Department of Clinical Veterinary Science, University of Bristol, Langford BS40 5DU, UK.
Proc Biol Sci. 2010 Oct 7;277(1696):2895-904. doi: 10.1098/rspb.2010.0303. Epub 2010 Aug 4.
A better understanding of animal emotion is an important goal in disciplines ranging from neuroscience to animal welfare science. The conscious experience of emotion cannot be assessed directly, but neural, behavioural and physiological indicators of emotion can be measured. Researchers have used these measures to characterize how animals respond to situations assumed to induce discrete emotional states (e.g. fear). While advancing our understanding of specific emotions, this discrete emotion approach lacks an overarching framework that can incorporate and integrate the wide range of possible emotional states. Dimensional approaches that conceptualize emotions in terms of universal core affective characteristics (e.g. valence (positivity versus negativity) and arousal) can provide such a framework. Here, we bring together discrete and dimensional approaches to: (i) offer a structure for integrating different discrete emotions that provides a functional perspective on the adaptive value of emotional states, (ii) suggest how long-term mood states arise from short-term discrete emotions, how they also influence these discrete emotions through a bi-directional relationship and how they may function to guide decision-making, and (iii) generate novel hypothesis-driven measures of animal emotion and mood.
更好地理解动物的情绪是神经科学到动物福利科学等学科的一个重要目标。情绪的有意识体验不能直接评估，但可以测量情绪的神经、行为和生理指标。研究人员使用这些措施来描述动物对被认为能引起离散情绪状态（例如恐惧）的情况的反应。虽然这种离散情绪方法提高了我们对特定情绪的理解，但它缺乏一个可以包含和整合广泛可能的情绪状态的总体框架。从普遍的核心情感特征（例如效价（积极与消极）和唤醒）来概念化情绪的维度方法可以提供这样一个框架。在这里，我们将离散和维度方法结合起来：（i）提供一个整合不同离散情绪的结构，为情绪状态的适应价值提供一个功能视角，（ii）表明长期情绪状态如何从短期离散情绪中产生，它们如何通过双向关系影响这些离散情绪，以及它们如何发挥作用以指导决策，以及（iii）产生新的基于假设的动物情绪和情绪的驱动措施。
