Differential contributions of machine learning and statistical analysis to language and cognitive sciences

TL;DR

This paper systematically compares machine learning and statistical analysis in language and cognitive sciences, illustrating their distinct techniques, objectives, and insights using the Buckeye Speech Corpus, and clarifying misconceptions to guide researchers.

Contribution

It provides a comprehensive tutorial-like comparison of machine learning and statistical methods applied to the same dataset, highlighting their differences, strengths, and appropriate applications in language research.

Findings

Machine learning excels at pattern recognition and prediction.

Statistical methods offer deeper insights into variable relationships.

Semantic relevance as a new metric enhances understanding of speech timing.

Abstract

Data-driven approaches have revolutionized scientific research, with machine learning and statistical analysis being commonly used methodologies. Despite their widespread use, these approaches differ significantly in their techniques, objectives and implementations. Few studies have systematically applied both methods to identical datasets to highlight potential differences, particularly in language and cognitive sciences. This study employs the Buckeye Speech Corpus to illustrate how machine learning and statistical analysis are applied in data-driven research to obtain distinct insights on language production. We demonstrate the theoretical differences, implementation steps, and unique objectives of each approach through a comprehensive, tutorial-like comparison. Our analysis reveals that while machine learning excels at pattern recognition and prediction, statistical methods provide deeper insights into relationships between variables. The study highlights how semantic relevance, a novel metric measuring contextual influence on target words, contributes to understanding word duration in speech. We also systematically compare the differences between regression models used in machine learning and statistical analysis, particularly focusing on the training and fitting processes. Additionally, we clarify several common misconceptions that contribute to the confusion between these two approaches. Overall, by elucidating the complementary strengths of machine learning and statistics, this research enhances our understanding of diverse data-driven strategies in language and cognitive sciences, offering researchers valuable guidance on when and how to effectively apply these approaches in different research contexts.