Objective measures of animal emotion-like and mood-like states are essential for preclinical studies of affective disorders and for assessing the welfare of laboratory and other animals. However, the development and validation of measures of these affective states poses a challenge partly because the relationships between affect and its behavioural, physiological and cognitive signatures are complex. Here, we suggest that the crisp characterisations offered by computational modelling of the underlying, but unobservable, processes that mediate these signatures should provide better insights. Although this computational psychiatry approach has been widely used in human research in both health and disease, translational computational psychiatry studies remain few and far between. We explain how building computational models with data from animal studies could play a pivotal role in furthering our understanding of the aetiology of affective disorders, associated affective states and the likely underlying cognitive processes involved. We end by outlining the basic steps involved in a simple computational analysis.

动物情绪样和心境样状态的客观测量对于情感障碍的临床前研究以及评估实验室和其他动物的福利至关重要。然而，这些情感状态测量的开发和验证提出了挑战，部分原因是情感与其行为、生理和认知特征之间的关系很复杂。在这里，我们建议通过对调解这些特征的底层但不可观察的过程进行计算建模所提供的清晰特征应该提供更好的见解。尽管这种计算精神病学方法已广泛应用于人类健康和疾病研究，但转化计算精神病学研究仍然很少。我们解释了如何利用动物研究数据构建计算模型，在进一步了解情感障碍的病因学、相关情感状态以及可能涉及的潜在认知过程方面发挥关键作用。最后，我们概述了简单计算分析所涉及的基本步骤。