to that of listeners in Toscano and Toscano (with data collected in 2020) and that performance did not differ based on mask experience. These findings suggest that listeners may have already adapted to masked speech by the time data were collected in 2020, are unable to adapt to masked speech, require additional context to be able to adapt, or that talkers also changed their productions over time. Implications for theories of perceptual learning in speech are discussed.