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Found 20 papers, 7 papers with code
SentSpace: Large-Scale Benchmarking and Evaluation of Text using Cognitively Motivated Lexical, Syntactic, and Semantic Features
The modular design of SentSpace allows researchersto easily integrate their own feature computation into the pipeline while benefiting from acommon framework for evaluation and visualization.
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
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 modalities that play a crucial role in human learning.
Comparing Plausibility Estimates in Base and Instruction-Tuned Large Language Models
Experiment 1 shows that, across model architectures and plausibility datasets, (i) log likelihood ($\textit{LL}$) scores are the most reliable indicator of sentence plausibility, with zero-shot prompting yielding inconsistent and typically poor results; (ii) $\textit{LL}$-based performance is still inferior to human performance; (iii) instruction-tuned models have worse $\textit{LL}$-based performance than base models.
How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?
In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior.
Quantifying the redundancy between prosody and text
Using a large spoken corpus of English audiobooks, we extract prosodic features aligned to individual words and test how well they can be predicted from LLM embeddings, compared to non-contextual word embeddings.
Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language
We quantify straightness using a 1-dimensional curvature metric, and present four findings in support of the trajectory straightening hypothesis: i) In trained models, the curvature decreases from the early to the deeper layers of the network.
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
But to achieve these results, LMs must be trained in distinctly un-human-like ways - requiring orders of magnitude more language data than children receive during development, and without perceptual or social context.
Joint cortical registration of geometry and function using semi-supervised learning
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces.
Dissociating language and thought in large language models
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.
A fine-grained comparison of pragmatic language understanding in humans and language models
We perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials.
Event knowledge in large language models: the gap between the impossible and the unlikely
Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals
Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models.
Interpretability of artificial neural network models in artificial Intelligence vs. neuroscience
Given the widespread calls to improve the interpretability of AI systems, we here highlight these different notions of interpretability and argue that the neuroscientific interpretability of ANNs can be pursued in parallel with, but independently from, the ongoing efforts in AI.
Grammatical cues to subjecthood are redundant in a majority of simple clauses across languages
The conclusion is that grammatical cues such as word order are necessary to convey subjecthood and objecthood in a minority of naturally occurring transitive clauses; nevertheless, they can (a) provide an important source of redundancy and (b) are crucial for conveying intended meaning that cannot be inferred from the words alone, including descriptions of human interactions, where roles are often reversible (e. g., Ray helped Lu/Lu helped Ray), and expressing non-prototypical meanings (e. g., "The bone chewed the dog.
The neural architecture of language: Integrative modeling converges on predictive processing
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models.
Representations of Computer Programs in the Human Brain
We find that the Multiple Demand system, a system of brain regions previously shown to respond to code, contains information about multiple specific code properties, as well as machine learned representations of code.
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
Because related words appear in similar contexts, such spaces - called "word embeddings" - can be learned from patterns of lexical co-occurrences in natural language.
The Natural Stories Corpus
It is now a common practice to compare models of human language processing by predicting participant reactions (such as reading times) to corpora consisting of rich naturalistic linguistic materials.
