Development and validation of an automated test of animal affect and welfare for laboratory rodents

To assess whether refinements to laboratory procedures do indeed improve animal welfare, it is essential that we use scientifically validated measures of welfare. A key determinant of welfare is an animal's affective (emotional) state. Subjective emotions cannot be measured directly so proxy indicators are used instead.

Existing indicators have significant shortcomings and we have therefore developed a new approach, inspired by the links between emotion and cognition observed in humans, and grounded in theoretical models of decision-making. The core hypothesis is that individuals in negative affective states judge ambiguous stimuli negatively compared to happier individuals. We have developed a task to test for such 'cognitive biases' in animals and have found, in several species, that putative affective state is related to judgement bias as predicted.

However, our tests are time-consuming and have not yet been extended to mice, the most commonly used laboratory species. We will therefore develop an automated version of our generic judgement bias task for laboratory rodents. We will use environmental and pharmacological manipulations of affective state to validate our new test. We will investigate whether judgement bias size relates to intensity of the induced state (using dose-response studies), and hence whether our approach can quantify emotional intensity. We will also investigate whether the test can detect the cumulative effects of long-term experience on affective state (e.g. by comparing animals kept in enriched and standard housing conditions).

An industrial partner has expressed interest in further implementing the test that we develop for widespread use. The study will also produce computational models of how affective states change in response to external events. These may provide the basis for modelling the effects of experimental treatments on an animal's affective state, hence facilitating better and more humane planning of studies.

Jones S, Neville V, Higgs L, Paul ES, Dayan P, Robinson ESJ, Mendl M (2018).  Assessing animal affect: an automated and self-initiated judgement bias task based on natural investigative behaviour.  Sci Rep. 8(1): 12400  doi: 10.1038/s41598-018-30571-x

Paul, ES and Mendl MT (2018). Animal emotion: Descriptive and prescriptive definitions and their implications for a comparative perspective. Applied Animal Behaviour Science  doi: 10.1016/j.applanim.2018.01.008

Jones S, Paul ES, Dayan P, Robinson ESJ, Mendl M (2017). Pavlovian influences on learning differ between rats and mice in a counter-balanced Go/NoGo judgement bias task. Behavioural Brain Research 331: 214-224 doi: 10.1016/j.bbr.2017.05.044

Trimmer PC, Paul ES, Mendl MT, McNamara JM, Houston AI (2013). On the evolution and optimality of mood states. Behavioral Sciences 3: 501–521 doi:10.3390/bs3030501


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Jan 2013 - Sep 2016

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