Abstract
Individuals use semantic expectancy – applying conceptual and linguistic knowledge to speech input – to improve the accuracy and speed of language comprehension. This study tested how adults use semantic expectancy in quiet and in the presence of speech-shaped broadband noise at -7 and -12 dB signal-to-noise ratio. Twenty-four adults (22.1 ± 3.6 years, mean ±SD) were tested on a four-alternative-forced-choice task whereby they listened to sentences and were instructed to select an image matching the sentence-final word. The semantic expectancy of the sentences was unrelated to (neutral), congruent with, or conflicting with the acoustic target. Congruent expectancy improved accuracy and conflicting expectancy decreased accuracy relative to neutral, consistent with a theory where expectancy shifts beliefs toward likely words and away from unlikely words. Additionally, there were no significant interactions of expectancy and noise level when analyzed in log-odds, supporting the predictions of ideal observer models of speech perception.
Highlights
two theories of how semantic expectancy affects identification of subsequent words using a speech in noise paradigm
The results supported the belief shift theory predicted by ideal observer models of speech perception
which suggests that semantic expectancy works by biasing responses
Summary
Everyday conversations require listeners to rapidly comprehend language as speech input unfolds over time. Individuals must match the spectrotemporal aspects of the speech input to lexical representations stored in long-term memory. As individuals process language incrementally, they continuously build up contextual representations and use that context to build up semantic expectancy to help them identify subsequent words (Repp, 1982; Kamide, 2008; Rigler et al, 2015). This strategy can be helpful for processing language when the speech input is degraded (Rönnberg et al, 2013; Winn, 2016). Semantic expectancy overcomes this reduced spectral fidelity by constraining the set of likely word candidates based on the preceding
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