article · Zenodo (CERN European Organization for Nuclear Research) · 2018
Language Acquisition
Salatino, D. R.
Cited by 0Open access
Abstract
In this paper, we will develop some aspects of a psycholinguistic theory that tries to explain how our natural language is acquired. We have divided this paper into three parts: First part: includes a brief review of different theories on language acquisition that are valid today, used as a contrast element. In particular, we will analyze in depth the model R-R (representational redescription) of Karmiloff-Smith, contrasting its cognitive-computational model with our approach. Then, using evolutionary biology as a model, we will address the relevance of a Universal Language existing in socio-cultural reality and its genetic aspects. Second part: it will deal with a possible Genetic Typology of the natural human language elaborated on the base of the theory of the “hidden colors.” Third part: we will highlight the relevance of the Universals, a possible origin of the lexical contextures, and an analysis of the importance of socio-cultural inheritance, all fundamental elements in the acquisition of natural human language.
Cite this paper
Salatino, D. R. (2018). Language Acquisition. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.1322754
Salatino, D. R. “Language Acquisition.” Zenodo (CERN European Organization for Nuclear Research), 2018, https://doi.org/10.5281/zenodo.1322754.
Salatino, D. R. 2018. “Language Acquisition.” Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.1322754.
Salatino, D.R. (2018) ‘Language Acquisition’, Zenodo (CERN European Organization for Nuclear Research). Available at: https://doi.org/10.5281/zenodo.1322754.
@article{salatino2018language,
title = {{Language Acquisition}},
author = {Salatino, D. R.},
journal = {Zenodo (CERN European Organization for Nuclear Research)},
year = {2018},
doi = {10.5281/zenodo.1322754}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.