Predicting Speech-in-Noise Recognition From Performance on the Trail Making Test

Ellis, Rachel J.; Molander, Peter; Rönnberg, Jerker; Lyxell, Björn; Andersson, Gerhard; Lunner, Thomas · 2015 · OpenAlex-citations

DOI: 10.1097/aud.0000000000000218

archive: archived pipeline: cataloged verified

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Summary

This study investigates whether performance on the Trail Making Test (TMT), a measure of cognitive processing speed and executive function, can predict speech-in-noise recognition abilities. Motivated by the established link between cognitive skills and speech perception, particularly under degraded listening conditions, the authors sought to validate the utility of an internet-administered TMT for screening speech-recognition difficulties. The research addresses the need for accessible, efficient tools to assess the cognitive factors influencing hearing, moving beyond complex working memory tests to simpler, widely used cognitive metrics. The researchers conducted a large-scale internet study involving 1,509 Swedish-speaking adults aged 18 to 91. Participants completed computerized versions of the TMT-A (numerical sequencing, indexing processing speed) and TMT-B (alternating numerical-alphabetical sequencing, indexing executive control and task switching), as well as an adaptive speech-in-noise recognition test. The speech test presented disyllabic words in speech-shaped noise, using a 1-up-1-down adaptive procedure to determine the signal-to-noise ratio (SNR) required for 50% correct identification. The internet-based TMT modified the traditional paper-and-pencil format by randomizing point configurations and equalizing trail lengths between parts A and B to control for motor speed confounds. Data were analyzed using correlational and multiple regression techniques, with age controlled for in partial correlations. The results demonstrated significant positive correlations between TMT performance and speech-in-noise recognition. Specifically, faster completion times on both TMT-A and TMT-B were associated with better speech recognition scores (lower SNR thresholds). These associations remained significant even after controlling for age, which was the strongest predictor in the model. A multiple regression analysis including age, TMT-A, and TMT-B scores explained 27.4% of the variance in speech-in-noise performance. Notably, both the simple TMT-A and the complex TMT-B contributed significantly to the prediction model, suggesting that processing speed is as relevant as executive function in this context. Additionally, 38% of participants exhibited speech-in-noise scores indicative of hearing loss. The findings conclude that the TMT is a valid and promising tool for predicting speech-in-noise recognition in adults, regardless of hearing status. The study validates the use of internet-based administration for cognitive and auditory screening, offering a cost-effective method for large-scale assessment. The results imply that speech-in-noise difficulties are linked to both basic processing speed and higher-order cognitive abilities. This supports the potential clinical application of the TMT in audiology for assessing cognitive contributions to hearing impairment and may facilitate earlier detection of hearing or cognitive issues through accessible online screening tools.

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• cognitive capacity variation