Short-term adaptation to sound statistics is unimpaired in developmental dyslexia

Abstract

Developmental dyslexia is presumed to arise from phonological impairments. Accordingly, people with dyslexia show speech perception deficits taken as indication of impoverished phonological representations. However, the nature of speech perception deficits in those with dyslexia remains elusive. Specifically, there is no agreement as to whether speech perception deficits arise from speech-specific processing impairments, or from general auditory impairments that might be either specific to temporal processing or more general. Recent studies show that general auditory referents such as Long Term Average Spectrum (LTAS, the distribution of acoustic energy across the duration of a sound sequence) affect speech perception. Here we examine the impact of preceding target sounds’ LTAS on phoneme categorization to assess the nature of putative general auditory impairments associated with dyslexia. Dyslexic and typical listeners categorized speech targets varying perceptually from /ga/-/da/ preceded by speech and nonspeech tone contexts varying. Results revealed a spectrally contrastive influence of the preceding context LTAS on speech categorization, with a larger magnitude effect for nonspeech compared to speech precursors. Importantly, there was no difference in the presence or magnitude of the effects across dyslexia and control groups. These results demonstrate an aspect of general auditory processing that is spared in dyslexia, available to support phonemic processing when speech is presented in context.

Increased reliance on top-down information to compensate for reduced bottom-up use of acoustic cues in dyslexia

“…To date, there is no consensus on whether speech perception impairments in DD are restricted to speech materials or also affect the perception of non-speech sounds (Rosen & Manganari, 2001), as well as whether they occur only for s o u n d s t h a t a r e c u e d b y t e m p o r a l i n f o r m a t i o n (Vandermosten et al, 2010;Vandermosten et al, 2011). Furthermore, the majority of studies that investigated categorical perception in DD examined speech categorization in isolation (but see Holt, 2018, andGabay et al, 2019). In real-world listening environments listeners have been shown to rely on contextual information to disambiguate speech sounds.…”

“…When trained to adapt to degraded speech signals, typical listeners are able to learn to rely on higher-level top-down information (semantic and lexical knowledge) as well as low-level information (acoustic cues) to better adapt to distorted input (Banai & Lavner, 2012 ; Guediche et al, 2016 ). In typical listeners, the learning of distorted speech generalizes across stimuli that share high-level representations (new talker, same tokens) but also to new items that do not share high-level representations with the trained one (same talker, new tokens) (Banai & Lavner, 2012 , 2014 ; Gabay et al, 2017 ). By contrast, for individuals with DD, such generalization is confined to situations in which trained and untrained information shares the same high-level top-down information (new talker, same tokens) (Gabay et al, 2017 ) but is not observed in situations in which only low-level sub-lexical cues are shared between the trained and untrained information (same talker, new tokens) (Gabay et al, 2017 ; Gabay & Holt, 2021 ).…”

Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories

“…The change in /ada/ effectively leads to category boundary shifts in acoustic continua, boundary shifts that are transient and will only add-up after consistent repetitions. Importantly, these effects are distinct from perceptual bias and specific adaptation, and from other documented short-term effects due to changes in the relative informational content of partially redundant acoustic features (Heald, Van Hedger, and Nusbaum 2017;Gabay and Holt 2018).…”

Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories

“…The model was motivated by experimental results showing that /aba/ sounds were more frequently mis-categorized as 'ada' when they were preceded by a fused McGurk (Lüttke et al 2016). Since the reported effect was distinct from other well-documented across-trial dependency effects, such as perceptual priming or selective adaptation (Heald, Van Hedger, and Nusbaum 2017;Gabay and Holt 2018), we sought to model it considering only changes in perceived speech categories without external feedback. Like previous approaches, ours builds on the idea that the brain achieves perception by inverting a generative model (e.g., Rao and Ballard 1999;Knill and Pouget 2004;Karl J. Friston 2005) and by continuously monitoring its performance to adapt it to changing stimulus landscapes.…”