Dataset Information

Semantic processing of English sentences using statistical computation based on neurophysiological models.

ABSTRACT: Computer programs that can accurately interpret natural human language and carry out instructions would improve the lives of people with language processing deficits and greatly benefit society in general. von Neumann in theorized that the human brain utilizes its own unique statistical neuronal computation to decode language and that this produces specific patterns of neuronal activity. This paper extends von Neumann's theory to the processing of partial semantics of declarative sentences. I developed semantic neuronal network models that emulate key features of cortical language processing and accurately compute partial semantics of English sentences. The method of computation implements the MAYA Semantic Technique, a mathematical technique I previously developed to determine partial semantics of sentences within a natural language processing program. Here I further simplified the technique by grouping repeating patterns into fewer categories. Unlike other natural language programs, my approach computes three partial semantics. The results of this research show that the computation of partial semantics of a sentence uses both feedforward and feedback projection which suggest that the partial semantic presented in this research might be a conscious activity within the human brain.

SUBMITTER: Mitchell MT

PROVIDER: S-EPMC4460779 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Semantic processing of English sentences using statistical computation based on neurophysiological models.

Mitchell Marcia T MT

Frontiers in physiology 20150522

Computer programs that can accurately interpret natural human language and carry out instructions would improve the lives of people with language processing deficits and greatly benefit society in general. von Neumann in theorized that the human brain utilizes its own unique statistical neuronal computation to decode language and that this produces specific patterns of neuronal activity. This paper extends von Neumann's theory to the processing of partial semantics of declarative sentences. I de ...[more]

PMID: 26106331

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Project description:This event-related potential (ERP) study examines the influence of dialectal competence differences (merged vs. unmerged dialect group) on cross-dialectal comprehension between Southern German dialects. It focuses on the question as to whether certain dialect phonemes (/[Formula: see text]/, /[Formula: see text]/), which are attributed to different lexemes in two dialect areas (Central Bavarian, Bavarian-Alemannic transition zone) evoke increased neural costs during sentence processing. In this context, the phonological and semantic processing of lexemes is compared in three types of potentially problematic communication settings (misunderstanding, incomprehension, allophonic variation = potential comprehension). For this purpose, an oddball design including whole sentences was combined with a semantic rating task. Listeners from the unmerged Central Bavarian dialect area heard sentences including either native or non-native lexemes from the merged neighboring dialect. These had to be evaluated with regard to their context acceptability. The main difference between the lexemes can be attributed to the fact that they have different meanings in the respective dialect areas or are non-existent in the linguistic competence of the Central Bavarians. The results provide evidence for the fact that non-native lexemes containing the /[Formula: see text]/-diphthong lead to enhanced neural costs during sentence processing. The ERP results show a biphasic pattern (N2b/N400, LPC) for non-existent lexemes (incomprehension) as well as for semantically incongruous lexemes (misunderstanding), reflecting an early error detection mechanism and enhanced costs for semantic integration and evaluation. In contrast, allophonic /[Formula: see text]/ deviations show reduced negativities and no LPC, indexing an unproblematic categorization and evaluation process. In the light of these results, an observed change of /[Formula: see text]/ to /[Formula: see text]/ in the Bavarian-Alemannic transition zone can be interpreted as a facilitation strategy of cross-dialectal comprehension to reduce both misunderstandings as well as neural costs in processing, which might be interpreted as the initial trigger for this particular phoneme change.

Dataset Information

Semantic processing of English sentences using statistical computation based on neurophysiological models.

Publications

Semantic processing of English sentences using statistical computation based on neurophysiological models.

Similar Datasets

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