Kathios Nicholas, Sachs Matthew E, Zhang Euan, Ou Yongtian, Loui Psyche
Department of Psychology, College of Science, Northeastern University.
Center for Science and Society, Columbia University.
Psychol Sci. 2024 Jan;35(1):34-54. doi: 10.1177/09567976231214185. Epub 2023 Nov 29.
Much of what we know and love about music hinges on our ability to make successful predictions, which appears to be an intrinsically rewarding process. Yet the exact process by which learned predictions become pleasurable is unclear. Here we created novel melodies in an alternative scale different from any established musical culture to show how musical preference is generated de novo. Across nine studies ( = 1,185), adult participants learned to like more frequently presented items that adhered to this rapidly learned structure, suggesting that exposure and prediction errors both affected self-report liking ratings. Learning trajectories varied by music-reward sensitivity but were similar for U.S. and Chinese participants. Furthermore, functional MRI activity in auditory areas reflected prediction errors, whereas functional connectivity between auditory and medial prefrontal regions reflected both exposure and prediction errors. Collectively, results support predictive coding as a cognitive mechanism by which new musical sounds become rewarding.
我们对音乐的诸多认知与喜爱都取决于我们做出成功预测的能力,而这似乎是一个本身就有回报的过程。然而,习得的预测如何变得令人愉悦的确切过程尚不清楚。在这里,我们用一种不同于任何既定音乐文化的替代音阶创作了新颖的旋律,以展示音乐偏好是如何从头产生的。在九项研究(N = 1185)中,成年参与者学会更喜欢那些遵循这种快速习得结构的更频繁呈现的项目,这表明接触和预测误差都影响了自我报告的喜好评分。学习轨迹因音乐奖励敏感性而异,但美国和中国参与者的学习轨迹相似。此外,听觉区域的功能磁共振成像活动反映了预测误差,而听觉与内侧前额叶区域之间的功能连接则反映了接触和预测误差。总体而言,研究结果支持预测编码作为一种认知机制,通过它新的音乐声音变得有回报。