The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks
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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.
KeywordsMachine learning Classification Functional Magnetic Resonance Imaging (fMRI) Feature selection Support vector machines Decision trees Radial basis function kernel Declarative memory Consolidation Semantic memory Informational biomarkers
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 . 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.
- 14.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)Google Scholar
- 15.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)Google Scholar
- 17.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
- 21.Vert, J.P., Tsuda, K., Schölkopf, B.: A primer on kernel methods. Kernel Methods in Computational Biology (2004)Google Scholar
- 22.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
- 26.Talairach, J., Tournoux, P.: Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging (1988)Google Scholar