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Abstract

Behaviour associated with poor welfare, such as ‘pessimistic’ decision-making, can arise from several different affect-induced shifts in cognitive function. For example, risk aversion can arise from an altered sensitivity to, or expectation of, rewards or punishers, and these processes can themselves be influenced by several environmental factors. By characterising the cognitive processes that generate behaviour, we can gain a better insight into the relationship between specific forms of adversity and indicators of welfare such as judgement bias. We aimed to use computational modelling to extract parameters relating to different aspects of cognitive processing from judgement bias decision-making data and to assess how these were influenced by reward experience, following the prediction that enhanced reward experience generates a positive affective state. To achieve this, we used an automated and self-initiated judgement bias task in which rats had to choose between a risky option which resulted in either an airpuff or apple juice, and a safe option which provided nothing. More specifically, rats initiated each trial by putting their nose in a trough which resulted in the immediate presentation of a tone, the frequency of which provided clear or ambiguous information about the potential outcome. Rats then either stayed in the trough for 2 s (‘stay’=risky option) or removed their nose (‘leave’=safe option). We manipulated reward experience by systematically varying the volume of juice in a sinusoidal manner (mean=1 ml, SD=0.3 ml). Rats were not water or food restricted as part of these studies And all rats were rehomed as pets at the end of the study. These experiments adhered to the ISAE and ASAB/ABS guidelines for the ethical use of animals in research. Following data collection, we modelled decision-making on the task (binary variable: ‘stay’ or ‘leave’) as a partially-observable Markov decision process with a two-dimensional state space describing each rat’s perception of the tone and time left to make a decision. The model provided a good fit of the data (RMSEA=0.028). The computational analysis revealed that variation in risk aversion could be attributed to changes in prior beliefs about the likelihood of reward which was modulated by what an individual had learnt from previous outcomes in the test environment. Specifically, an individual’s expectation that the trial would be rewarded prior to presentation of the tone was greater when they had learnt that they were in a high reward environment, assumed to generate positive affect, resulting in more ‘optimistic’ decision-making (dAIC=4.979, P<0.001). As such, these models inform our understanding of the relationship between the environment, affect, and decision-making. The parameters obtained using this approach may provide a more precise measure of welfare than the decision itself and hence provide a better estimate of the affective impact of poor or improved husbandry. Computational modelling can be a useful tool in the study of animal welfare.