Humans organize sequences of events into a single overall experience, and evaluate the aggregated experience as a whole, such as a generally pleasant dinner, movie, or trip. However, such evaluations are potentially computationally taxing, and so our brains must employ heuristics (i.e., approximations). For example, the peak-end rule hypothesis suggests that we average the peaks and end of a sequential event vs. integrating every moment. However, there is no general model to test viable hypotheses quantitatively. Here, we propose a general model and test among multiple specific ones, while also examining the role of working memory. The models were tested with a novel picture-rating task. We first compared averaging across entire sequences vs. the peak-end heuristic. Correlation tests indicated that averaging prevailed, with peak and end both still having significant prediction power. Given this, we developed generalized order-dependent and relative-preference-dependent models to subsume averaging, peak and end. The combined model improved the prediction power. However, based on limitations of relative-preference—including imposing a potentially arbitrary ranking among preferences—we introduced an absolute-preference-dependent model, which successfully explained the remembered utilities. Yet, because using all experiences in a sequence requires too much memory as real-world settings scale, we then tested “windowed” models, i.e., evaluation within a specified window. The windowed (absolute) preference-dependent (WP) model explained the empirical data with long sequences better than without windowing. However, because fixed-windowed models harbor their own limitations—including an inability to capture peak-event influences beyond a fixed window—we then developed discounting models. With (absolute) preference-dependence added to the discounting rate, the results showed that the discounting model reflected the actual working memory of the participants, and that the preference-dependent discounting (PD) model described different features from the WP model. Taken together, we propose a combined WP-PD model as a means by which people evaluate experiences, suggesting preference-dependent working-memory as a significant factor underlying our evaluations.

中文翻译：

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顺序事件的回顾性评估和偏好依赖工作记忆的影响：计算检查

人类将一系列事件组织成单一的整体体验，并将汇总的体验作为一个整体进行评估，例如总体上令人愉快的晚餐、电影或旅行。然而，这样的评估可能在计算上很繁重，因此我们的大脑必须采用启发式方法（即近似值）。例如，峰终规则假设表明，我们对连续事件的峰和结束进行平均，而不是对每个时刻进行积分。但是，没有通用模型来定量测试可行的假设。在这里，我们提出了一个通用模型并在多个特定模型之间进行测试，同时还检查了工作记忆的作用。这些模型用一种新颖的图片评级任务进行了测试。我们首先比较了整个序列的平均与峰端启发式。相关性测试表明平均占优势，峰值和结束仍然具有显着的预测能力。鉴于此，我们开发了广义顺序依赖和相对偏好依赖模型来包含平均、峰值和结束。组合模型提高了预测能力。然而，基于相对偏好的局限性——包括在偏好之间强加潜在的任意排名——我们引入了一个绝对偏好依赖模型，它成功地解释了记住的效用。然而，因为在一个序列中使用所有经验需要太多的记忆作为现实世界的设置规模，然后我们测试了“窗口化”模型，即在指定窗口内评估。加窗（绝对）偏好依赖 (WP) 模型比不加窗更好地解释了具有长序列的经验数据。然而，因为固定窗口模型有其自身的局限性——包括无法捕捉超出固定窗口的峰值事件影响——我们随后开发了贴现模型。将（绝对）偏好依赖添加到贴现率中，结果表明贴现模型反映了参与者的实际工作记忆，并且偏好依赖贴现（PD）模型描述了与 WP 模型不同的特征。总之，我们提出了一个组合的 WP-PD 模型作为人们评估体验的一种手段，表明偏好依赖的工作记忆是我们评估的重要因素。结果表明，贴现模型反映了参与者的实际工作记忆，偏好依赖贴现（PD）模型描述了与 WP 模型不同的特征。总之，我们提出了一个组合的 WP-PD 模型作为人们评估体验的一种手段，表明偏好依赖的工作记忆是我们评估的重要因素。结果表明，贴现模型反映了参与者的实际工作记忆，偏好依赖贴现（PD）模型描述了与 WP 模型不同的特征。总之，我们提出了一个组合的 WP-PD 模型作为人们评估体验的一种手段，表明偏好依赖的工作记忆是我们评估的重要因素。