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Watson, S. K.

Publications and source records attributed to Watson, S. K..

2 recordsLinked to original sources

Domestic goats can follow the direction of human voices to solve a hidden-object task

The capacity for animals to produce and comprehend vocalisations that provide referential acoustic cues to their eliciting cause (e.g. predator-specific alarm calls and food calls) is a highly adaptive means of maximising the benefits of group living, and has been widely studied in diverse species. However, an underexplored dimension of referentiality is the ability to process the direction in which a vocalisation is emitted as a cue towards its referent. Here, we replicated an experimental design previously applied to dogs, chimpanzees and human infants to investigate whether domestic goats (Capra hircus) can use human voices as a directional cue in a hidden-object task. Twenty-nine goats from a UK sanctuary participated in three experimental conditions. In each condition, goats were individually presented with a human experimenter obscured by a barrier, and two identical containers, one of which was baited with food. In the reward directed speech condition, the experimenter vocalised excitedly towards the baited container while sitting closer to the un-baited container, and then the goat was able to select which container to explore. While substantial inter-individual variation existed, on average, subjects chose the baited container at above chance level across the four trials. Two control conditions explored alternative explanations for this result: a "no speech" condition, in which the experimenter was silent but remained in the same location as in the test condition, and a "non-reward directed speech" condition, in which the experimenter directed their voice away from both containers. Subjects showed no evidence of choosing the baited container at above chance level in either of these control conditions. We conclude that goats, like dogs, but not chimpanzees, are capable of attending to the directional cues provided by human voices, and discuss the possible role of domestication in the taxonomic distribution of this ability.

animal behavior and cognition↗

Same data, different results? Evaluating machine learning approaches for individual identification in animal vocalisations

Automated acoustic analysis is increasingly used in behavioural ecology, and determining caller identity is a key element for many investigations. However, variability in feature extraction and classification methods limits the comparability of results across species and studies, constraining conclusions we can draw about the ecology and evolution of the groups under study. We investigated the impact of using different feature extraction (spectro-temporal measurements, linear and Mel-frequency cepstral coefficients, as well as highly comparative time-series analysis) and classification methods (discriminant function analysis, neural networks, random forests, and support vector machines) on the consistency of caller identity classification accuracy across 16 mammalian datasets. We found that Mel-frequency cepstral coefficients and random forests yield consistently reliable results across datasets, facilitating a standardised approach across species that generates directly comparable data. These findings remained consistent across vocalisation sample sizes and number of individuals considered. We offer guidelines for processing and analysing mammalian vocalisations, fostering greater comparability, and advancing our understanding of the evolutionary significance of acoustic communication in diverse mammalian species.

animal behavior and cognition↗