Efficient data compression is essential for capacity-limited systems, such as biological perception and perceptual memory. We hypothesize that the need for efficient compression shapes biological systems in many of the same ways that it shapes engineered systems. If true, then the tools that engineers use to analyze and design systems, namely rate-distortion theory (RDT), can profitably be used to understand human perception and memory. The first portion of this article discusses how three general principles for efficient data compression provide accounts for many important behavioral phenomena and experimental results. We also discuss how these principles are embodied in RDT. The second portion notes that exact RDT methods are computationally feasible only in low-dimensional stimulus spaces. To date, researchers have used deep neural networks to approximately implement RDT in high-dimensional spaces, but these implementations have been limited to tasks in which the sole goal is compression with respect to reconstruction error. Here, we introduce a new deep neural network architecture that approximately implements RDT. An important property of our architecture is that it is trained "end-to-end," operating on raw perceptual input (e.g., pixel values) rather than intermediate levels of abstraction, as is the case with most psychological models. The article's final portion conjectures on how efficient compression can occur in memory over time, thereby providing motivations for multiple memory systems operating at different time scales, and on how efficient compression may explain some attentional phenomena such as RTs in visual search. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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感知和感知记忆中的高效数据压缩。

有效的数据压缩对于容量有限的系统至关重要，例如生物感知和感知记忆。我们假设对有效压缩的需求以许多与塑造工程系统相同的方式塑造生物系统。如果为真，那么工程师用来分析和设计系统的工具，即率失真理论 (RDT)，可以有利地用于理解人类的感知和记忆。本文的第一部分讨论了有效数据压缩的三个一般原则如何解释许多重要的行为现象和实验结果。我们还讨论了这些原则如何体现在 RDT 中。第二部分指出，精确的 RDT 方法仅在低维刺激空间中在计算上可行。迄今为止，研究人员已经使用深度神经网络在高维空间中近似实现 RDT，但这些实现仅限于唯一目标是压缩重建误差的任务。在这里，我们介绍了一种近似实现 RDT 的新深度神经网络架构。我们架构的一个重要特性是它是“端到端”训练的，在原始感知输入（例如，像素值）而不是中间抽象级别上操作，就像大多数心理模型的情况一样。文章的最后一部分推测了内存中随着时间的推移如何有效压缩，从而为在不同时间尺度上运行的多个内存系统提供动力，以及关于高效压缩如何解释一些注意现象，例如视觉搜索中的 RT。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。