Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, Japan.
Sci Rep. 2019 Jul 22;9(1):10539. doi: 10.1038/s41598-019-46907-0.
The ultrasonic vocalizations of rats can transmit affective states to listeners. For example, rats typically produce shorter calls in a higher frequency range in social situations (pleasant call: PC), whereas they emit longer calls with lower frequency in distress situations (distress call: DC). Knowing what acoustical features contribute to auditory discrimination between these two calls will help to better characterize auditory perception of vocalized sounds in rats. In turn, this could lead to better estimation of models for processing vocalizations in sensory systems in general. Here, using an operant discrimination procedure, we examined the impact of various acoustical features on discriminating emotional ultrasonic vocalizations. We did this by systematically swapping three features (frequency range, time duration, and residual frequency-modulation pattern) between two emotional calls. After rats were trained to discriminate between PC and DC, we presented probe stimuli that were synthesized calls with one or two acoustical features swapped, and examined if the rats judged these calls as either PC or DC. The results revealed that all features were important for discrimination between the two call types, but frequency range provided the most information for discrimination. This supports the hypothesis that while rats utilize all acoustical features to perceive emotional vocalizations, they considerably rely on frequency cues.
大鼠的超声波发声可以将情感状态传递给听众。例如,大鼠在社交情境中通常会发出较短、高频的叫声(愉快叫声:PC),而在痛苦情境中会发出较长、低频的叫声(痛苦叫声:DC)。了解哪些声学特征有助于区分这两种叫声,将有助于更好地表征大鼠对发声声音的听觉感知。反过来,这也可以更好地估计一般感官系统中对发声处理的模型。在这里,我们使用操作性辨别程序,研究了各种声学特征对辨别情绪性超声波发声的影响。我们通过系统地在两种情绪叫声之间交换三个特征(频率范围、持续时间和剩余频率调制模式)来实现这一点。在大鼠被训练区分 PC 和 DC 之后,我们呈现了合成的探针刺激,这些刺激是具有一个或两个声学特征交换的叫声,并检查大鼠是否将这些叫声判断为 PC 或 DC。结果表明,所有特征对于区分两种叫声类型都很重要,但频率范围提供了最多的辨别信息。这支持了这样一种假设,即大鼠虽然利用所有声学特征来感知情绪性发声,但它们非常依赖频率线索。