Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach
Akshaya V S
Akshaya V S
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India and Department of Social and Applied Sciences, UNIFACVEST University Center, Brazil, South America.
Latent Semantic Analysis (LSA) is an approach used for expressing and extracting textual meanings using statistical evaluations or modeling applied to vast corpora of text, and its development has been a major motivation for this study to understand the design thinking approach. We introduced LSA and gave some instances of how it might be used to further our knowledge of cognition and to develop practical technology. Since LSA's inception, other alternative statistical models for meaning detection and analysis in text corpora have been created, tested, and refined. This study demonstrates the value that statistical models of semantics provide to the study of cognitive science and the development of cognition. These models are particularly useful because they enable researchers to study a wide range of problems pertaining to knowledge, discourse perception, text cognition, and language using expansive representations of human intelligence.
Keywords
Latent Semantic Analysis, Human Cognition, Human Intelligence, Statistical Models, Working Memory, Design Thinking, Man and Machine.
N. Sigala, Z. Kaldy, and G. D. Reynolds, “Editorial: The cognitive neuroscience of visual working memory, Volume II,” Front. Syst. Neurosci., vol. 16, p. 1017754, 2022.
I. Karamitsos, S. Albarhami, and C. Apostolopoulos, “Tweet sentiment analysis (TSA) for cloud providers using classification algorithms and latent semantic analysis,” J. Data Anal. Inf. Process., vol. 07, no. 04, pp. 276–294, 2019.
J. Huang and Z. Jia, “A cross-product free Jacobi–Davidson type method for computing a partial generalized singular value decomposition of a large matrix pair,” J. Sci. Comput., vol. 94, no. 1, 2023.
L. Hogben and J. A. H. Waterhouse, “Statistical models bearing on the semantics of correlation; the non-replacement model,” Hum. Biol., vol. 21, no. 3, pp. 163–186, 1949.
D. Ryzhova and D. Paperno, “Chapter 11. Constructing a typological questionnaire with distributional semantic models,” in The Typology of Physical Qualities, Amsterdam: John Benjamins Publishing Company, 2022, pp. 309–328.
Y. Yuan, W. Yang, Z. Luo, and R. Gou, “Temporal Context Modeling Network with local-global complementary architecture for temporal proposal generation,” Electronics (Basel), vol. 11, no. 17, p. 2674, 2022.
W. Farhan, Z. Wang, Y. Huang, S. Wang, F. Wang, and X. Jiang, “A predictive model for medical events based on contextual embedding of temporal sequences,” JMIR Med. Inform., vol. 4, no. 4, p. e39, 2016.
Y. Kassem, H. Gökçekuş, A. Iravanian, and R. Gökçekuş, “Predictive suitability of renewable energy for desalination plants: the case of güzelyurt region in northern Cyprus,” Model. Earth Syst. Environ., vol. 8, no. 3, pp. 3657–3677, 2022.
W. Xie, F. Wang, Y. Li, L. Lai, and J. Pei, “Advances and challenges in DE Novo drug design using three-dimensional deep generative models,” J. Chem. Inf. Model., vol. 62, no. 10, pp. 2269–2279, 2022.
H. Maslak, University of Wolverhampton, UK, R. Mitkov, and University of Wolverhampton, UK, “Paragraph similarity matches for generating multiple-choice test items,” in Proceedings of the Student Research Workshop Associated with RANLP 2021, 2021.
“Identifying semantic role clusters and alignment types via microrole coexpression tendencies,” Stud. Lang., vol. 38, no. 3, pp. 463–484, 2014.
O. Savic, L. Unger, and V. M. Sloutsky, “Exposure to co-occurrence regularities in language drives semantic integration of new words,” J. Exp. Psychol. Learn. Mem. Cogn., vol. 48, no. 7, pp. 1064–1081, 2022.
“Supplemental material for exposure to co-occurrence regularities in language drives semantic integration of new words,” J. Exp. Psychol. Learn. Mem. Cogn., 2022.
A. Ikegami, R. Okada, and T. Nakanishi, “The discovery of historical transition in aesthetic notions through changes in co-occurrence words mainly used in Waka poetry in three major poetry anthologies,” in Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Cham: Springer International Publishing, 2022, pp. 152–173.
H. Wang et al., “Identifying objective and subjective words via topic modeling,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 3, pp. 718–730, 2018.
J. Liang et al., “Ellipse fitting via low-rank generalized multidimensional scaling matrix recovery,” Multidimens. Syst. Signal Process., vol. 29, no. 1, pp. 49–75, 2018.
G. Knezek, D. Gibson, R. Christensen, O. Trevisan, and M. Carter, “Assessing approaches to learning with nonparametric multidimensional scaling,” Br. J. Educ. Technol., 2022.
IT Services Information, “James O. ramsay,” Department of Psychology. [Online]. Available: https://www.mcgill.ca/psychology/james-o-ramsay. [Accessed: 25-Dec-2022].
C. Mulat, M. Donias, P. Baylou, G. Vignoles, and C. Germain, “Optimal orientation estimators for detection of cylindrical objects,” Signal Image Video Process., vol. 2, no. 1, pp. 51–58, 2008.
F. Kardan, F. Qadiri, Department of Sport Management and Motor Behavior, Faculty of Physical Education, University of Kharazmi, Tehran, Iran, and Department of Sport Management and Motor Behavior, Faculty of Physical Education, University of Kharazmi, Tehran, Iran, “Comparison of the effect of (child-oriented and teacher-oriented) educational methods on the motor skills development of children,” jcmh, vol. 6, no. 1, pp. 251–264, 2019.
R. Bottini, P. Morucci, A. D’Urso, O. Collignon, and D. Crepaldi, “The concreteness advantage in lexical decision does not depend on perceptual simulations,” J. Exp. Psychol. Gen., vol. 151, no. 3, pp. 731–738, 2022.
S. Kuchinsky, “Words matter: Analyzing lexical effects on recognition and effort using normed word stimuli,” 2021.
E. Saunders and D. Quinto-Pozos, “Comprehension benefits of visual-gestural iconicity and spatial referencing,” Second Lang. Res., p. 026765832110376, 2021.
Z. Swann, A. Daliri, and C. F. Honeycutt, “Impact of startling acoustic stimuli on word repetition in individuals with aphasia and apraxia of speech following stroke,” J. Speech Lang. Hear. Res., vol. 65, no. 5, pp. 1671–1685, 2022.
F. Manhardt, A. Özyürek, B. Sümer, K. Mulder, D. Z. Karadöller, and S. Brouwer, “Iconicity in spatial language guides visual attention: A comparison between signers’ and speakers’ eye gaze during message preparation,” J. Exp. Psychol. Learn. Mem. Cogn., vol. 46, no. 9, pp. 1735–1753, 2020.
S. Sharoff, “Functional Text Dimensions for the annotation of web corpora,” Corpora, vol. 13, no. 1, pp. 65–95, 2018.
S. Watanabe and Y. Maesako, “Co-occurrence pattern of congeneric tree species provides conflicting evidence for competition relatedness hypothesis,” PeerJ, vol. 9, no. e12150, p. e12150, 2021.
K. Livesay and C. Burgess, “Mediated Priming does not Rely on Weak Semantic Relatedness or Local Co-occurrence,” in Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, New York: Routledge, 2022, pp. 609–614.
A. Nesti, S. Nooij, M. Losert, H. H. Bülthoff, and P. Pretto, “Roll rate perceptual thresholds in active and passive curve driving simulation,” Simulation, vol. 92, no. 5, pp. 417–426, 2016.
N. A. Andriyanov, “Combining text and image analysis methods for solving multimodal classification problems,” Pattern Recognit. Image Anal., vol. 32, no. 3, pp. 489–494, 2022.
L. R. Warr, M. J. Heaton, W. F. Christensen, P. A. White, and S. B. Rupper, “Distributional validation of precipitation data products with spatially varying mixture models,” J. Agric. Biol. Environ. Stat., 2022.
C. H. Wu, S. Motamed, S. Srivastava, and F. De la Torre, “Generative Visual Prompt: Unifying distributional control of pre-trained generative models,” arXiv [cs.CV], 2022.
Huh, “Hierarchical semantic correspondence analysis on feature classes between two geospatial datasets using a graph embedding method,” ISPRS Int. J. Geoinf., vol. 8, no. 11, p. 479, 2019.
N. Chandra, H. Vaidya, and J. K. Ghosh, “Human cognition-based framework for detecting roads from remote sensing images,” Geocarto Int., vol. 37, no. 8, pp. 2365–2384, 2022.
E. Lundblom, E. R. Cohn, and L. Tindall Covert, “School-based telepractice assessment (STA): Guidance for evaluating school-based speech-language telepractice service delivery,” Top. Lang. Disord., vol. 42, no. 2, pp. 173–188, 2022.
I. Park, Y. Jeong, B. Yoon, and L. Mortara, “Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis,” Technol. Anal. Strat. Manag., vol. 27, no. 7, pp. 759–781, 2015.
V. A. Leksin, “Symmetrization and overfitting in probabilistic latent semantic analysis,” Pattern Recognit. Image Anal., vol. 19, no. 4, pp. 565–574, 2009.
L. C. Q. Lam, T. K. Toai, and S. Vaclav, “A latent semantic analysis method for ranking the results of human disease search engine,” Bull. Electr. Eng. Inform., vol. 12, no. 2, pp. 1189–1195, 2023.
B. Sun, B. Wang, W. Che, D. Wu, Z. Chen, and T. Liu, “Improving pre-trained language models with syntactic dependency prediction task for Chinese Semantic Error Recognition,” arXiv [cs.CL], 2022.
A. S. Szczesniak and E. Z. Skinner, “Meaning of texture words to the consumer,” J. Texture Stud., vol. 4, no. 3, pp. 378–384, 1973.
K. Seshadri, “Parallel hierarchical clustering of big text corpora,” in Handbook of Big Data Analytics Volume 2: Applications in ICT, security and business analytics, Institution of Engineering and Technology, 2021, pp. 313–342.
M. A. P. Subali and I. K. P. Suniantara, “Determining the best answers for Balinese language problems using Latent Semantic Analysis,” Jurnal Informatika dan Komputer, vol. 24, no. 2, pp. 175–181, 2022.
Z. Tang, Q. Xiao, L. Zhu, K. Li, and K. Li, “A semantic textual similarity measurement model based on the syntactic-semantic representation,” Intell. Data Anal., vol. 23, no. 4, pp. 933–950, 2019.
S. M. Taghavi, V. Ghezavati, H. M. Bidhandi, and S. M. J. M. Al-e-Hashem, “Green-resilient supplier selection and order allocation under disruption by utilizing conditional value at risk: Mixed response strategies,” Proc.integration Optim. Sustain., 2022.
Akshaya, V., and Purusothaman, T, “Business Intelligence as a Service in Analysis of Academic Courses”, Int. J. Appl. Eng. Res, 2016, 11(4), 2458-2467.
Mohammad Biglarbegian, “Formalization and Knowledge Representation in Advanced Engineering Informatics”, Journal of Computing and Natural Science, vol.2, no.1, pp. 008-014, January 2022. doi: 10.53759/181X/JCNS202202002.
A. H and V.A “Analysis of Artificial Intelligence in Future Industrial Applications,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 10, pp. 205–210, Oct. 2019, doi: 10.5373/jardcs/v11i10/20193162.
Amir Antonie and Andrew Mathus, “A Model for Performance Evaluation”, Journal of Computing and Natural Science, vol.2, no.1, pp. 001-007, January 2022. doi: 10.53759/181X/JCNS202202001.
A. Mohanasundaram and S. K. Aruna, “Improved Henon Chaotic Map-based Progressive Block-based visual cryptography strategy for securing sensitive data in a cloud EHR system,” International Journal of Intelligent Networks, vol. 3, pp. 109–112, 2022, doi: 10.1016/j.ijin.2022.08.004.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Akshaya V S
Akshaya V S
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India and Department of Social and Applied Sciences, UNIFACVEST University Center, Brazil, South America.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Akshaya V S, Beatriz Lucia Salvador Bizotto, Mithileysh Sathiyanarayanan, “Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach”, Journal of Machine and Computing, pp. 159-170, April 2023. doi: 10.53759/7669/jmc202303015.
