Stansbury Amanda L, de Freitas Mafalda, Wu Gi-Mick, Janik Vincent M
Sea Mammal Research Unit, School of Biology, University of St. Andrews.
J Comp Psychol. 2015 Nov;129(4):412-20. doi: 10.1037/a0039756. Epub 2015 Oct 12.
Past researchers have found that gray seals (Halichoerus grypus) are capable of classifying vocal signals by call type using a trained set, but were unable to generalize to novel exemplars (Shapiro, Slater, & Janik, 2004). Given the importance of auditory categorization in communication, it would be surprising if the animals were unable to generalize acoustically similar calls into classes. Here, we trained a juvenile gray seal to discriminate novel calls into 2 classes, "growls" and "moans," by vocally matching call types (i.e., the seal moaned when played a moan and growled when played a growl). Our method differed from the previous study as we trained the animal using a comparatively large set of exemplars with standardized durations, consisting of both the seal's own calls and those of 2 other seals. The seal successfully discriminated growls and moans for both her own (94% correct choices) and the other seals' (87% correct choices) calls. We used a generalized linear model (GLM) and found that the seal's performance significantly improved across test sessions, and that accuracy was higher during the first presentation of a sound from her own repertoire but decreased after multiple exposures. This pattern was not found for calls from unknown seals. Factor analysis for mixed data (FAMD) identified acoustic parameters that could be used to discriminate between call types and individuals. Growls and moans differed in noise, duration and frequency parameters, whereas individuals differed only in frequency. These data suggest that the seal could have gained information about both call type and caller identity using frequency cues.
过去的研究人员发现,灰海豹(Halichoerus grypus)能够使用一组经过训练的信号,根据叫声类型对声音信号进行分类,但无法将其推广到新的样本中(夏皮罗、斯莱特和亚尼克,2004年)。鉴于听觉分类在交流中的重要性,如果动物无法将声学上相似的叫声归为一类,那将令人惊讶。在这里,我们训练了一只幼年灰海豹,通过声音匹配叫声类型,将新的叫声分为“咆哮”和“呻吟”两类(即播放呻吟声时海豹发出呻吟,播放咆哮声时海豹发出咆哮)。我们的方法与之前的研究不同,因为我们使用了一组相对较大的、具有标准化时长的样本对动物进行训练,这些样本包括海豹自己的叫声以及另外两只海豹的叫声。这只海豹成功地区分了自己的叫声(正确选择率为94%)和其他海豹的叫声(正确选择率为87%)中的咆哮声和呻吟声。我们使用了广义线性模型(GLM),发现这只海豹在测试过程中的表现显著提高,并且在首次播放其自身全部叫声中的声音时准确率更高,但在多次接触后准确率下降。对于来自未知海豹的叫声,没有发现这种模式。混合数据因子分析(FAMD)确定了可用于区分叫声类型和个体的声学参数。咆哮声和呻吟声在噪声、时长和频率参数方面存在差异,而个体之间仅在频率方面存在差异。这些数据表明,这只海豹可能利用频率线索获得了关于叫声类型和叫声者身份的信息。