Animal affect, welfare, and decision-making: a computational modelling approach

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People care about animal welfare because they assume that non-human animals including mammals and birds are able to experience negative emotions and hence to suffer. In order to monitor welfare and how it is affected by housing and husbandry, we therefore need accurate indicators of animal emotion. Because we can't be sure what other animals feel, and even whether they have the capacity for conscious experiences, our measures are necessarily indirect. Nevertheless, animal welfare scientists, neuroscientists and others are working to develop new and better indicators. An area of growing interest is the use of cognitive markers of emotion, for example how different affective states alter the way in which decisions are made. We have developed a measure based on human psychology findings that people in negative affective states make more negative or pessimistic judgements about ambiguous events than happier people. Over 100 studies in a range of species have now been published using the 'judgement bias' (JB) test, and a meta-analysis that we are conducting indicates that, like humans, animals in more negative states exhibit more 'pessimistic' judgement biases. However, it also detects considerable variation in study findings. One important reason for this, which is receiving increasing attention in human cognitive neuroscience, is that emotional states influence a variety of hidden underlying decision processes which in turn determine the actual decisions made. Such processes can be revealed by computational modelling of data from human decision-making tasks and include not only a person's expectations of good or bad decision outcomes, but also how they value these outcomes, how well they learn about changes in outcomes, and how strongly they adhere to what they have learnt when making a new decision. Anxious people, for example, appear to upgrade the anticipated unpleasantness of negative outcomes whilst depressed people downgrade the expected value of rewarding outcomes. Computational modelling techniques thus allow us to reveal hidden decision processes, evaluate how they are altered by emotions, and hence shed light on the complex links between affect and the actual decisions that we observe.We will develop and implement a computational modelling approach to identify hidden decision-processes in animals, and how these are influenced by affective states. We will use a variant of our JB task that allows appropriate modelling, and a more naturalistic 'risky choice' test that doesn't require the pre-training necessary in JB tasks and hence is quicker to implement. We will induce both short- and longer-term positive and negative affective states using standardised manipulations. Computational modelling will then be employed to investigate how these influence underlying decision-making processes such as those previously identified in human studies. Our work will introduce a novel computational modelling approach to the study of animal affect and welfare. It will identify new decision-making markers of affective states, including those that are replicable across decision tasks and hence particularly robust and reliable. It will also provide a deeper fundamental understanding of links between affect and decision-making processes in animals, and how similar these are to those observed in humans. This will indicate evolutionary similarities across species, and the potential for developing novel and translatable cognitive measures of affective state usable in animal welfare science and other fields. In order to open up the approach to other researchers, we will develop a Matlab toolbox, complete with code, examples, and documentation, that others can use to implement computational methods using similarly-designed JB tasks and our new risky choice task. In this way we hope to drive a step change in analytical methods, theoretical understanding, and new measurement tools that will advance animal welfare science.