Many morphs: Parsing gesture signals from the noise.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Behavior Research Methods Pub Date : 2024-10-01 Epub Date: 2024-03-04 DOI:10.3758/s13428-024-02368-6
Alexander Mielke, Gal Badihi, Kirsty E Graham, Charlotte Grund, Chie Hashimoto, Alex K Piel, Alexandra Safryghin, Katie E Slocombe, Fiona Stewart, Claudia Wilke, Klaus Zuberbühler, Catherine Hobaiter
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引用次数: 0

Abstract

Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top-down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing detailed features of each signal and clustering cases based on patterns of similarity in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data become available. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across, for example, species, populations, and levels of signal granularity. Doing so, we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.

Abstract Image

许多变形从噪音中解析手势信号
从噪音中解析信号是信号发出者和接收者以及研究交流系统的研究人员面临的一个普遍问题。在比较其他物种如何编码信息和意义以及如何构建信号系统方面,人们投入了大量的精力。然而,研究工作有赖于识别和区分代表有意义分析单位的信号。早期的方法是采用自上而下的方法来定义信号库,将案例划分为预定义的信号类型。最近,劳动密集型方法采用了自下而上的方法,描述了每个信号的详细特征,并根据多维特征空间中的相似性模式对病例进行了聚类,而这在以前是无法检测到的。尽管如此,从使用物种的角度来看,评估所得到的重演集是否由相关单元组成,并在获得更多数据时重新定义重演集仍然是至关重要的。在本文中,我们提供了一个框架,该框架从目前可用的最大野生黑猩猩(Pan troglodytes)手势集中获取数据,根据使用潜类分析(一种基于模型的分类变量聚类检测算法)修改手势表达的特征,在精细尺度上拆分手势类型,然后确定这一拆分过程是否减少了手势目标或群体的不确定性。我们的方法允许将不同的兴趣特征纳入拆分过程,从而为未来提供了很大的灵活性,例如跨越物种、种群和信号粒度水平。这样,我们就提供了一个强大的工具,让对手势交流感兴趣的研究人员能够建立相关单元的汇集,以便在交流系统内部和系统之间进行后续分析。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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