91 citations · 189 across the 25 of their papers we have counts for
29 papers
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Shared circuits predict whether LLMs generalize across formats in arithmetic reasoning
Andrea Gregor de Varda, Sana Pandey, Pengrui Han +2
In many forms of reasoning, including arithmetic reasoning, generalizing across superficial changes in input format is effortless for humans: anyone who can solve 2+5 can also solv…
Modular Cognitive Architecture Emerges in Large Language Models
Pengrui Han, Jacob Andreas, Evelina Fedorenko +1
The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning ab…
Representational Curvature Modulates Behavioral Uncertainty in Large Language Models
Jack King, Evelina Fedorenko, Eghbal A. Hosseini
In autoregressive large language models (LLMs), temporal straightening offers an account of how the next-token prediction objective shapes representations. Models learn to progress…
Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion
Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko +1
Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predict…
Different types of syntactic agreement recruit the same units within large language models
Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko +1
Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open quest…
What does it mean to understand language?
Colton Casto, Anna Ivanova, Evelina Fedorenko +1
Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we pro…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…
Representing Speech Through Autoregressive Prediction of Cochlear Tokens
Greta Tuckute, Klemen Kotar, Evelina Fedorenko +1
We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms…
Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung +2
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises…
The time scale of redundancy between prosody and linguistic context
Tamar I. Regev, Chiebuka Ohams, Shaylee Xie +5
In spoken communication, information is transmitted not only via words, but also through a rich array of non-verbal signals, including prosody--the non-segmental auditory features…
Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models
Anna A. Ivanova, Aalok Sathe, Benjamin Lipkin +17
The ability to build and reason about models of the world is essential for situated language understanding. But evaluating world modeling capabilities in modern AI systems -- espec…
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
Today's most accurate language models are trained on orders of magnitude more language data than human language learners receive - but with no supervision from other sensory modali…
Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models
Carina Kauf, Emmanuele Chersoni, Alessandro Lenci +2
Semantic plausibility (e.g. knowing that "the actor won the award" is more likely than "the actor won the battle") serves as an effective proxy for general world knowledge. Languag…
How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
Greta Tuckute, Dawn Finzi, Eshed Margalit +8
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no cons…
Quantifying the redundancy between prosody and text
Lukas Wolf, Tiago Pimentel, Evelina Fedorenko +4
Prosody -- the suprasegmental component of speech, including pitch, loudness, and tempo -- carries critical aspects of meaning. However, the relationship between the information co…
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
Eghbal A. Hosseini, Evelina Fedorenko
Predicting upcoming events is critical to our ability to interact with our environment. Transformer models, trained on next-word prediction, appear to construct representations of…
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are m…
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with…
Program Comprehension Does Not Primarily Rely On the Language Centers of the Human Brain
Shashank Srikant, Anna A. Ivanova, Yotaro Sueoka +5
Our goal is to identify brain regions involved in comprehending computer programs. We use functional magnetic resonance imaging (fMRI) to investigate two candidate systems of brain…
Joint cortical registration of geometry and function using semi-supervised learning
Jian Li, Greta Tuckute, Evelina Fedorenko +3
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learnin…
Dissociating language and thought in large language models
Kyle Mahowald, Anna A. Ivanova, Idan A. Blank +3
Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. He…
A fine-grained comparison of pragmatic language understanding in humans and language models
Jennifer Hu, Sammy Floyd, Olessia Jouravlev +2
Pragmatics and non-literal language understanding are essential to human communication, and present a long-standing challenge for artificial language models. We perform a fine-grai…
Event knowledge in large language models: the gap between the impossible and the unlikely
Carina Kauf, Anna A. Ivanova, Giulia Rambelli +5
Word co-occurrence patterns in language corpora contain a surprising amount of conceptual knowledge. Large language models (LLMs), trained to predict words in context, leverage the…