We study
the short-term memory capacity of ancient readers of the original New Testament
written in Greek, of its translations to Latin and to modern languages. To
model it, we consider the number of words between any two contiguous
interpunctions Ip, because
this parameter can model how the human mind memorizes “chunks” of information.
Since IP can be calculated
for any alphabetical text, we can perform experiments—otherwise impossible—with
ancient readers by studying the literary works they used to read. The
“experiments” compare the IP of texts of a language/translation to those of another language/translation by
measuring the minimum average probability of finding joint readers (those who
can read both texts because of similar short-term memory capacity) and by defining an “overlap
index”. We also define the population of universal readers, people who can
read any New Testament text in any language. Future work is vast, with many
research tracks, because alphabetical literatures are very large and allow many
experiments, such as comparing authors, translations or even texts written by
artificial intelligence tools.
References
[1]
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
[2]
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
[3]
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 20.
https://doi.org/10.3390/info13010020
[4]
Matricciani, E. (2022) Multiple Communication Channels in Literary Texts. Open Journal of Statistics, 12, 486-520. https://doi.org/10.4236/ojs.2022.124030
[5]
Matricciani, E. (2023) Capacity of Linguistic Communication Channels in Literary Texts: Application to Charles Dickens’ Novels. Information, 14, Article 68.
https://doi.org/10.3390/info14020068
[6]
Matricciani, E. (2023) Readability Indices Do Not Say It All on a Text Readability. Analytics, 2, 296-314. https://doi.org/10.3390/analytics2020016
[7]
Miller, G.A. (1955) 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
[8]
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
[9]
Cowan, N. (2000) 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
[10]
Pothos, E.M. and Joula, P. (2000) Linguistic Structure and Short-Term Memory. Behavioral and Brain Sciences, 24, 138-139.
https://doi.org/10.1017/S0140525X01463928
[11]
Jones, G. and Macken, B. (2015) Questioning Short-Term Memory and Its Measurements: Why Digit Span Measures Long-Term Associative Learning. Cognition, 144, 1-13. https://doi.org/10.1016/j.cognition.2015.07.009
[12]
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
[13]
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
[14]
Chen, Z. and Cowan, N. (2005) Chunk Limits and Length Limits in Immediate Recall: A Reconciliation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 1235-1249. https://doi.org/10.1037/0278-7393.31.6.1235
[15]
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
[16]
Barrouillest, 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
[17]
Conway, A.R.A., Cowan, N., Michael, F., Bunting, M.F., Therriaulta, 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
[18]
Islam, M., Sarkar, A., Hossain, M., Ahmed, M. and Ferdous, A. (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
[19]
Hayashi, K. and Takahashi, N. (2020) The Relationship between Phonological Short-Term Memory and Vocabulary Acquisition in Japanese Young Children. Open Journal of Modern Linguistics, 10, 132-160.
https://doi.org/10.4236/ojml.2020.102009
[20]
Flesch, R. (1948) A New Readability Yardstick. Journal of Applied Psychology, 32, 222-233. https://doi.org/10.1037/h0057532
[21]
Flesch, R. (1974) The Art of Readable Writing. Harper & Row, New York.
[22]
Kincaid, J.P., Fishburne, R.P, Rogers, R.L. and Chissom, B.S. (1975) Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel. Research Branch Report 8-75, Chief of Naval Technical Training. Naval Air Station, Memphis.
[23]
DuBay, W.H. (2004) The Principles of Readability. Impact Information, Costa Mesa.
[24]
Bailin, A. and Graftstein, A. (2001) The Linguistic Assumptions Underlying Readability Formulae: A Critique. Language & Communication, 21, 285-301.
https://doi.org/10.1016/S0271-5309(01)00005-2
[25]
DuBay, W.H. (2006) The Classic Readability Studies. Impact Information, Costa Mesa.
[26]
Zamanian, M. and Heydari, P. (2012) Readability of Texts: State of the Art. Theory and Practice in Language Studies, 2, 43-53. https://doi.org/10.4304/tpls.2.1.43-53
[27]
Benjamin, R.G. (2012) Reconstructing Readability: Recent Developments and Recommendations in the Analysis of Text Difficulty. Educational Psychology Review, 24, 63-88. https://doi.org/10.1007/s10648-011-9181-8
[28]
Collins-Thompson, K. (2014) Computational Assessment of Text Readability: A Survey of Current and Future Research. ITL—International Journal of Applied Linguistics, 165, 97-135. https://doi.org/10.1075/itl.165.2.01col
[29]
Kandel, L. and Moles, A. (1958) Application de l’indice de Flesch à la langue française. Cahiers Etudes de Radio-Télévision, 19, 253-274.
[30]
Matricciani, E. and Caro, L.D. (2019) A Deep-Language Mathematical Analysis of Gospels, Acts and Revelation. Religions, 10, Article 257.
https://doi.org/10.3390/rel10040257
[31]
Papoulis, A. (1990) Probability & Statistics. Prentice Hall, Hoboken.
[32]
Shannon, C.E. (1948) A Mathematical Theory of Communication. The Bell System Technical Journal, 27, 623-656. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x