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

人们关心动物福利，因为他们认为非人类的动物，包括哺乳动物和鸟类，能够体验负面情绪，因此遭受痛苦。为了监测福利以及它如何受到住房和畜牧业的影响，我们需要准确的动物情绪指标。因为我们不能确定其他动物的感受，甚至不能确定它们是否有意识体验的能力，所以我们的测量必然是间接的。然而，动物福利科学家、神经科学家和其他人正在努力开发新的、更好的指标。人们越来越感兴趣的一个领域是使用情感的认知标记，例如不同的情感状态如何改变决策的方式。我们根据人类心理学的发现开发了一种测量方法，即处于消极情感状态的人比快乐的人对模棱两可的事件做出更多消极或悲观的判断。现在已经发表了100多项关于一系列物种的研究，使用“判断偏差”（JB）测试，我们正在进行的一项荟萃分析表明，像人类一样，处于更消极状态的动物表现出更多的“悲观”判断偏差。然而，它也发现了研究结果的相当大的差异。其中一个重要的原因是，情绪状态会影响各种隐藏的潜在决策过程，而这些决策过程反过来又会决定实际做出的决策，这在人类认知神经科学中越来越受到关注。这些过程可以通过对人类决策任务的数据进行计算建模来揭示，不仅包括一个人对好或坏的决策结果的期望，还包括他们如何评估这些结果，他们对结果变化的了解程度，以及他们在做出新决策时对所学知识的坚持程度。例如，焦虑的人似乎会提升负面结果的预期不愉快程度，而抑郁的人则会降低奖励结果的预期价值。因此，计算建模技术使我们能够揭示隐藏的决策过程，评估它们是如何被情绪改变的，从而阐明情感和我们观察到的实际决策之间的复杂联系。我们将开发和实施一种计算建模方法，以识别动物中隐藏的决策过程，以及情感状态如何影响这些过程。我们将使用JB任务的一个变体，它允许适当的建模，以及一个更自然的“风险选择”测试，它不需要JB任务中必要的预训练，因此可以更快地实现。我们将使用标准化的操作来诱导短期和长期的积极和消极情感状态。然后将采用计算建模来研究这些因素如何影响潜在的决策过程，例如先前在人类研究中确定的决策过程。我们的工作将介绍一种新的计算建模方法来研究动物的影响和福利。它将确定情感状态的新决策标记，包括那些在决策任务中可复制的标记，因此特别强大和可靠。它还将提供对动物中情感和决策过程之间联系的更深入的基本理解，以及这些与人类中观察到的相似性。这将表明物种之间的进化相似性，以及开发可用于动物福利科学和其他领域的情感状态的新颖和可翻译的认知措施的潜力。为了向其他研究人员开放该方法，我们将开发一个Matlab工具箱，其中包含代码，示例和文档，其他人可以使用类似设计的JB任务和我们新的风险选择任务来实现计算方法。通过这种方式，我们希望推动分析方法，理论理解和新的测量工具的一步变化，这将推动动物福利科学。

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.