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

动物情感和福利：决策和计算方法

• 批准号: 2265855
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2265855
• 关键词: Animal; emotion; welfare; decision; making

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.

动物的幸福在很大程度上取决于它的情绪(情感)状态。这些状态在操作上可以被定义为由奖惩事件引起的，允许进行实证研究，即使这种状态在其他物种中的有意识体验仍然未知。长期的“情绪”(例如，抑郁状态)是动物福利的特别重要的决定因素，也可能通过偏向个体对积极或消极结果的期望而在指导决策中发挥关键作用，特别是在模棱两可的情况下(Mendl等人)。2010年。程序罗伊SoC。B.277,2895-2904)。我们已经开发了一种模糊情况下决策的“判断偏差”(JB)分析来测试这一想法，并提供一种客观的动物情感的“认知”测量(Harding等人)。2004年。自然427,312)。目前已有100多项研究使用这一方法，许多研究都支持这样一种假设，即处于积极情绪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，而处于消极状态的个体则相反。然而，也有空的和相反的结果。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。这个项目将结合实验室啮齿动物决策行为的操作研究和结果数据的计算模型，确切地探索情感状态是如何改变决策的。使用我们的JB分析和其他可操作的任务，我们将产生数据，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识别影响决策的潜在参数，如反应偏差和奖励敏感度。计算建模将允许测试特定的假设，例如，短期负面状态会产生对模糊结果的负面偏见，但也会增加奖励敏感度，从而澄清文献中的发现，并推进关于情感和决策之间关系的理论。还将有机会开发理论计算模型来调查我们的预测，包括在环境中的奖惩经验产生适应性决策简档。学生将接受动物学习和行为、感知和情感心理学以及计算理论和建模方面的培训，该培训由一个在ALALEAS方面具有专业知识的监督团队提供。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，对复杂数据集进行计算、统计和逐个试验的分析，并建立理论计算模型。在现代生物学日益数学化的背景下，这些技能将是无价的。