Date: Wednesday 19 November 2025, 12:15 pm to 13:45 pm
Venue: Kollegienhaus KH 1.016
About: This event is organized by the RTG Dimensions of Constructional Space.
Title: Language and thought in humans and machines
About the speaker: Evelina Fedorenko (MIT) is an Associate Professor of Brain and Cognitive Sciences at MIT. Her work aims to understand how the language system works in the brain, which has led to the identification of a constellation of regions in the left-hemisphere frontal and temporal cortex that are highly selective for language processing. She calls this network the “language system”.
Abstract
I seek to understand how humans understand and produce language, and how language relates to, and works together with, the rest of human cognition. I will discuss the ‘core’ language network, which includes left-hemisphere frontal and temporal areas, and show that this network is ubiquitously engaged during language processing across input and output modalities, strongly interconnected, and causally important for language. This language network plausibly stores language knowledge and supports linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. Importantly, the language network is sharply distinct from higher-level systems of knowledge and reasoning. First, the language areas show little neural activity when individuals solve math problems, infer patterns from data, or reason about others’ minds. And second, some individuals with severe aphasia lose the ability to understand and produce language but can still do math, play chess, and reason about the world. Thus, language does not appear to be necessary for thinking and reasoning. Human thinking instead relies on several brain systems, including the network that supports social reasoning and the network that supports abstract formal reasoning and fluid intelligence. These systems are sometimes engaged when we use language in the real world—and thus have to work with the language system—but are not language-selective. Many exciting questions remain about the representations and computations in the systems of thought and about how the language system interacts with these higher-level systems. Furthermore, the sharp separation between language and thought in the human brain may have implications for how we think about this relationship in the context of AI models, and for what we can expect from neural network models trained solely on linguistic input with the next-word prediction objective.