Week 7

I’ve read the recent Nature perspective, “Language is primarily a tool for communication rather than thought” (Fedorenko et al., 2024), which presents current evidence for the neural distinction between language processing and thought processes. This distinction echoes foundational ideas about communication and control systems, like those discussed by Wiener in “The Human Use of Human Beings,” and resonates with the arguments more recently made in “Dissociating language and thought in large language models” (Mahowald et al., 2024), where this neurological evidence was used to infer a similar separation might exist within LLMs.

This topic is particularly relevant given recent work I encountered, such as Meta AI’s “Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach” (Xu et al., 2024). Their approach, which uses latent continuous tokens for reasoning steps rather than standard language tokens, seems to better approximate the brain’s potential non-linguistic processing and breaks the typical feedforward flow. This doesn’t necessarily mean LLMs don’t reason; I suspect they do. There’s some indication that specific parts of large networks might specialize in reasoning-like computations, perhaps operating within the embedding space in ways not directly tied to predicting the next word (though this is more my own speculation).

The Nature paper also highlights the information-theoretic perspective, arguing that language exhibits properties optimized for communication – being “easy to produce and understand, while being robust to noise.” These characteristics support the view that its primary function is efficient information transfer.

In my view, the evidence for language and thought being neurologically separate systems is compelling. Nevertheless, they are deeply interconnected. Developing highly complex reasoning capabilities seems difficult without the efficient data input provided by communication, primarily through language. A single mind cannot generate vast amounts of knowledge a priori; it requires data, often transmitted efficiently by language from others.

Furthermore, there’s the aspect of applying words to concepts, like emotions or categories. While this labeling might not directly enhance ‘pure’ reasoning, it arguably allows for more efficient categorization, memory consolidation, and learning. I wonder how this functions in individuals with aphasia. How do they categorize feelings like anxiety or excitement? Lacking specific words, perhaps they associate the feeling with somatic markers or memories of specific events (e.g., “feeling like I did before speaking to the class”) rather than applying a linguistic label. Language, in this sense, is another tool in the cognitive toolkit.