An integrative and functional framework for the study of animal emotion and mood

Abstract

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.

Figure 1.

Core affect represented in two-dimensional space. Words in italics indicate possible locations of specific reported affective states (including discrete/basic emotions). Positive affective states are in quadrants Q1 and Q2, and negative states in quadrants Q3 and Q4. Arrows indicate putative biobehavioural systems associated with reward acquisition and the Q3–Q1 axis of core affect (green), and punishment avoidance and the Q2–Q4 axis of core affect (red). Adapted from Russell (e.g. Russell & Barrett 1999) and Panksepp (e.g. Burgdorf & Panksepp 2006).

Figure 2.

Hypothetical examples of how an organism's core affective state may change with time. The right-hand green loop represents changes during successful cycles of reward acquisition. The left-hand green loop represents changes when reward acquisition is unsuccessful. The red line represents changes in response to the presence (quadrant Q4) or successful avoidance (Q2) of threats or punishers. See text for details.