Abstract
Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.
Highlights
Listeners’ ability to rapidly learn to understand unfamiliar speech conditions such as accented, disordered or noise-vocoded speech is impressive
In the first step of the analysis, we identified the maximal random slope structure of our models to allow for the fact that different participants or items may vary with regard to how sensitive they are with respect to the variables at hand (Cunnings, 2012; Barr et al, 2013): if, e.g., vocabulary knowledge only matters for the understanding of some sentences in the perceptual learning task but not for others, the effect of vocabulary should be modeled individually for each sentence and removed from the fixed effect structure
Several studies reported that older adults are sensitive to probabilistic sequences (Salthouse et al, 1999; Negash et al, 2003; Simon et al, 2011; Campbell et al, 2012) and found the ability to adapt to novel speech conditions to be preserved in older adults (Peelle and Wingfield, 2005; Golomb et al, 2007; Adank and Janse, 2010; Gordon-Salant et al, 2010)
Summary
Listeners’ ability to rapidly learn to understand unfamiliar speech conditions such as accented, disordered or noise-vocoded speech is impressive. When lower-level representations have been modified, performance under difficult conditions can be based on accessing these low-level representations This is illustrated by findings that adaptation to noise-vocoded speech generalizes to novel words (Hervais-Adelman et al, 2008), to non-words (Loebach et al, 2008) and to the recognition of environmental sounds (Loebach et al, 2009). These generalization findings suggest that perceptual learning in speech modifies representations at lower levels of the hierarchy, that is, representations at a sublexical level (Hervais-Adelman et al, 2008; Banai and Amitay, 2012)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
