Auditory Processing in Noise: A Preschool Biomarker for Literacy

Abstract

Learning to read is a fundamental developmental milestone, and achieving reading competency has lifelong consequences. Although literacy development proceeds smoothly for many children, a subset struggle with this learning process, creating a need to identify reliable biomarkers of a child's future literacy that could facilitate early diagnosis and access to crucial early interventions. Neural markers of reading skills have been identified in school-aged children and adults; many pertain to the precision of information processing in noise, but it is unknown whether these markers are present in pre-reading children. Here, in a series of experiments in 112 children (ages 3-14 y), we show brain-behavior relationships between the integrity of the neural coding of speech in noise and phonology. We harness these findings into a predictive model of preliteracy, revealing that a 30-min neurophysiological assessment predicts performance on multiple pre-reading tests and, one year later, predicts preschoolers' performance across multiple domains of emergent literacy. This same neural coding model predicts literacy and diagnosis of a learning disability in school-aged children. These findings offer new insight into the biological constraints on preliteracy during early childhood, suggesting that neural processing of consonants in noise is fundamental for language and reading development. Pragmatically, these findings open doors to early identification of children at risk for language learning problems; this early identification may in turn facilitate access to early interventions that could prevent a life spent struggling to read.

Fig 1. Overview of the auditory-neurophysiological biomarker and three derived neural measures.

(A) Recording paradigm: [da] is presented repeatedly over a continuous background track of nonsense sentences spoken by multiple talkers. (B) A time-domain average waveform of the response. The response shows many of the physical characteristics of the eliciting stimulus. The gray box highlights the time region of the response that corresponds to the consonant transition (the region of interest). (C) The peaks of interest are identified here with arrows. (D) A frequency domain representation of the grand average response to the consonant transition. (E) To illustrate the trial-by-trial stability measure, two representative subjects are shown. One pair of sub-averages each is shown for a subject with high stability and one with poor stability (right).

Fig 2

(A) In Year 1 (Experiment 1) each child’s score on the phonological processing test is plotted against the model’s predicted scores (n = 37). The two are highly correlated (r = 0.826, p < .001; when a correction is applied for the unreliability of the psychoeducational test, r = 0.870, p < .001). (B) A histogram of the error of estimation (the difference between a preschooler’s actual and predicted scores). For a majority of children, the model predicts scores within 2 points on the test. Please refer to the S1 Data for data underlying this figure.

Fig 3. In preschoolers (n = 34), model predictions of phonological processing in Year 1 (based on auditory neurophysiology) predict rapid automatized naming time in Year 2, with higher predicted scores correlating with faster naming times for objects and colors (r = -.663, p < .001).

Please refer to the S1 Data for data underlying this figure.