Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain

Area of Science:

• Neuroscience
• Evolutionary Biology
• Computational Modeling

Background:

• The evolution of the human brain and language remains a complex area of study.
• Understanding the neural basis of language requires integrating evolutionary and computational perspectives.

Purpose of the Study:

• To propose Computational Comparative Neuroprimatology as a framework for analyzing human brain function and evolution.
• To model the evolutionary trajectory of the language-ready brain from primate ancestors to Homo sapiens.
• To compare auditory and visual processing streams for language comprehension and meaning.

Main Methods:

• Modeling macaque brains to hypothesize the last common ancestor of macaques and humans (LCA-m).
• Dynamic brain modeling to explore ape gesture acquisition and evolution from LCA-m to the last common ancestor of chimpanzees and humans (LCA-c).
• Hypothesizing the role of imitation, protosign, and protospeech in human evolution.
• Analyzing cultural evolution from protolanguages to full human languages.
• Comparing auditory and visual dorsal and ventral streams for language processing models.

Main Results:

• A hypothesized evolutionary path for the language-ready brain, incorporating mirror systems, gesture acquisition, and the emergence of protospeech.
• A model for cultural evolution leading to grammatical and compositional language in Homo sapiens.
• Comparative analysis of auditory and visual streams, providing a basis for two language processing models.

Conclusions:

• Computational Comparative Neuroprimatology provides a robust framework for understanding human brain and language evolution.
• The study presents novel hypotheses on the evolutionary stages and mechanisms of language development.
• Further research is needed to synthesize models and address challenges in computational neuroprimatology.