Abstract
This work use supervised machine learning methods on fMRI brain scans, taken/measured during a memory-retrieval task, to support establishing the existence of two distinct systems for human declarative memory (“Explicit Encoding” (EE) and “Fast Mapping” (FM)). The importance of using retrieval is that it allows a direct comparison between exemplars designed to use EE and those designed to use FM. This is not directly available under acquisition tasks because of the nature of the purported memory systems since the tasks are necessarily somewhat distinct between the two systems under acquisition. This means that there could be a confounding of the distinction in the task with the difference in the representation and mechanism of the internal memory system during analysis. Retrieval tasks, on the other hand allow for identity of task. Thus this work fills a lacuna in earlier work which used memory acquisition tasks. In addition, since the data used in this work was gathered over a two day period, the classification methods is also able to identify a distinction in the consolidation of the memories in the two systems. The results presented here clearly support the existence of the two distinct memory systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Squire, L.R.: Declarative and non-declarative memory: multiple brain systems supporting learning and memory. J. Cogn. Neurosci. 4(3), 232–243 (1992)
McClelland, L., McNaughton, B.L., O’Reilly, R.C.: 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. Psychol. Rev. 102(3), 419–457 (1995)
Squire, L.R., Alvarez, P.: Retrograde amnesia and memory consolidation: a neurobiological perspective. Current Opin. Neurobiol. 5(2), 169–177 (1995)
Frankland, P.W., Bontempi, B.: The organization of recent and remote memories. Nature Rev. Neurosci. 6, 119–130 (2005)
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.: Sleep transforms the cerebral trace of declarative memories. Proc. Nat. Acad. Sci. USA 104(47), 18778–18783 (2007)
Bauer, P.J.: Toward a neuro-developmental account of the development of declarative memory. Dev. Psychobiol. 50(1), 19–31 (2008)
Uematsu, A., Matsui, M., Tanaka, C., Takahashi, T., Noguchi, K., Suzuki, M., Nishijo, H.: Developmental trajectories of amygdale and hippocampus from infancy to early adulthood in healthy individuals. PLoS ONE 7(10), e46970 (2012)
Sharon, T., Moscovitch, M., Gilboa, A.: Rapid neocortical acquisition of long-tem arbitrary associations independent of the hippocampus. Proc. Nat. Acad. Sci. USA 108(3), 1146–1151 (2011)
Merhav, M., Karni, A., Gilboa, A.: Neocortical catastrophic interference in healthy and amnesic adults: A paradoxical matter of time. Hippocampus 24(12), 1653–1662 (2014)
Norman, K.A., Polyn, S.M., Detre, G.J., Haxby, J.V.: Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10(9), 424–430 (2006)
Mitchell, T., Shinkareva, S., Carlson, A., Chang, K.M., Malave, V.L., Mason, R., Just, M.A.: Predicting human brain activity associated with the meanings of nouns. Science 320(5880), 1191–1195 (2008)
Kriegeskorte, N., Goebel, R., Bandettini, P.: Information-based functional brain mapping. Proc. Nat. Acad. Sci. USA 103(10), 3863–3868 (2006)
Nawa, N.E., Ando, H.: Classification of self-driven mental tasks from whole-brain activity patterns. PLoS ONE 9(5), e97296 (2014)
Atir-Sharon, T., Gilboa, A., Hazan, H., Koilis, E., Manevitz, L.M.: Decoding the formation of new semantics: MVPA investigation of rapid neocortical plasticity during associative encoding through Fast Mapping. Neural Plast. 2015, 17 (2015)
Gilboa, A., Hazan, H., Koilis, E., Manevitz, L., Sharon, T.: Two memory systems: identifying human memory encoding mechanisms from psychological fMRI data via machine learning techniques. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), p. 54 (2011)
Merhav, M., Karni, A., Gilboa, A.: Not all declarative memories are created equal: fast mapping as a direct route to cortical declarative representations. Neuroimage 117, 80–92 (2015)
Wiesen, J.P.: Benefits, Drawbacks, and Pitfalls of z-Score Weighting. In: 30th Annual IPMAAC Conference (2006). http://annex.ipacweb.org/library/conf/06/wiesen.pdf, 27 Jun 2006
Sladky, R., Friston, K.J., Tröstl, J., Cunnington, R., Moser, E., Windischberger, C.: Slice-timing effects and their correction in functional MRI. Neuroimage 58(2), 588–594 (2011)
Gonzalez-Castillo, J., Saad, Z.S., Handwerker, D.A., Inati, S.J., Brenowitz, N., Bandettini, P.A.: Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc. Nat. Acad. Sci. 109(14), 5487–5492 (2012)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Vert, J.P., Tsuda, K., Schölkopf, B.: A primer on kernel methods. Kernel Methods in Computational Biology (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hanke, M., Sederberg, P.B., Hanson, S.J., Haxby, J.V., Pollmann, S.: PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7(1), 37–53 (2009)
Hu, S., Liang, H.: Causality analysis of neural connectivity: New tool and limitations of spectral granger causality. Neurocomputing 76(1), 44–47 (2012)
Cox, C.: AFNI: software for analysis and visualization of functional magnetic resonance images. Comput. Biomed. Res. 29, 126–173 (1996)
Talairach, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging (1988)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
Gelfand, S.B., Ravishankar, C.S., Delp, E.J.: An iterative growing and pruning algorithm for classification tree design. IEEE Trans. Pattern Anal. Mach. Intell. 13(2), 163–174 (1991)
Acknowlegments
Part of this work appears in the M.Sc thesis of Ms. Gal Star at University of Haifa under the supervision of Prof. Larry Manevitz at the Neuro-Computation Laboratory at Caesarea Rothschild Institute (CRI), Haifa, Israel.
The research is based on data gathered by Rotman Research Institute at Baycrest, Toronto, Canada. The examining of this data was suggested by Dr. A. Gilboa and complements the work of Merhav, Karni and Gilboa [16]. The computational analysis of the data was performed at the Neuro-Computation Laboratory at the Caesarea Rothschild Institute at the University of Haifa, Israel under the supervision of Prof. Larry Manevitz. The authors are listed in alphabetical order.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Frid, A., Hazan, H., Koilis, E., Manevitz, L.M., Merhav, M., Star, G. (2016). The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks. In: Nguyen, N., Kowalczyk, R., Filipe, J. (eds) Transactions on Computational Collective Intelligence XXIV. Lecture Notes in Computer Science(), vol 9770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53525-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-662-53525-7_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-53524-0
Online ISBN: 978-3-662-53525-7
eBook Packages: Computer ScienceComputer Science (R0)