Empirical approaches to the study of language evolution

Area of Science:

• Evolutionary linguistics
• Cognitive evolution
• Comparative psychology

Background:

• Language evolution research faces skepticism but shares methodologies with historical sciences like geology and cosmology.
• Models of language evolution generate testable hypotheses, enabling empirical evaluation with available data.
• Interdisciplinary data sources, including comparative, neural, and genetic evidence, are crucial for model validation.

Discussion:

• The primary obstacle in language evolution studies is not data scarcity but a limited adherence to hypothesis-testing and strong inference principles.
• The interdisciplinary nature of relevant data presents a challenge, requiring integrated approaches for robust analysis.
• Contemporary models of language evolution offer diverse perspectives on the developmental trajectory of human language capabilities.

Key Insights:

• Well-designed models in language evolution yield numerous testable hypotheses.
• Strong inference, involving simultaneous testing of multiple hypotheses, is key to empirical evaluation.
• Comparative, neural, and genetic data provide empirical grounds for testing evolutionary models.

Outlook:

• Future research should prioritize hypothesis-driven approaches and strong inference in language evolution.
• Integrating diverse data sources (comparative, neural, genetic) is essential for advancing the field.
• A multistage model is proposed for understanding the evolution of language components and language-ready brains.