Compression and Decompression in Cognition

  • Michael O. Vertolli
  • Matthew A. Kelly
  • Jim Davies
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8598)

Abstract

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.

Keywords

decompression generative cognition imagination context coherence vector symbolic architectures cognitive modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer (2005)Google Scholar
  2. 2.
    Barlow, H.B.: Possible principles underlying the transformation of sensory messages. Sensory Communication, 217–234 (1961)Google Scholar
  3. 3.
    Rolls, E.T.: Memory, Attention, and Decision Making: A Unifying Computational Neuroscience Approach. Oxford University Press, Oxford (2008)Google Scholar
  4. 4.
    Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annu. Rev. Neurosci. 24(1), 1193–1216 (2001)CrossRefGoogle Scholar
  5. 5.
    Vertolli, M.O., Davies, J.: Visual imagination in context: Retrieving a coherent set of labels with Coherencer. In: West, R., Stewart, T. (eds.) 12th International Conference on Cognitive Modeling. Carleton University, Ottawa (2013)Google Scholar
  6. 6.
    Vertolli, M.O., Davies, J.: Coherence in the visual imagination: Local hill search outperforms Thagard’s connectionist model. In: 36th International Conference of the Cognitive Science Society. Cognitive Science Society, Quebec (2014)Google Scholar
  7. 7.
    Jones, M.N., Mewhort, D.J.K.: Representing word meaning and order information in a composite holographic lexicon. Psychol. Rev. 114, 1–37 (2007)CrossRefGoogle Scholar
  8. 8.
    Breault, V., Ouellet, S., Somers, S., Davies, J.: SOILIE: A computational model of 2D visual imagination. In: West, R., Stewart, T. (eds.) 12th International Conference on Cognitive Modeling. Carleton University, Ottawa (2013)Google Scholar
  9. 9.
    Vertolli, M.O., Breault, V., Ouellet, S., Somers, S., Gagné, J., Davies, J.: Theoretical assessment of the SOILIE model of the human imagination. In: 36th International Conference of the Cognitive Science Society. Cognitive Science Society, Quebec (2014)Google Scholar
  10. 10.
    Von Ahn, L., Liu, R., Blum, M.: Peekaboom: a game for locating objects in images. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 55–64. ACM (2006)Google Scholar
  11. 11.
    Byrne, M.D.: How many times should a stochastic model be run? An approach based on confidence intervals. In: West, R., Stewart, T. (eds.) 12th International Conference on Cognitive Modeling. Carleton University, Ottawa (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michael O. Vertolli
    • 1
  • Matthew A. Kelly
    • 1
  • Jim Davies
    • 1
  1. 1.Institute of Cognitive ScienceCarleton UniversityOttawaCanada

Personalised recommendations