“Language Is Primarily a Tool for Communication rather than Thought”, 2024-06-19 ():
[media] Language is a defining characteristic of our species, but the function, or functions, that it serves has been debated for centuries. Here we bring recent evidence from neuroscience and allied disciplines to argue that in modern humans, language is a tool for communication, contrary to a prominent view that we use language for thinking.
We begin by introducing the brain network that supports linguistic ability in humans. We then review evidence for a double dissociation between language and thought, and discuss several properties of language that suggest that it is optimized for communication.
We conclude that although the emergence of language has unquestionably transformed human culture, language does not appear to be a prerequisite for complex thought, including symbolic thought. Instead, language is a powerful tool for the transmission of cultural knowledge; it plausibly co-evolved with our thinking and reasoning capacities, and only reflects, rather than gives rise to, the signature sophistication of human cognition.
Fedorenko went on to become a cognitive neuroscientist at MIT using brain scanning to investigate how the brain produces language. And after 15 years, her research has led her to a startling conclusion: We don’t need language to think.
“When you start evaluating it, you just don’t find support for this role of language in thinking”, she said.
When Fedorenko began this work in 2009, studies had found that the same brain regions required for language were also active when people reasoned or carried out arithmetic.
But Fedorenko and other researchers discovered that this overlap was a mirage. Part of the trouble with the early results was that the scanners were relatively crude. Scientists made the most of their fuzzy scans by combining the results from all their volunteers, creating an overall average of brain activity.
In her own research, Fedorenko used more powerful scanners and ran more tests on each volunteer. Those steps allowed her and her colleagues to gather enough data from each person to create a fine-grained picture of an individual brain.
The scientists then ran studies to pinpoint brain circuits that were involved in language tasks, such as retrieving words from memory and following rules of grammar. In a typical experiment, volunteers read gibberish, followed by real sentences. The scientists discovered certain brain regions that became active only when volunteers processed actual language.
Each volunteer had a language network—a constellation of regions that become active during language tasks. “It’s very stable”, Fedorenko said. “If I scan you today, and 10–15 years later, it’s going to be in the same place.”
The researchers then scanned the same people as they performed different kinds of thinking, such as solving a puzzle. “Other regions in the brain are working really hard when you’re doing all these forms of thinking”, she said. But the language networks stayed quiet. “It became clear that none of those things seem to engage language circuits”, she said.
In a paper published Wednesday in Nature, Fedorenko and her colleagues argued that studies of people with brain injuries point to the same conclusion.
Strokes and other forms of brain damage can wipe out the language network, leaving people struggling to process words and grammar, a condition known as aphasia. But scientists have discovered that people can still do algebra and play chess even with aphasia. [cf. 2021] In experiments, people with aphasia can look at two numbers—“123” and “321”, say—and recognize that, by using the same pattern, “456” should be followed by “654”.
See Also:
No evidence for a bilingual executive function advantage in the nationally representative ABCD study
Not Everyone Has an Inner Voice: Behavioral Consequences of Anendophasia
Persistent neuronal activity in human prefrontal cortex links perception and action
Brains and algorithms partially converge in natural language processing
The Phase Transition In Human Cognition § Phase Transitions in Language Processing