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.
Table 1.
Neural coding of consonants in noise predicts preschooler’s phonological processing.
These model parameters are applied in Experiments 2–4.
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.
Table 2.
Behavioral test battery for each experiment.