Music-reading training alleviates crowding with musical notation
43.415, Monday, 19-May, 8:30 am - 12:30 pm, Banyan Breezeway
Yetta Kwailing Wong1, Alan C.-N. Wong2; 1City University of Hong Kong, 2The Chinese University of Hong Kong
Crowding refers to the phenomenon in which recognition of a target object is disrupted by nearby distractors. Prior studies observed smaller crowding for perceptual experts in a number of object categories, such as faces (Louie et al., 2007), letters (Grainger, Tydgat & Issele, 2010) and musical notation (Wong & Gauthier, 2012). However, since experience was not manipulated in these studies, it was unclear whether these real-world experts happened to have better visuospatial resolution for the objects of expertise independent of perceptual experience. We tested whether crowding can be alleviated by music-reading training in the laboratory. Participants with intermediate music-reading ability completed eight hours of music-reading training within two weeks. In the match-to-sample training task, a target music sequence with four to five notes appeared briefly, followed by two sequences, one identical to the target and the other slightly different. Participants were required to identify the target sequence. The presentation duration of the sequence decreased if participants attained 90% accuracy in a block of 20 trials. After training, the average presentation duration was ten times shorter than that in session one. Crowding with musical notation was measured before and after training. For the baseline condition, participants judged whether a dot was on or off a line in the parafoveal region. For the crowded condition, four additional staff lines and two flanking dots were added around the target dot. After training, the Weber contrast threshold for 75% accuracy significantly decreased for the crowded condition but not for the baseline condition, and that for Landolt Cs stayed similar across training. Results show that crowding can be reduced with typical perceptual expertise training paradigms that refine high-level object representation, and it can be achieved without direct practice on the crowding task (e.g., Chung, 2007; Huckauf & Nazir, 2007; Sun et al., 2010).