Musical training in younger years can prevent the decay in speech listening skills in later life, according to a study conducted at Rotman Research Institute (RRI) at Baycrest Health Sciences. The study indicated that older adults who had musical training in their youth were 20 percent faster in identifying speech sounds than their non-musician peers on speech identification tests.
20 healthy older adults aged 55-75 years, 10 musicians and 10 non-musicians were part of the study. They were asked to put on headphones in a controlled lab setting and were asked to identify random speech sounds. Some of the sounds were single vowel sounds like an 'ooo' or an 'ahhh', others more ambiguous as a mix of two sounds that posed a greater challenge to their auditory processing abilities for categorizing the speech sound correctly.
AdvertisementResearchers recorded the neural activity of each participant using electroencephalography (EEG) during these testing cycles. EEG measures to a very precise degree the exact timing of the electrical activity which occurs in the brain in response to external stimuli. This is displayed as waveforms on the computer screen. This technology can be used to study how the brain makes sense of our complex acoustical environment and how aging impacts cognitive functions.
Study lead Gavin Bidelman said, "Musical activities are an engaging form of cognitive brain training and we are now seeing robust evidence of brain plasticity from musical training not just in younger brains, but in older brains too. In our study we were able to predict how well older people classify or identify speech using EEG imaging. We saw a brain-behavior response that was two to three times better in the older musicians compared to non-musicians peers. In other words, old musicians' brains provide a much more detailed, clean and accurate depiction of the speech signal, which is likely why they are much more sensitive and better at understanding speech."
The study has been published in The Journal of Neuroscience.
You May Also Like