Version 1
: Received: 8 May 2023 / Approved: 8 May 2023 / Online: 8 May 2023 (13:28:28 CEST)
How to cite:
Matricciani, E. Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts. Preprints2023, 2023050543. https://doi.org/10.20944/preprints202305.0543.v1
Matricciani, E. Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts. Preprints 2023, 2023050543. https://doi.org/10.20944/preprints202305.0543.v1
Matricciani, E. Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts. Preprints2023, 2023050543. https://doi.org/10.20944/preprints202305.0543.v1
APA Style
Matricciani, E. (2023). Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts. Preprints. https://doi.org/10.20944/preprints202305.0543.v1
Chicago/Turabian Style
Matricciani, E. 2023 "Short–Term Memory Capacity Across Time and Language Estimated from Ancient and Modern Literary Texts" Preprints. https://doi.org/10.20944/preprints202305.0543.v1
Abstract
We study the short−term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and modern languages. To model the short–term capacity, we have considered the number of words per interpunctions, the “word interval” , because this parameter can model how the human mind memorizes “chunks” of information. Since 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 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 their similar short–-term memory capacity) and by defining an “overlap index”. We also define a population of universal readers who can read any the New Testament written in alphabetical language. More than 50% of the readers of specific languages overlap with the universal readers with probability . 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.
Keywords
Alphabetical Languages; Artificial Intelligence Writing; Greek; Latin; New Testament; Readers Overlap Probability; Short−Term Memory Capacity, Texts; Translation; Word Interval
Subject
Social Sciences, Language and Linguistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
