Animal emotion and welfare: a decision-making and computational approach

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An animal's welfare is strongly dependent on its emotional (affective) state. These states can beoperationally defined as being elicited by rewarding and punishing events, allowing empirical studyeven though the conscious experience of such states in other species remains unknown. Long-term'moods' (e.g. depressed states) are particularly important determinants of animal welfare and mayalso play a key role in guiding decision-making by biasing an individual's expectations of positive ornegative outcomes, especially in ambiguous situations (Mendl et al. 2010. Proc. Roy. Soc. B. 277, 2895-2904). We have developed a 'judgement bias' (JB) assay of decision-making under ambiguity to testthis idea and to provide an objective 'cognitive' measure of animal affect (Harding et al. 2004. Nature427, 312). There are now over 100 studies using this assay, many supporting the hypothesis thatindividuals in a positive affective state behave as if anticipating positive outcomes under ambiguity,and vice versa for those in a negative state. However, there are also null and opposite results. Onepotentially important reason for these is that affective states have a variety of different influences ondecision-making, leading to a variety of different responses.This project will explore exactly how affective states alter decision-making, by combining operantstudies of laboratory rodent decision-making behaviour with computational modelling of the resultingdata. Using our JB assay and other operant tasks, data will be generated that can be analysed usingconventional statistical approaches, and also modelled computationally to identify underlyingparameters, such as response bias and reward sensitivity, that influence decision-making.Computational modelling will allow specific hypotheses to be tested, for example that short-termnegative states generate negative biases about the outcomes of ambiguity but also increase rewardsensitivity, and hence to clarify findings in the literature and advance theory on the relationshipbetween affect and decision-making. There will also be opportunity to develop theoreticalcomputational models to investigate our predictions, including that experience of rewards andpunishments in the environment generates adaptive decision-making profiles.The student will receive training in animal learning and behaviour, perceptual and affectivepsychology, and computational theory and modelling from a supervisory team with expertise in allareas. They will learn to design perceptual discrimination tasks, to programme and use automatedoperant equipment, to implement computational, statistical, and trial-by-trial analysis of complexdatasets, and to build theoretical computational models. Such skills will be invaluable within theincreasingly mathematical context of modern biology.