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

动物的福利在很大程度上依赖于它的情感状态。这些状态可以在操作上定义为由奖励和惩罚事件引起的，允许实证研究，尽管其他物种的这种状态的有意识体验仍然未知。长期的“情绪”（如抑郁状态）是动物福利的重要决定因素，也可能在指导决策中发挥关键作用，通过影响个体对积极或消极结果的预期，尤其是在模棱两可的情况下（Mendl et al. 2010）。Proc。罗伊。Soc。B. 277,2895 -2904)。我们开发了一种模糊决策的“判断偏差”（JB）分析来验证这一观点，并提供了一种客观的动物影响的“认知”测量（Harding et al. 2004）。Nature427, 312)。现在有超过100项研究使用了这种分析方法，许多研究都支持这样的假设，即处于积极情感状态的个体在模棱两可的情况下表现得好像在期待积极的结果，而处于消极情感状态的个体则相反。然而，也有null和相反的结果。其中一个潜在的重要原因是，情感状态对决策有各种不同的影响，导致各种不同的反应。该项目将通过将实验室啮齿动物决策行为的操作性研究与结果数据的计算建模相结合，探索情感状态如何改变决策。使用我们的JB试验和其他操作任务，将生成数据，这些数据可以使用传统的统计方法进行分析，也可以通过计算建模来确定影响决策的潜在参数，如反应偏差和奖励敏感性。计算模型将允许测试特定的假设，例如，短期负面状态会产生关于模糊结果的负面偏见，但也会增加奖励敏感性，从而澄清文献中的发现，并推进关于情感与决策之间关系的理论。也将有机会发展理论计算模型来调查我们的预测，包括环境中的奖惩经验产生适应性决策档案。该学生将接受动物学习和行为、感知和情感心理学、计算理论和建模等方面的培训，导师团队在各个领域都具有专业知识。他们将学习设计感知辨别任务，编程和使用自动化操作设备，实现复杂数据集的计算，统计和逐个试验分析，并建立理论计算模型。在现代生物学日益数学化的背景下，这些技能将是无价之宝。

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.