No generalization from training on a SAM-detection task to a SAM-rate discrimination task with different depths

Forfattere

  • Liping Zhang Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK
  • Friederike Schlaghecken Department of Psychology, University of Warwick, Coventry, UK
  • James Harte Institute of Digital Healthcare, WMG, University of Warwick, Coventry, UK

Resumé

Information is carried in speech and sounds both in subtle amplitude and frequency variations over time. Hearing-impaired people have a reduced ability to detect these cues, particularly in challenging auditory environments. Any improvements in these perceptual tasks, through for example auditory training, could help to alleviate some of these difficulties. Practice can improve the detection threshold for amplitude modulation (AM) in sound stimuli. A recent study (Fitzgerald and Wright, 2010) demonstrated that AM-detection learning generalizes from trained to untrained AM rates, but not to a new task (rate discrimination). The present study investigated whether this lack of generalization was due to the use of 100% AM depth in the rate-discrimination task. The present study aims to investigate if it is possible to improve the generalization of sinusoidal amplitude modulation (SAM) detection to rate discrimination by using lower AM depths, such as 70% and 40%, in the discrimination task. The results from this study do not show generalization from SAM-detection to SAM-rate discrimination with any of the lower modulation depths.

Referencer

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Publiceret

2013-12-15

Citation/Eksport

Zhang, L., Schlaghecken, F., & Harte, J. (2013). No generalization from training on a SAM-detection task to a SAM-rate discrimination task with different depths. Proceedings of the International Symposium on Auditory and Audiological Research, 4, 61–67. Hentet fra https://proceedings.isaar.eu/index.php/isaarproc/article/view/2013-07

Nummer

Årg. 4 (2013): Auditory Plasticity - Listening with the Brain

Sektion

2013/1. Basic perceptual studies of training, learning, and generalization

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