Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory

Alex Frid, Hananel Hazan, Ester Koilis, Larry M. Manevitz, Maayan Merhav, Gal Star

2015

Abstract

This work uses supervised machine learning methods over fMRI brain scans to establish the existence of two different encoding procedures for human declarative memory. Declarative knowledge refers to the memory for facts and events and initially depends on the hippocampus. Recent studies which used patients with hippocampal lesions and neuroimaging data, suggested the existence of an alternative process to form declarative memories. This process is triggered by learning mechanism called "Fast Mapping (FM)", as opposed to the 'standard' "Explicit Encoding (EE)" learning procedure. The present work gives a clear biomarker on the existence of two distinct encoding procedures as we can accurately predict which of the processes is being used directly from voxel activity in fMRI scans. The scans are taken during retrieval of information wherein the tasks are identical regardless of which procedure was used for acquisition and by that reflect conclusive prediction. This is an identification of a more subtle cognitive task than direct perceptual cognitive tasks as it requires some encoding and processing in the brain.

References

  1. Atir-Sharon, T., Gilboa, A., Hazan, H., Koilis, E. & Manevitz, L. M. 2015. "Decoding the formation of new semantics: MVPA investigation of rapid neocortical plasticity during associative encoding through Fast Mapping.". Neural Plasticity, vol. 2015, Article ID 804385, 17 pages.
  2. Bauer, P. J., 2008. "Toward a neuro-developmental account of the development of declarative memory". Dev Psychobiol, vol. 50, no. 1, pp. 19-31.
  3. Chang, C. C. & Lin, C. J., 2011. "LIBSVM: a library for support vector machines". ACM Transactions on Intelligent Systems and Technology, available from: <http://www.csie.ntu.edu.tw/cjlin/libsvm>.
  4. Cox, C., 1996. "AFNI: software for analysis and visualization of functional magnetic resonance images", Computers and Biomedial Research, vol. 29, pp. 126-173.
  5. Frankland, P. W., and Bontempi, B., 2005. "The organization of recent and remote memories". Nature Review: Neuroscience, vol. 6, pp. 119-130.
  6. Gais, S., Albouy, G., Boly, M., Dang-Vu, T.T., Darsaud, A., Desseilles, M., Rauchs, G., Schabus, M., Sterpenich, V., Vandewalle, G., Maquet, P., Peigneux, P., 2007. "Sleep transforms the cerebral trace of declarative memories". Proceedings of the National Academy of Sciences of the USA, vol. 104, no. 47, pp. 18778-18783.
  7. Gilboa, A., Hazan, H., Koilis, E., Manevitz, L. and Sharon, T, 2011. "Two memory systems: identifying human memory encoding mechanisms from psychological fMRI data via machine learning techniques". Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 54.
  8. Gonzalez-Castillo, J., Saad, Z.S., Handwerker, D.A., Inati, S.J., Brenowitz, N., Bandettini, P.A., 2012. "Wholebrain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis". Proceedings of the National Academy of Sciences, vol. 109, no. 14, pp. 5487-5492.
  9. Hanke, M., Sederberg, P. B., Hanson, S. J., Haxby, J. V., and Pollmann, S., 2009. "PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data". Neuroinformatics, vol. 7, no. 1, pp. 37-53.
  10. Hu, S. & Liang, H., 2012. "Causality analysis of neural connectivity: New tool and limitations of spectral granger causality". Neurocomputing, vol. 76, no. 1, pp. 44-47.
  11. Kriegeskorte, N., Goebel, R., and Bandettini, P., 2006. "Information-based functional brain mapping". Proceedings of National Academy of Science USA, vol. 103, no. 10, pp. 3863-3868.
  12. McClelland, L., McNaughton, B. L., and O'Reilly, R. C., 1995. "Why there are complementary learning system in the hippocampus and neo-cortex: insights from the successes and failure of connectionist models of learning and memory". Psychological Review, vol. 102, no. 3, pp. 419-457.
  13. Merhav, M., Karni, A. and Gilboa, A., 2014. "Neocortical catastrophic interference in healthy and amnesic adults: A paradoxical matter of time". Hippocampus, vol. 24, no. 12, pp. 1653-1662.
  14. Merhav, M., Karni, A. and Gilboa A., 2015. "Not all declarative memories are created equal: fast mapping as a direct route to cortical declarative representations". Neuroimage, vol. 117, pp. 80-92.
  15. Mitchell, T., Shinkareva, S., Carlson, A., Chang, K. M., Malave, V. L., Mason, R. and Just M. A., 2008. "Predicting human brain activity associated with the meanings of nouns". Science, vol. 320, no. 5880, pp. 1191-1195.
  16. Nawa, N. E. & Ando H., 2014. "Classification of selfdriven mental tasks from whole-brain activity patterns". PLos One, vol. 9, no. 5, e97296.
  17. Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V., 2006. "Beyond mind-reading: multi-voxel pattern analysis of fMRI data". Trends in cognitive science, vol. 10, no. 9, pp. 424-430.
  18. Sharon. T., Moscovitch, M., and Gilboa, A., 2011. "Rapid neocortical acquisition of long-tem arbitrary associations independent of the hippocampus". Proceedings of the National Academy of Science of the USA, vol. 108, no. 3, pp. 1146-1151.
  19. Sladky, R., Friston, K. J., Tröstl, J., Cunnington, R., Moser, E. & Windischberger, C., 2011. "Slice-timing effects and their correction in functional MRI". Neuroimage, vol. 58, no. 2, pp. 588-594.
  20. Squire, L. R., 1992. "Declarative and non-declarative memory: multiple brain systems supporting learning and memory", Journal of Cognitive Neuroscience, vol. 4, no. 3, pp. 232-243.
  21. Squire, L. R., and Alvarez, P., 1995. "Retrograde amnesia and memory consolidation: a neurobiological perspective". Current Opinion in Neurobiology, vol. 5, no. 2, pp. 169-177.
  22. Uematsu, A., Matsui, M., Tanaka, C., Takahashi, T., Noguchi, K., Suzuki, M. and Nishijo, H., 2012. "Developmental trajectories of amygdale and hippocampus from infancy to early adulthood in healthy individuals". PLos One, vol. 7, no. 10, e46970.
  23. Vapnik, V., 1998. "Statistical learning theory". New York, NY: Wiley.
  24. Vert, J. P, Tsuda, K. and Schölkopf, B., 2004. "A primer on kernel methods". Kernel Methods in Computational Biology.
  25. Warren, D. E. and Duff, M. C., 2014. "Not so fast: Hippocampal amnesia slow word learning despite successful fast mapping". Hippocampus, vol. 24, no. 8, pp. 920-933.
  26. Wiesen J. P., 2006. "Benefits, Drawbacks, and Pitfalls of z-Score Weighting". 30th Annual IPMAAC Conference. Available at: "http://annex.ipacweb.org/ library/conf/06/wiesen.pdf" (27 Jun 2006).
Download


Paper Citation


in Harvard Style

Frid A., Hazan H., Koilis E., M. Manevitz L., Merhav M. and Star G. (2015). Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 114-121. DOI: 10.5220/0005594501140121


in Bibtex Style

@conference{ncta15,
author={Alex Frid and Hananel Hazan and Ester Koilis and Larry M. Manevitz and Maayan Merhav and Gal Star},
title={Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={114-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005594501140121},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - Machine Learning Techniques and the Existence of Variant Processes in Humans Declarative Memory
SN - 978-989-758-157-1
AU - Frid A.
AU - Hazan H.
AU - Koilis E.
AU - M. Manevitz L.
AU - Merhav M.
AU - Star G.
PY - 2015
SP - 114
EP - 121
DO - 10.5220/0005594501140121