Research Paper:
Structural Coupling System for Cognitive Modeling in Immersive VR Assessment Tasks
Takuro Sekiguchi*, Takenori Obo*, , Tadamitsu Matsuda** , and Naoyuki Kubota*
*Department of Mechanical System Engineering, Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
Corresponding author
**Department of Physical Therapy, Juntendo University
Tokyo, Japan
Unilateral spatial neglect (USN) is a disorder characterized by the inability to attend to the space opposite the cerebral hemisphere lesion, significantly hindering daily activities. Due to the complex neural circuitry of the brain, understanding the mechanisms of USN has proven challenging. In clinical settings, the Behavioral Inattention Test (BIT), a paper-based examination, is commonly used to assess USN. However, improved scores on this test do not necessarily guarantee functional improvement, as it solely evaluates performance in a two-dimensional space. To address this limitation, various approaches utilizing information and communication technology (ICT) and measurement devices in rehabilitation engineering have been proposed. However, related studies have focused on analyzing motor responses to specific sensory stimuli, and the assessment measures often fail to capture a patient’s symptoms in dynamic environments. Therefore, this study proposes a methodology for modeling human spatial cognition. This cognitive architecture utilizes a structural coupling system that integrates parameters from multiple computational intelligence subsystems. In this study, we constructed a simulation environment capable of replicating the movements of patients with USN using empirical data collected from actual experiments. Furthermore, in this simulation environment, we developed patient agents that incorporated the proposed cognitive architecture. The experimental results suggest that hypothesis testing concerning attention mechanisms can be applied through the performance of patient agents within the simulation environment.
- [1] https://www.mhlw.go.jp/stf/wp/hakusyo/kousei/18/backdata/index.html [Accessed January 10, 2024]
- [2] L. J. Buxbaum, M. K. Ferraro, T. Veramonti, A. Farne, J. Whyte, E. Ladavas, F. Frassinetti, and H. B. Coslett, “Hemispatial Neglect: Subtypes, Neuroanatomy and Disability,” Neurogy J., Vol.9, pp. 749-756, 2004. https://doi.org/10.1002/tee.23699
- [3] N. Kubota, J. Botzheim, and T. Obo, “Human Motion Tracking and Feature Extraction for Cognitive Rehabilitation in Informationally Structured Space,” Proc. of the 9th France-Japan & 7th Europe-Asia Congress on Mechatronics (MECATRONICS) and the 13th Int. Workshop on Research and Education in Mechatronics (REM), 2012. https://doi.org/10.1109/MECATRONICS.2012.6451049
- [4] Y. Takamura, M. Imanishi, M. Osaka, S. Ohmatsu, T. Tominaga, K. Yamanaka, S. Morioka, and N. Kawashima, “Intentional gaze shift to neglected space: A compensatory strategy during recovery after unilateral spatial neglect,” Brain, Vol.139, Issue 11, pp. 2970-2982, 2016. https://doi.org/10.1093/brain/aww226
- [5] K. Nukui and S. Ishiai, “Full-field input generated from right visual field information for healthy participants reproduces performance simulating left unilateral spatial neglect in line bisection,” J. Neuropsychol., Vol.17, Issue 3, pp. 505-520, 2023. https://doi.org/10.1111/jnp.12316
- [6] M. Tamura, M. Shirakawa, Z. Luo, and R. Tanemura, “Assessment for unilateral spatial neglect using a virtual reality task during walking under a dual-task condition,” Cognitive Rehabilitation, Vol.27, No.1, 2022. https://doi.org/10.50970/cogrehab.2022.001
- [7] K. Yasuda, S. Takazawa, D. Muroi, Y. Fujimoto, M. Hirano, A. Koshino, and H. Iwata, “Unilateral Spatial Neglect Affected by Right-Sided Stimuli in A Three-Dimensional Virtual Environment: A Preliminary Proof-of-Concept Study,” Proc. of Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. https://doi.org/10.1109/embc40787.2023.10340296
- [8] T. Sekiguchi, T. Obo, N. Kubota, and T. Matsuda, “LSTM-based Motion Trajectory Prediction in a Perceptual-Action Cycle System,” Proc. of 2023 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), pp. 2820-2825, 2023. https://doi.org/10.1109/SMC53992.2023.10394385
- [9] T. Obo and N. Kubota, “Topological Structured Learning for Assessment of Unilateral Spatial Neglect,” Proc. of IEEE Joint 22nd Int. Symp. on Computational Intelligence and Informatics and 8th Int. Conf. on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo 2022), 2022. https://doi.org/10.1109/CINTI-MACRo57952.2022.10029515
- [10] S. Palminteri, V. Wyart, and E. Koechlin, “The importance of falsification in computational cognitive modeling,” Trends in Cognitive Sciences, Vol.21, Issue 6, pp. 425-433, 2021. https://doi.org/10.1016/j.tics.2017.03.011
- [11] K. Friston, “The free-energy principle: a unified brain theory?,” Nature Reviews Neuroscience, Vol.11, No.2, pp. 127-138, 2010. https://doi.org/10.1038/nrn2787
- [12] J. R. Anderson, “How Can the Human Mind Occur in the Physical Universe?,” Oxford University Press, 2007. https://doi.org/10.1093/acprof:oso/9780195324259.001.0001
- [13] Y. Cui, S. Ahmad, and J. Hawkins, “Continuous Online Sequence Learning with an Unsupervised Neural Network Model,” Neural Computation, Vol.28, No.11, pp. 2474-2504, 2016. https://doi.org/10.1162/NECO_a_00893
- [14] S. Franklin, “Global Workspace Theory, Shanahan, And Lida,” Int. J. of Machine Consciousness, Vol.3, No.2, pp. 327-337, 2011. https://doi.org/10.1142/S1793843011000728
- [15] U. Ramamurthy, B. Baars, S. Mello, and S. Franklin, “LIDA: A Working Model of Cognition,” Proc. of the 7th Int. Conf. on Cognitive Modeling (ICCM 2006), 2006.
- [16] B. J. Baars, “A Cognitive Theory of Consciousness,” Cambridge University Press, 1988.
- [17] J. J. Gibson, “The ecological approach to visual perception,” Houghton, Mifflin and Company, 1979.
- [18] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, Vol.7, pp. 625-632, 1995.
- [19] X. D. Huang, Y. Ariki, and M. A. Jack, “Hidden Markov Models for speech recognition,” Edinburgh University Press, Edinburgh, 1990.
- [20] E. Keogh and C. A. Ratanamahatana, “Exact indexing of dynamic time warping,” Knowledge and Information Systems, Vol.7, Issue 3, pp. 358-386, 2005. https://doi.org/10.1007/s10115-004-0154-9
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