Intensity and duration are both pertinent aspects of an emotional experience, yet how they are related is unclear. Though stronger emotions usually last longer, sometimes they abate faster than the weaker ones. We present a quantitative model of affective adaptation, the process by which emotional responses to unchanging affective stimuli weaken with time, that addresses this intensity-duration problem. The model, described by three simple linear algebraic equations, assumes that the relationship between an affective stimulus and its experiencer can be broken down into three parameters. Self-relevance and explanation level combine multiplicatively to determine emotion intensity whereas the interaction of these with explanatory ease determines its duration. The model makes predictions, consistent with available empirical data, about emotion intensity, its duration, and adaptation speed for different scenarios. It predicts when the intensity-duration correlation is positive, negative or even absent, thus offering a solution to the intensity-duration problem. The model also addresses the shortcomings of past models of affective adaptation with its enhanced predictive power and by offering a more complete explanation to empirical observations that earlier models explain inadequately or fail to explain altogether. The model has potential applications in areas such as virtual reality training, games, human-computer interactions, and robotics.

中文翻译：

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解释情绪强度-持续时间关系的情感适应模型

强度和持续时间都是情绪体验的相关方面，但它们之间的关系尚不清楚。尽管强烈的情绪通常会持续更长时间，但有时它们会比较弱的情绪消退得更快。我们提出了情感适应的定量模型，即对不变情感刺激的情绪反应随时间减弱的过程，解决了这个强度-持续时间问题。该模型由三个简单的线性代数方程描述，假设情感刺激与其体验者之间的关系可以分解为三个参数。自我相关性和解释水平相乘地结合以确定情绪强度，而这些与解释容易程度的相互作用决定了其持续时间。该模型做出预测，与可用的经验数据一致，关于情绪强度、持续时间和不同场景的适应速度。它预测强度-持续时间相关性何时为正、负或什至不存在，从而为强度-持续时间问题提供解决方案。该模型还通过其增强的预测能力和对早期模型解释不充分或无法完全解释的经验观察提供更完整的解释，解决了过去情感适应模型的缺点。该模型在虚拟现实培训、游戏、人机交互和机器人技术等领域具有潜在应用。该模型还通过其增强的预测能力和对早期模型解释不充分或无法完全解释的经验观察提供更完整的解释，解决了过去情感适应模型的缺点。该模型在虚拟现实培训、游戏、人机交互和机器人技术等领域具有潜在应用。该模型还通过其增强的预测能力和对早期模型解释不充分或无法完全解释的经验观察提供更完整的解释，解决了过去情感适应模型的缺点。该模型在虚拟现实培训、游戏、人机交互和机器人技术等领域具有潜在应用。