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.

Additional Metadata
Keywords cognitive modeling, coherence, context, decompression, generative cognition, imagination, vector symbolic architectures
Persistent URL dx.doi.org/10.1007/978-3-319-09274-4_30
Series Lecture Notes in Computer Science
Citation
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