A rational framework is proposed to explain how we accommodate unbounded sensory input within bounded memory. According to this framework, memory is stored as a statistic-like representation that is repeatedly summarized and compressed to make room for new input. Summarization of sensory input must be rapid; that of abstract trace might be slower and more deliberative, drawing on elaborative processes some of which might occasionally reach consciousness (as in mind-wandering). Short-term sensory traces are summarized as simple statistics organized into structures such as a time series, graph or dictionary, and longer-term abstract traces as more complex statistic-like structures. Summarization at multiple time scales requires an intensive process of memory curation which might account for the high metabolic consumption of the brain at rest. Summarization may be guided by heuristics to help choose which statistics to apply at each step, so that the trace is useful for a wide range of future needs, the objective being to "represent the past" rather than tune for a specific task. However, the choice of statistics (or of heuristics to guide that choice) is a potential target for learning, possibly over long-term scales of development or evolution. The framework is intended as an aid to make sense of our extensive empirical and theoretical knowledge of memory and bring us closer to understanding it in functional and mechanistic terms.

翻译：本文提出一个理性框架来解释我们如何在有限记忆容量内容纳无限感官输入。根据该框架，记忆以类统计表征形式存储，通过反复概括与压缩为新输入腾出空间。感官输入的概括必须迅速；抽象痕迹的概括则可能更缓慢且更具审慎性，调用某些可能偶尔进入意识层面的精细化过程（如心智游移）。短期感官痕迹被概括为组织成时间序列、图或字典等结构的简单统计量，长期抽象痕迹则被概括为更复杂的类统计结构。多时间尺度的概括需要密集的记忆管理过程，这或许能解释大脑静息状态下的高代谢消耗。概括过程可由启发式方法引导，帮助选择每个步骤适用的统计量，使痕迹能适应未来广泛需求，其目标在于"表征过去"而非针对特定任务进行优化。然而，统计量的选择（或指导该选择的启发式方法）可能成为学习的目标，这种学习可能跨越发育或进化的长期尺度。该框架旨在帮助我们理解关于记忆的大量经验与理论知识，推动我们从功能与机制层面更深入地理解记忆。