Samuel A. Nastase
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View article: Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models
Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models Open
While evidence has accumulated to support the argument of shared computational mechanisms underlying language comprehension between humans and large language models (LLMs), few studies have examined this argument beyond native-speaker popu…
View article: Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts
Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts Open
To what extent can language give rise to complex conceptual representation? Is multisensory experience essential? Recent large language models (LLMs) challenge the necessity of grounding for concept formation: whether LLMs without groundin…
View article: Cortical language areas are coupled via a soft hierarchy of model-based linguistic features
Cortical language areas are coupled via a soft hierarchy of model-based linguistic features Open
Natural language comprehension is a complex task that relies on coordinated activity across a network of cortical regions. In this study, we propose that regions of the language network are coupled to one another through subspaces of share…
View article: Context modulates brain state dynamics and behavioral responses during narrative comprehension
Context modulates brain state dynamics and behavioral responses during narrative comprehension Open
Narrative comprehension is inherently context-sensitive, yet the brain and cognitive mechanisms by which brief contextual priming shapes story interpretation remain unclear. Using hidden Markov modeling (HMM) of fMRI data, we identified dy…
View article: A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations Open
View article: Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations
Linguistic coupling between neural systems for speech production and comprehension during real-time dyadic conversations Open
The core use of human language is communicating complex ideas from one mind to another in everyday conversations. In conversations, comprehension and production processes are intertwined, as speakers soon become listeners, and listeners be…
View article: The “Podcast” ECoG dataset for modeling neural activity during natural language comprehension
The “Podcast” ECoG dataset for modeling neural activity during natural language comprehension Open
Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain’s linguistic capabilities. ECoG offers a high temporal resolution suitable for investigating processes at multiple temporal timescales a…
View article: Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision
Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision Open
We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test a number of models to better understand the neural representational…
View article: Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision
Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision Open
We effortlessly extract behaviorally relevant information from dynamic visual input in order to understand the actions of others. In the current study, we develop and test different classes of models to better understand the neural represe…
View article: Author response: Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language
Author response: Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language Open
View article: Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language
Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language Open
Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, n…
View article: Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language
Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language Open
Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, n…
View article: Author Correction: Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
Author Correction: Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns Open
View article: Information-making processes in the speaker’s brain drive human conversations
Information-making processes in the speaker’s brain drive human conversations Open
The neural basis of spontaneous speech production, in which speakers efficiently and effortlessly generate utterances on the fly to express their thoughts, is among the least understood aspects of human cognition. This study utilizes infor…
View article: Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language
Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language Open
Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, n…
View article: Aligning Brains into a Shared Space Improves their Alignment to Large Language Models
Aligning Brains into a Shared Space Improves their Alignment to Large Language Models Open
A bstract Recent studies have shown that large language models (LLMs) can accurately predict neural activity measured using electrocorticography (ECoG) during natural language processing. To predict word-by-word neural activity, most prior…
View article: Multimodality and Attention Increase Alignment in NaturalLanguage Prediction Between Humans and ComputationalModels
Multimodality and Attention Increase Alignment in NaturalLanguage Prediction Between Humans and ComputationalModels Open
The potential of multimodal generative artificial intelligence (mAI) to replicate human grounded language understanding, including the pragmatic, context-rich aspects of communication, remains to be clarified. Humans are known to use salie…
View article: Proceedings of the OHBM Brainhack 2022
Proceedings of the OHBM Brainhack 2022 Open
OHBM Brainhack 2022 took place in June 2022. The first hybrid OHBM hackathon, it had an in-person component taking place in Glasgow and three hubs around the globe to improve inclusivity and fit as many timezones as possible. In the buzzin…
View article: Onscreen presence of instructors in video lectures affects learners’ neural synchrony and visual attention during multimedia learning
Onscreen presence of instructors in video lectures affects learners’ neural synchrony and visual attention during multimedia learning Open
COVID-19 forced students to rely on online learning using multimedia tools, and multimedia learning continues to impact education beyond the pandemic. In this study, we combined behavioral, eye-tracking, and neuroimaging paradigms to ident…
View article: The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains
The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains Open
Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their us…
View article: Author response: Cross-movie prediction of individualized functional topography
Author response: Cross-movie prediction of individualized functional topography Open
View article: How a speaker herds the audience: Multi-brain neural convergence over time during naturalistic storytelling
How a speaker herds the audience: Multi-brain neural convergence over time during naturalistic storytelling Open
Storytelling—an ancient way for humans to share individual experiences with others—has been found to induce neural synchronization among listeners. In our exploration of the dynamic fluctuations in listener-listener (LL) coupling throughou…
View article: Intersubject correlation tutorial: Example data
Intersubject correlation tutorial: Example data Open
This dataset comprises preprocessed fMRI data acquired while participants listened to the "Pie Man" spoken story. The data accompany the manuscript "Measuring shared responses across subjects using intersubject correlation" by Nastase, Gaz…
View article: Intersubject correlation tutorial: Example data
Intersubject correlation tutorial: Example data Open
This dataset comprises preprocessed fMRI data acquired while participants listened to the "Pie Man" spoken story. The data accompany the manuscript "Measuring shared responses across subjects using intersubject correlation" by Nastase, Gaz…
View article: Predicting brain activity from word embeddings during natural language comprehension: Example Data
Predicting brain activity from word embeddings during natural language comprehension: Example Data Open
Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
View article: Predicting brain activity from word embeddings during natural language comprehension: Data
Predicting brain activity from word embeddings during natural language comprehension: Data Open
Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
View article: Predicting brain activity from word embeddings during natural language comprehension: Example Data
Predicting brain activity from word embeddings during natural language comprehension: Example Data Open
Example data for the "Predicting brain activity from word embeddings during natural language comprehension" tutorial.
View article: Cross-movie prediction of individualized functional topography
Cross-movie prediction of individualized functional topography Open
Participant-specific, functionally-defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in p…
View article: A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations
A shared linguistic space for transmitting our thoughts from brain to brain in natural conversations Open
Effective communication hinges on a mutual understanding of word meaning in different contexts. The embedding space learned by large language models can serve as an explicit model of the shared, context-rich meaning space humans use to com…
View article: Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations
Deep speech-to-text models capture the neural basis of spontaneous speech in everyday conversations Open
Humans effortlessly use the continuous acoustics of speech to communicate rich linguistic meaning during everyday conversations. In this study, we leverage 100 hours (half a million words) of spontaneous open-ended conversations and concur…