{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:24:44Z","timestamp":1772252684427,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/EEI-SCR\/2072\/2014"],"award-info":[{"award-number":["PTDC\/EEI-SCR\/2072\/2014"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT\u2019s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size.<\/jats:p>","DOI":"10.3390\/app10061994","type":"journal-article","created":{"date-parts":[[2020,3,17]],"date-time":"2020-03-17T09:27:41Z","timestamp":1584437261000},"page":"1994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0699-6940","authenticated-orcid":false,"given":"Rahul","family":"Sharma","sequence":"first","affiliation":[{"name":"Department of Informatics Engineering\u2014University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Bernardete","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering\u2014University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Alexandre","family":"Miguel Pinto","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering\u2014University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"F. Am\u00edlcar","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering\u2014University of Coimbra, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/j.cortex.2011.04.006","article-title":"Conceptual representations in mind and brain: Theoretical developments, current evidence and future directions","volume":"48","author":"Kiefer","year":"2012","journal-title":"Cortex"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3186729","article-title":"Conceptual representations for computational concept creation","volume":"52","author":"Xiao","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hill, F., and Korhonen, A. (2014, January 25\u201329). Learning abstract concept embeddings from multi-modal data: Since you probably can\u2019t see what I mean. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1032"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/S0006-3223(99)00116-X","article-title":"Cognition and control in schizophrenia: A computational model of dopamine and prefrontal function","volume":"46","author":"Braver","year":"1999","journal-title":"Biol. Psychiatry"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1126\/science.1127242","article-title":"Biologically based computational models of high-level cognition","volume":"314","year":"2006","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/nrn2462","article-title":"Computational models of schizophrenia and dopamine modulation in the prefrontal cortex","volume":"9","author":"Rolls","year":"2008","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jpsychires.2012.09.010","article-title":"Mental illness, suicide and creativity: 40-Year prospective total population study","volume":"47","author":"Kyaga","year":"2013","journal-title":"J. Psychiatr. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1207\/s15327051hci1204_5","article-title":"ACT-R: A theory of higher level cognition and its relation to visual attention","volume":"12","author":"Anderson","year":"1997","journal-title":"Hum. Comput. Interact."},{"key":"ref_9","first-page":"926","article-title":"A practical guide to training restricted boltzmann machines","volume":"9","author":"Hinton","year":"2010","journal-title":"Momentum"},{"key":"ref_10","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, R., and Peterson, T. (1996, January 3\u20136). Learning in reactive sequential decision tasks: The CLARION model. Proceedings of the IEEE International Conference on Neural Networks, Washington, DC, USA.","DOI":"10.1109\/ICNN.1996.549047"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1177\/105971239400300202","article-title":"Automatic definition of modular neural networks","volume":"3","author":"Gruau","year":"1994","journal-title":"Adapt. Behav."},{"key":"ref_14","unstructured":"G\u00e4rdenfors, P. (2004). Conceptual Spaces: The Geometry of Thought, MIT Press."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1985). Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science. Technical Report.","DOI":"10.21236\/ADA164453"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Freedman, D.A. (2009). Statistical Models: Theory and Practice, Cambridge University Press. Chapter 7.","DOI":"10.1017\/CBO9780511815867"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, T. (2004, January 4\u20138). Solving large scale linear prediction problems using stochastic gradient descent algorithms. Proceedings of the Twenty-First International Conference on Machine Learning, Banff, AB, Canada.","DOI":"10.1145\/1015330.1015332"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1162\/0898929054021102","article-title":"Distinct brain systems for processing concrete and abstract concepts","volume":"17","author":"Binder","year":"2005","journal-title":"J. Cogn. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.neuropsychologia.2019.05.032","article-title":"Distinguishing abstract from concrete concepts in supramodal brain begions","volume":"131","author":"Gao","year":"2019","journal-title":"Neuropsychologia"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1037\/a0021446","article-title":"The representation of abstract words: Why emotion matters","volume":"140","author":"Kousta","year":"2011","journal-title":"J. Exp. Psychology Gen."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Barsalou, L.W., and Wiemer-Hastings, K. (2005). Situating abstract concepts. Grounding Cognition: The Role of Perception and Action in Memory, Language, and Thought, Cambridge University Press.","DOI":"10.1017\/CBO9780511499968.007"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/09515089.2018.1517207","article-title":"Embodied cognition and abstract concepts: Do concept empiricists leave anything out?","volume":"32","year":"2019","journal-title":"Philos. Psychol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0010-0277(96)00723-8","article-title":"Why many concepts are metaphorical","volume":"61","author":"Gibbs","year":"1996","journal-title":"Cognition"},{"key":"ref_25","unstructured":"Iosif, E., Potamianos, A., Giannoudaki, M., and Zervanou, K. (2013, January 19\u201322). Semantic similarity computation for abstract and concrete nouns using network-based distributional semantic models. Proceedings of the 10th International Conference on Computational Semantics, Potsdam, Germany."},{"key":"ref_26","unstructured":"Iosif, E. (2013). Network-Based Distributional Semantic Models. [Ph.D. Thesis, Technical University of Crete]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/72.265959","article-title":"Genetic evolution of the topology and weight distribution of neural networks","volume":"5","author":"Maniezzo","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","article-title":"Evolving neural networks through augmenting topologies","volume":"10","author":"Stanley","year":"2002","journal-title":"Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., and Duffy, N. (2019). Evolving deep neural networks. Artificial Intelligence in the Age of Neural Networks and Brain Computing, Elsevier.","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"ref_30","unstructured":"Hintze, A., Edlund, J.A., Olson, R.S., Knoester, D.B., Schossau, J., Albantakis, L., Tehrani-Saleh, A., Kvam, P., Sheneman, L., and Goldsby, H. (2017). Markov Brains: A technical introduction. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pinto, A.M., and Barroso, L. (2014). Principles of Regulated Activation Networks. Graph-Based Representation and Reasoning, Springer.","DOI":"10.1007\/978-3-319-08389-6_19"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1037\/0278-7393.9.1.21","article-title":"Perceptual enhancement: Persistent effects of an experience","volume":"9","author":"Jacoby","year":"1983","journal-title":"J. Exp. Psychol. Learn. Mem. Cogn."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3758\/BF03197728","article-title":"Effects of varying modality, surface features, and retention interval on priming in word-fragment completion","volume":"15","author":"Roediger","year":"1987","journal-title":"Mem. Cogn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1037\/0278-7393.21.4.803","article-title":"Creating false memories: Remembering words not presented in lists","volume":"21","author":"Roediger","year":"1995","journal-title":"J. Exp. Psychol. Learn. Mem. Cogn."},{"key":"ref_35","first-page":"9","article-title":"Conceptual spaces as a framework for knowledge representation","volume":"2","year":"2004","journal-title":"Mind Matter"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1111\/j.1467-9450.1994.tb00939.x","article-title":"Color naming: A mapping in the IMCS of common color terms","volume":"35","author":"Sivik","year":"1994","journal-title":"Scandi. J. Psychol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1037\/0096-3445.104.3.192","article-title":"Cognitive representations of semantic categories","volume":"104","author":"Rosch","year":"1975","journal-title":"J. Exp. Psychol. Gen."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1146\/annurev.ps.32.020181.000513","article-title":"Categorization of natural objects","volume":"32","author":"Mervis","year":"1981","journal-title":"Ann. Rev. Psychol."},{"key":"ref_39","unstructured":"Rosch, E. (1983). Prototype classification and logical classification: The two systems. New Trends in Conceptual Representation: Challenges to Piaget\u2019s Theory, Lawrence Erlbaum Associates."},{"key":"ref_40","unstructured":"Parsons, L. (2004, January 24). Evaluating subspace clustering algorithms. Proceedings of the Workshop on Clustering High Dimensional Data and its Applications, SIAM International Conference on Data Mining, Lake Buena Vista, FL, USA."},{"key":"ref_41","unstructured":"Livingstone, D. (2009). Unsupervised Learning, John Wiley & Sons, Ltd."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1023\/A:1012861931139","article-title":"BitCube: Clustering and statistical analysis for XML documents","volume":"17","author":"Yoon","year":"2001","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Van Deursen, A., and Kuipers, T. (1999, January 21\u201328). Identifying objects using cluster and concept analysis. Proceedings of the 21st International Conference on Software Engineering, Los Angeles, CA, USA.","DOI":"10.1145\/302405.302629"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning deep architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_47","unstructured":"Eigen, D., Rolfe, J., Fergus, R., and LeCun, Y. (2014, January 14\u201316). Understanding deep architectures using a recursive convolutional network. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0022-5371(83)90201-3","article-title":"A spreading activation theory of memory","volume":"22","author":"Anderson","year":"1983","journal-title":"J. Verb. Learn. Verb. Behav."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/S0022-5371(69)80069-1","article-title":"Retrieval time from semantic memory","volume":"8","author":"Collins","year":"1969","journal-title":"J. Verb. Learn. Verb. Behav."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1023\/A:1006569829653","article-title":"Application of spreading activation techniques in information retrieval","volume":"11","author":"Crestani","year":"1997","journal-title":"Artif. Intell. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/0749-596X(88)90025-3","article-title":"Depth of spreading activation revisited: Semantic mediated priming occurs in lexical decisions","volume":"27","author":"McNamara","year":"1988","journal-title":"J. Mem. Lang."},{"key":"ref_53","unstructured":"Roediger, H., Balota, D., and Watson, J. (2001). Spreading activation and arousal of false memories. The Nature of Remembering: Essays in Honor of Robert G. Crowder, American Psychological Association."},{"key":"ref_54","unstructured":"Kavukcuoglu, K., Sermanet, P., Boureau, Y., Gregor, K., Mathieu, M., and LeCun, Y. (2010). Learning Convolutional Feature Hierachies for Visual Recognition. Advances in Neural Information Processing Systems (NIPS 2010), Neural Information Processing Systems Foundation, Inc."},{"key":"ref_55","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2014, January 14\u201316). OverFeat: Integrated recognition, localization and detection using convolutional networks. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sharma, R., Ribeiro, B., Pinto, A.M., and Cardoso, F.A. (2018, January 8\u201313). Perceiving abstract concepts via evolving computational cognitive modeling. Proceedings of the IEEE 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brasil.","DOI":"10.1109\/IJCNN.2018.8489505"},{"key":"ref_57","first-page":"100","article-title":"Algorithm AS 136: A K-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"Frey","year":"2007","journal-title":"Science"},{"key":"ref_59","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","unstructured":"Lichman, M. (2020, March 13). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_61","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Higuera, C., Gardiner, K.J., and Cios, K.J. (2015). Self-organizing feature maps identify proteins critical to learning in a mouse model of down syndrome. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129126"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1118\/1.2786864","article-title":"The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process","volume":"34","author":"Elter","year":"2007","journal-title":"Med. Phys."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1117\/12.148698","article-title":"Nuclear feature extraction for breast tumor diagnosis","volume":"Volume 1905","author":"Street","year":"1993","journal-title":"Biomedical Image Processing and Biomedical Visualization"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/10556789208805504","article-title":"Robust linear programming discrimination of two linearly inseparable sets","volume":"1","author":"Bennett","year":"1992","journal-title":"Optim. Methods Softw."},{"key":"ref_66","unstructured":"Evett, I.W., and Spiehler, E.J. (1987). Rule Induction in Forensic Science, Central Research Establishment, Home Office Forensic Science Service. Technical Report."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1006\/ijhc.1987.0321","article-title":"Simplifying decision trees","volume":"51","author":"Quinlan","year":"1999","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"ref_68","first-page":"179","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Hum. Gen."},{"key":"ref_69","first-page":"191","article-title":"PARVUS: An extendable package of programs for data exploration, classification and correlation","volume":"4","author":"Forina","year":"1988","journal-title":"J. Chemom."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/10\/6\/1994\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:59Z","timestamp":1760173619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/10\/6\/1994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,14]]},"references-count":69,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["app10061994"],"URL":"https:\/\/doi.org\/10.3390\/app10061994","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202001.0375.v1","asserted-by":"object"}]},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,14]]}}}