Efficient data compression in perception and perceptual memory.

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Abstract

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).