This paper proposes that decompression is an important and often overlooked component of cognition in all domains where compressive stimuli reduction is a requirement. We support this claim by comparing two compression representations, co-occurrence probabilities and holographic vectors, and two decompression procedures, top-n and Coherencer, on a context generation task from the visual imagination literature. We tentatively conclude that better decompression procedures increase optimality across compression types.

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Keywords cognitive modeling, coherence, context, decompression, generative cognition, imagination, vector symbolic architectures
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Series Lecture Notes in Computer Science
Vertolli, M.O. (Michael O.), Kelly, M.A. (Matthew A.), & Davies, J. (2014). Compression and decompression in cognition. In Lecture Notes in Computer Science. doi:10.1007/978-3-319-09274-4_30