The effect of timbre on verbal identification performance in Turkish makam music

This study investigates the influence of instrument timbre on the categorical learning and verbal identification of Turkish makams. In an experimental design, 37 secondary school students (with no formal training in makam music) were taught two makams, Hüseynî and Saba, through a standardized ear-training program totaling 220 minutes. During training, all melodic examples were presented using instruments from the bağlama family (cura, bağlama, divan sazı) to establish a consistent timbral context. A pre-test/post-test procedure measured participants' ability to identify makams in novel contexts. Specifically, before and after training, students were asked to identify Saba and Hüseynî makam excerpts played on both the familiar bağlama-family timbres and on different instrument timbres (piano, oud, kanun, etc.). The stimuli also varied in motif repetition levels (low, medium, high) to examine the effect of exposure frequency on learning. Results showed a significant improvement in post-test identification accuracy for both makams following training (p < .001). Crucially, this improvement was much larger for training-specific timbres: participants recognized makams most accurately when heard in the timbre of the bağlama-family instruments used during instruction, whereas recognition in non-trained timbres increased only modestly. Variation in motif repetition did not yield a significant overall effect on learning, although all repetition groups experienced substantial pre-to-post gains. These findings indicate that timbre is an integral component of how novices mentally represent and recall musical modes. Theoretically, the study provides the first empirical evidence that timbral cues shape the cognitive schemata of makam categories, extending music cognition models beyond pitch and melody cues. Practically, the outcomes suggest that ear-training curricula and automated makam recognition systems should incorporate multiple instrument timbres to ensure robust, generalized learning and performance.