Developing a translational and computational approach to studying animal affect and welfare

对动物的情感和情绪进行良好的测量对科学和社会都非常重要。全球有数十亿动物在人类的照顾下，例如在农场，动物园和实验室中，并且有越来越多的社会动力来确保这些动物有良好的生活质量。此外，对情绪障碍的病因学和治疗的研究严重依赖于动物模型。尽管如此，现有的测量方法仍有各种局限性，而且还有开发新测量方法的空间。近年来，已经开发出了计算方法来研究人类的情绪障碍-这些方法涉及数学描述行为背后的认知过程以及这些过程在情绪障碍患者中的差异。这个理论驱动的计算精神病学领域在促进我们对人类情感和情绪的理解方面非常成功。我的研究表明，计算分析在更好地理解情绪和情绪对大鼠行为的影响方面也是有价值的，但翻译和计算方法尚未得到充分探索和利用。我建议评估两种现象的有效性，这两种现象已经在计算精神病学中研究过，作为大鼠情绪的潜在新措施（因此福利，考虑到最小化消极情绪的体验和最大化积极情绪的体验对于确保良好的福利至关重要）：巴甫洛夫干扰和目标导向与习惯性学习。巴甫洛夫干扰描述了硬连线倾向对行为的影响--对人类来说，按下一个按钮比避免按下一个按钮要难得多，以获得奖励。计算精神病学研究表明，经历情绪障碍的人更容易受到巴甫洛夫干扰;情绪障碍患者更难违背硬连线倾向。目标导向学习与习惯性学习是指个人在多大程度上基于对其行为后果的完全理解而做出决定，而不是基于过去哪些行为是成功的而做出决定。使用计算方法的研究表明，患有情绪障碍的人更倾向于依赖后一种形式的决策。行为任务已经被开发来研究巴甫洛夫干扰和目标导向与习惯性学习的大鼠，但这些方法尚未与计算方法和操作相结合，以评估其作为福利措施的潜力。我还建议开发行为任务，使用基于Raspberry Pi的设备在啮齿动物中研究这些现象，以便它们可以更容易地针对不同物种进行放大或缩小，并且可以以较低的成本进行研究，从而帮助采用这些方法，并最终在家庭笼子内进行，以尽量减少对动物的干扰。最终，这项研究有可能推动转向更可翻译和负担得起的方法来评估动物的情绪，情绪和福利，这可能有助于我们改善圈养动物的福利，并开发更有效的情绪障碍治疗方法。

Having good measures of emotion and mood in animals is highly important to science and society. There are billions of animals globally under the care of humans, for example in farms, in zoos, and in laboratories, and there is an increasing societal drive to ensure that these animals have a good quality of life. Additionally, research into the aetiology and treatment of mood disorders relies heavily on animal models. Despite this, existing measures have various limitations and there is scope for the development of new measures.In recent years, computational approaches have been developed to study mood disorders in humans - these involve mathematically describing the cognitive processes underlying behaviour and how these differ in patients with mood disorders. This theory-driven field of computational psychiatry has been highly successful in furthering our understanding of emotion and mood in humans. My research has shown that computational analyses can also be valuable in better understanding the influence of emotion and mood on behaviour in rats, but a translational and computational approach has yet to be fully explored and exploited. I propose assessing the validity of two phenomena that have been studied in computational psychiatry as potential novel measures of mood (and hence welfare, given that minimising experience of negative moods and maximising experience of positive moods is crucial to ensuring good welfare) in rats: Pavlovian interference and goal-directed vs. habitual learning. Pavlovian interference describes the influence of hard-wired tendencies on behaviour - it is much harder for humans to press a button, than to avoid pressing a button, to get a reward. Computational psychiatry research has shown that humans experiencing mood disorders are more susceptible to Pavlovian interference; it's even harder for patients with mood disorders to go against hard-wired tendencies. Goal-directed vs. habitual learning refers to the extent to which an individual makes decisions based on a complete understanding of the consequences of their actions, as opposed to making decisions based on which actions were successful in the past. Studies using computational methods have demonstrated that individuals with mood disorders are more prone to relying on the latter form of decision-making. Behavioural tasks have been developed to study both Pavlovian interference and goal-directed vs habitual learning in rats, but these methods have yet to be combined with computational approaches and manipulations to assess their potential as measures of welfare. I also propose developing behavioural tasks to study these phenomena in rodents using Raspberry Pi based equipment, so that they can more easily be scaled up or down for different species and studies can be conducted at lower cost hence aiding uptake of these methods, and ultimately also conducted within the home-cage to minimise disturbance to the animals. Ultimately, this research has the potential to drive a shift towards more translatable and affordable methods to assess animal emotion, mood, and welfare, that may help us to improve the welfare of captive animals and develop more effective treatments for mood disorders.