Developing a translational and computational approach to studying animal affect and welfare

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Having good measures of emotion and mood in animals is highly important to science and society. There are billions of animals globally under the care of humans, for example in farms, in zoos, and in laboratories, and there is an increasing societal drive to ensure that these animals have a good quality of life. Additionally, research into the aetiology and treatment of mood disorders relies heavily on animal models. Despite this, existing measures have various limitations and there is scope for the development of new measures.In recent years, computational approaches have been developed to study mood disorders in humans - these involve mathematically describing the cognitive processes underlying behaviour and how these differ in patients with mood disorders. This theory-driven field of computational psychiatry has been highly successful in furthering our understanding of emotion and mood in humans. My research has shown that computational analyses can also be valuable in better understanding the influence of emotion and mood on behaviour in rats, but a translational and computational approach has yet to be fully explored and exploited. I propose assessing the validity of two phenomena that have been studied in computational psychiatry as potential novel measures of mood (and hence welfare, given that minimising experience of negative moods and maximising experience of positive moods is crucial to ensuring good welfare) in rats: Pavlovian interference and goal-directed vs. habitual learning. Pavlovian interference describes the influence of hard-wired tendencies on behaviour - it is much harder for humans to press a button, than to avoid pressing a button, to get a reward. Computational psychiatry research has shown that humans experiencing mood disorders are more susceptible to Pavlovian interference; it's even harder for patients with mood disorders to go against hard-wired tendencies. Goal-directed vs. habitual learning refers to the extent to which an individual makes decisions based on a complete understanding of the consequences of their actions, as opposed to making decisions based on which actions were successful in the past. Studies using computational methods have demonstrated that individuals with mood disorders are more prone to relying on the latter form of decision-making. Behavioural tasks have been developed to study both Pavlovian interference and goal-directed vs habitual learning in rats, but these methods have yet to be combined with computational approaches and manipulations to assess their potential as measures of welfare. I also propose developing behavioural tasks to study these phenomena in rodents using Raspberry Pi based equipment, so that they can more easily be scaled up or down for different species and studies can be conducted at lower cost hence aiding uptake of these methods, and ultimately also conducted within the home-cage to minimise disturbance to the animals. Ultimately, this research has the potential to drive a shift towards more translatable and affordable methods to assess animal emotion, mood, and welfare, that may help us to improve the welfare of captive animals and develop more effective treatments for mood disorders.