A multi-dimensional mathematical theory applied to texts belonging to the classical Greek Literature spanning eight centuries reveals interesting connections between them. By studying words, sentences, and interpunctions in texts, the theory defines deep-language variables and linguistic channels. These mathematical entities are due to writer’s unconscious design and can reveal connections between texts far beyond writer’s awareness. The analysis, based on 3,225,839 words contained in 118,952 sentences, shows that ancient Greek writers, and their readers, were not significantly different from modern writers/readers. Their sentences were processed by a short-term memory modelled with two independent processing units in series, just like modern readers do. In a society in which people were used to memorize information more often than modern people do, the ancient writers wrote almost exactly, mathematically speaking, as modern writers do and for readers of similar characteristics. Since meaning is not considered by the theory, any text of any alphabetical language can be studied exactly with the same mathematical/statistical tools and comparisons are possible, regardless of different languages and epochs of writing.
KeywordsAlphabetical LanguagesDeep-Language VariablesExtended Short-Term MemoryGreek LiteratureIliadLinguistic ChannelsNew TestamentOdysseyUniversal Readability Index
Matricciani, E. (2019) Deep Language Statistics of Italian throughout Seven Centuries of Literature and Empirical Connections with Miller’s 7 ± 2 Law and Short-Term Memory. Open Journal of Statistics , 9, 373-406. https://doi.org/10.4236/ojs.2019.93026
Matricciani, E. (2020) A Statistical Theory of Language Translation Based on Communication Theory. Open Journal of Statistics , 10, 936-997. https://doi.org/10.4236/ojs.2020.106055
Matricciani, E. (2022) Multiple Communication Channels in Literary Texts. Open Journal of Statistics , 12, 486-520. https://doi.org/10.4236/ojs.2022.124030
Matricciani, E. (2022) Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament. Information , 13, Article No. 20. https://doi.org/10.3390/info13010020
Matricciani, E. (2023) Capacity of Linguistic Communication Channels in Literary Texts: Application to Charles Dickens’ Novels. Information , 14, Article No. 68. https://doi.org/10.3390/info14020068
Matricciani, E. (2023) Linguistic Communication Channels Reveal Connections between Texts: The New Testament and Greek Literature. Information , 14, Article No. 405. https://doi.org/10.3390/info14070405
Matricciani, E. (2023) Readability across Time and Languages: The Case of Matthew’s Gospel Translations. AppliedMath , 3, 497-509. https://doi.org/10.3390/appliedmath3020026
Matricciani, E. (2023) Readability Indices Do Not Say It All on a Text Readability. Analytics , 2, 296-314. https://doi.org/10.3390/analytics2020016
Matricciani, E. (2023) Is Short-Term Memory Made of Two Processing Units? Clues from Italian and English Literatures down Several Centuries. Information , 15, Article No. 6. https://doi.org/10.3390/info15010006
Matricciani, E. (2024) A Mathematical Structure Underlying Sentences and Its Connection with Short-Term Memory. AppliedMath , 4, 120-142. https://doi.org/10.3390/appliedmath4010007
Matricciani, E. (2024) Multi-Dimensional Data Analysis of Deep Language in J.R.R. Tolkien and C.S. Lewis Reveals Tight Mathematical Connections. AppliedMath , 4, 927-949. https://doi.org/10.3390/appliedmath4030050
Calvanese Strinati, E. and Barbarossa, S. (2021) 6G Networks: Beyond Shannon towards Semantic and Goal-Oriented Communications. Computer Networks , 190, Article ID: 107930. https://doi.org/10.1016/j.comnet.2021.107930
Shi, G., Xiao, Y., Li, Y. and Xie, X. (2021) From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems. IEEE Communications Magazine , 59, 44-50. https://doi.org/10.1109/mcom.001.2001239
Xie, H., Qin, Z., Li, G.Y. and Juang, B. (2021) Deep Learning Enabled Semantic Communication Systems. IEEE Transactions on Signal Processing , 69, 2663-2675. https://doi.org/10.1109/tsp.2021.3071210
Luo, X., Chen, H. and Guo, Q. (2022) Semantic Communications: Overview, Open Issues, and Future Research Directions. IEEE Wireless Communications , 29, 210-219. https://doi.org/10.1109/mwc.101.2100269
Yang, W., Du, H., Liew, Z.Q., Lim, W.Y.B., Xiong, Z., Niyato, D., et al . (2023) Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges. IEEE Communications Surveys & Tutorials , 25, 213-250. https://doi.org/10.1109/comst.2022.3223224
Bellegarda, J.R. (2000) Exploiting Latent Semantic Information in Statistical Language Modeling. Proceedings of the IEEE , 88, 1279-1296. https://doi.org/10.1109/5.880084
D’Alfonso, S. (2011) On Quantifying Semantic Information. Information , 2, 61-101. https://doi.org/10.3390/info2010061
Zhong, Y. (2017) A Theory of Semantic Information. China Communications , 14, 1-17. https://doi.org/10.1109/cc.2017.7839754
Deniz, F., Nunez-Elizalde, A.O., Huth, A.G. and Gallant, J.L. (2019) The Representation of Semantic Information across Human Cerebral Cortex during Listening versus Reading Is Invariant to Stimulus Modality. The Journal of Neuroscience , 39, 7722-7736. https://doi.org/10.1523/jneurosci.0675-19.2019
Miller, G.A. (1994) The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review , 101, 343-352. https://doi.org/10.1037//0033-295x.101.2.343
Crowder, R.G. (1993) Short-Term Memory: Where Do We Stand? Memory & Cognition , 21, 142-145. https://doi.org/10.3758/bf03202725
Lisman, J.E. and Idiart, M.A.P. (1995) Storage of 7 ± 2 Short-Term Memories in Oscillatory Subcycles. Science , 267, 1512-1515. https://doi.org/10.1126/science.7878473
Cowan, N. (2001) The Magical Number 4 in Short-Term Memory: A Reconsideration of Mental Storage Capacity. Behavioral and Brain Sciences , 24, 87-114. https://doi.org/10.1017/s0140525x01003922
Bachelder, B.L. (2001) The Magical Number 4 = 7: Span Theory on Capacity Limitations. Behavioral and Brain Sciences , 24, 116-117. https://doi.org/10.1017/s0140525x01243921
Saaty, T.L. and Ozdemir, M.S. (2003) Why the Magic Number Seven Plus or Minus Two. Mathematical and Computer Modelling , 38, 233-244. https://doi.org/10.1016/s0895-7177(03)90083-5
Burgess, N. and Hitch, G.J. (2006) A Revised Model of Short-Term Memory and Long-Term Learning of Verbal Sequences. Journal of Memory and Language , 55, 627-652. https://doi.org/10.1016/j.jml.2006.08.005
Richardson, J.T.E. (2007) Measures of Short-Term Memory: A Historical Review. Cortex , 43, 635-650. https://doi.org/10.1016/s0010-9452(08)70493-3
Mathy, F. and Feldman, J. (2012) What’s Magic about Magic Numbers? Chunking and Data Compression in Short-Term Memory. Cognition , 122, 346-362. https://doi.org/10.1016/j.cognition.2011.11.003
Gignac, G.E. (2015) The Magical Numbers 7 and 4 Are Resistant to the Flynn Effect: No Evidence for Increases in Forward or Backward Recall across 85 Years of Data. Intelligence , 48, 85-95. https://doi.org/10.1016/j.intell.2014.11.001
Trauzettel-Klosinski, S. and Dietz, K. (2012) Standardized Assessment of Reading Performance: The New International Reading Speed Texts Irest. Investigative Opthalmology & Visual Science , 53, 5452-5461. https://doi.org/10.1167/iovs.11-8284
Melton, A.W. (1963) Implications of Short-Term Memory for a General Theory of Memory. Journal of Verbal Learning and Verbal Behavior , 2, 1-21. https://doi.org/10.1016/s0022-5371(63)80063-8
Atkinson, R.C. and Shiffrin, R.M. (1971) The Control of Short-Term Memory. Scientific American , 225, 82-90. https://doi.org/10.1038/scientificamerican0871-82
Murdock, B.B. (1972) Short-Term Memory. In: Psychology of Learning and Motivation , Elsevier, 67-127. https://doi.org/10.1016/s0079-7421(08)60440-5
Baddeley, A.D., Thomson, N. and Buchanan, M. (1975) Word Length and the Structure of Short-Term Memory. Journal of Verbal Learning and Verbal Behavior , 14, 575-589. https://doi.org/10.1016/s0022-5371(75)80045-4
Case, R., Kurland, D.M. and Goldberg, J. (1982) Operational Efficiency and the Growth of Short-Term Memory Span. Journal of Experimental Child Psychology , 33, 386-404. https://doi.org/10.1016/0022-0965(82)90054-6
Grondin, S. (2001) A Temporal Account of the Limited Processing Capacity. Behavioral and Brain Sciences , 24, 122-123. https://doi.org/10.1017/s0140525x01303928
Pothos, E.M. and Juola, P. (2001) Linguistic Structure and Short-Term Memory. Behavioral and Brain Sciences , 24, 138-139. https://doi.org/10.1017/s0140525x01463928
Conway, A.R.A., Cowan, N., Bunting, M.F., Therriault, D.J. and Minkoff, S.R.B. (2002) A Latent Variable Analysis of Working Memory Capacity, Short-Term Memory Capacity, Processing Speed, and General Fluid Intelligence. Intelligence , 30, 163-183. https://doi.org/10.1016/s0160-2896(01)00096-4
Jonides, J., Lewis, R.L., Nee, D.E., Lustig, C.A., Berman, M.G. and Moore, K.S. (2008) The Mind and Brain of Short-Term Memory. Annual Review of Psychology , 59, 193-224. https://doi.org/10.1146/annurev.psych.59.103006.093615
Barrouillet, P. and Camos, V. (2012) As Time Goes by: Temporal Constraints in Working Memory. Current Directions in Psychological Science , 21, 413-419. https://doi.org/10.1177/0963721412459513
Potter, M.C. (2012) Conceptual Short Term Memory in Perception and Thought. Frontiers in Psychology , 3, Article No. 113. https://doi.org/10.3389/fpsyg.2012.00113
Jones, G. and Macken, B. (2015) Questioning Short-Term Memory and Its Measurement: Why Digit Span Measures Long-Term Associative Learning. Cognition , 144, 1-13. https://doi.org/10.1016/j.cognition.2015.07.009
Chekaf, M., Cowan, N. and Mathy, F. (2016) Chunk Formation in Immediate Memory and How It Relates to Data Compression. Cognition , 155, 96-107. https://doi.org/10.1016/j.cognition.2016.05.024
Norris, D. (2017) Short-Term Memory and Long-Term Memory Are Still Different. Psychological Bulletin , 143, 992-1009. https://doi.org/10.1037/bul0000108
Van Houdt, G., Mosquera, C. and Nápoles, G. (2020) A Review on the Long Short-Term Memory Model. Artificial Intelligence Review , 53, 5929-5955. https://doi.org/10.1007/s10462-020-09838-1
Islam, M.A., Sarkar, A.K., Hossain, M.I., Ahmed, M.T. and Ferdous, A.H.M.I. (2023) Prediction of Attention and Short-Term Memory Loss by EEG Workload Estimation. Journal of Biosciences and Medicines , 11, 304-318. https://doi.org/10.4236/jbm.2023.114022
Parkes, M.B. (2016) Pause and Effect: An Introduction to the History of Punctuation in the West. Routledge. https://doi.org/10.4324/9781315247243
Matricciani, E. and Caro, L.D. (2019) A Deep-Language Mathematical Analysis of Gospels, Acts and Revelation. Religions , 10, Article No. 257. https://doi.org/10.3390/rel10040257
Battezzato, L. (2009) Techniques of Reading and Textual Layout in Ancient Greek Texts. The Cambridge Classical Journal , 55, 1-23. https://doi.org/10.1017/s1750270500000166
Murphy, J.J. and Thaiss, C. (2020) A Short History of Writing Instruction: From Ancient Greece to the Modern United States. 4th Edition, Routledge. https://doi.org/10.4324/9781003020899.
Spelman, H. (2019) Schools, Reading and Poetry in the Early Greek World. The Cambridge Classical Journal , 65, 150-172. https://doi.org/10.1017/s1750270519000046
Fudin, R. (1989) Reading Efficiency and the Development of Left-to-Right Writing by the Ancient Greeks. Perceptual and Motor Skills , 69, 1251-1258. https://doi.org/10.2466/pms.1989.69.3f.1251
Johnson, W.A. (2000) Toward a Sociology of Reading in Classical Antiquity. American Journal of Philology , 121, 593-627. https://doi.org/10.1353/ajp.2000.0053
Johnson, W.A. (2010). Readers and Reading Culture in the High Roman Empire: A Study of Elite Communities. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195176407.001.0001
Krauß, A., Leipziger, J. and Schücking-Jungblut, F. (2020) Material Aspects of Reading in Ancient and Medieval Cultures. Walter de Gruyter GmbH.
Vatri, A. (2012) The Physiology of Ancient Greek Reading. The Classical Quarterly , 62, 633-647. https://doi.org/10.1017/s0009838812000213
Abramovitz, M. and Stegun, I.A. (1985) Handbook of Mathematical Functions, with Formulas, Graphs and Mathematical Tables. 9th Edition, Dover Publications.
Rosenzweig, M.R., Bennett, E.L., Colombo, P.J., Lee, D.W. and Serrano, P.A. (1993) Short-Term, Intermediate-Term, and Long-Term Memories. Behavioural Brain Research , 57, 193-198. https://doi.org/10.1016/0166-4328(93)90135-d
Kamiński, J. (2017) Intermediate-Term Memory as a Bridge between Working and Long-Term Memory. The Journal of Neuroscience , 37, 5045-5047. https://doi.org/10.1523/jneurosci.0604-17.2017