Autogenic Training With Natural Language Processing Modules: A Recent Tool For Certain Neuro Cognitive Studies

TL;DR

This paper investigates integrating NLP modules with neural networks to enhance autogenic training for neurocognitive studies, aiming to improve response completeness and communication understanding in voice-text interactions.

Contribution

It introduces a novel approach combining NLP and neural networks specifically for autogenic training in medical neurocognitive applications.

Findings

NLP modules can effectively support neurocognitive response analysis.

Neural networks enhance the abstraction of responses in autogenic training.

The approach shows promise for scalable medical neurocognitive tools.

Abstract

Learning to respond to voice-text input involves the subject's ability in understanding the phonetic and text based contents and his/her ability to communicate based on his/her experience. The neuro-cognitive facility of the subject has to support two important domains in order to make the learning process complete. In many cases, though the understanding is complete, the response is partial. This is one valid reason why we need to support the information from the subject with scalable techniques such as Natural Language Processing (NLP) for abstraction of the contents from the output. This paper explores the feasibility of using NLP modules interlaced with Neural Networks to perform the required task in autogenic training related to medical applications.