Efficient Data Compression Leads to Categorical Bias in Perception and Perceptual Memory

AbstractEfficient data compression is essential for capacity-limited sys- tems, such as biological memory. We hypothesize that the need for efficient data compression shapes biological perception and perceptual memory 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 perception and memory. 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 in a task-general manner. An important property of our architecture is that it is trained “end-to-end”, operating on raw perceptual input (e.g., pixels) rather than an intermediate level of abstraction, as is the case with most psychological models. We demonstrate that our framework accounts for categorical biases in perception and perceptual memory.

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