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

开发一种翻译和计算方法来研究动物影响和福利

基本信息

批准号：
BB/X009696/1
负责人：
Vikki Neville
金额：
$ 51.78万
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FX009696%2F1
关键词：
Developing translational computational approach studying

项目摘要

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.

对动物的情绪和情绪良好的衡量对科学和社会非常重要。在人类的照顾下，全球有数十亿只动物，例如在农场，动物园和实验室中，并且社会动力越来越多，以确保这些动物具有良好的生活质量。此外，对情绪障碍的病因和治疗的研究在很大程度上取决于动物模型。尽管如此，现有的措施仍有各种局限性，并且有开发新措施的范围。近年来，已经开发了用于研究人类情绪障碍的计算方法 - 这些方法涉及数学上描述潜在行为的认知过程以及患者的差异有情绪障碍。这一理论驱动的计算精神病学领域在增进了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，计算分析对于更好地理解情绪和情绪对大鼠行为的影响也很有价值，但是一种翻译和计算方法尚未得到充分的探索和利用。我建议评估在计算精神病学中研究的两种现象的有效性，这是一种潜在的情绪衡量标准（因此，福利，鉴于最大程度地减少了负面情绪的经验并最大程度地提高积极情绪的经验对于确保善良福利至关重要）：Pavlovian：Pavlovian干扰和目标指导与习惯学习。帕夫洛维亚人的干扰描述了硬有线倾向对行为的影响 - 人类按下按钮要比避免按下按钮要获得奖励要困难得多。计算精神病学研究表明，患有情绪障碍的人更容易受到帕夫洛维亚干扰的影响。对于情绪障碍的患者而言，面对硬连线倾向的倾向更加困难。目标指导与习惯学习是指个人根据对行动的后果的完全理解，而不是基于过去的行动成功做出决定的程度。使用计算方法的研究表明，情绪障碍的人更容易依靠后一种决策形式。已经开发了行为任务来研究大鼠的帕夫洛夫干扰和目标指导与习惯学习，但是这些方法尚未与计算方法和操纵相结合，以评估其作为福利措施的潜力。我还建议使用基于Raspberry Pi的设备在啮齿动物中研究这些现象，以便更轻松地将它们缩放或向下缩放或下降，以便以较低的成本进行研究，从而有助于吸收这些方法，并最终还可以采用。进行家庭式进行，以最大程度地减少对动物的干扰。最终，这项研究有可能推动朝着评估动物情绪，情绪和福利的更可转换和负担得起的方法的转变，这可能有助于我们改善圈养动物的福利并为情绪障碍开发更有效的治疗方法。