Animal affect and decision-making
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Key finding:
The scientific study of animal affect (emotion) is an area of widespread interest. Whilst research on the mechanisms underlying emotion-like states in animals has expanded and predominated, studies of their function - what they actually do - is less advanced. This is not due to a lack of hypotheses - in both humans and animals, affective states are frequently proposed to play a pivotal role in coordinating adaptive responses and decisions. However, exactly how they might do this (what processes might implement this function) is often left rather vague. Here we propose a framework for integrating animal affect and decision-making that is couched in modern reinforcement learning decision theory. We employ an operational definition of animal affect that dovetails with the well-established 'core affect' view of emotion developed in human psychology, and allows us to study animal affect empirically. We develop a model of how core affect, including short-term (emotion-like) and longer-term (mood-like) states, influences decision-making via processes that we label 'affective options', 'affective predictions', and 'affective outcomes' and which correspond to similar concepts in models of the links between human emotion and decision-making. Our framework provides a formalised way of conceptualising the various types and functions of animal affect in the context of decision-making, generates empirically tractable questions for further research, offers a structured approach to addressing these, and helps our thinking about the links between affect and decision-making in a way that is relevant and applicable across a broad range of taxa.
Links to Open Access Publications or DOI:
Citation:
Mendl, M. & Paul, E.S. (2020). Animal affect and decision-making. Neuroscience and Biobehavioral Reviews 112, 144-163.
