Philipp Thölke

Yann Harel

Kory Mathewson

Jay A. Olson

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (voir plus)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs’ semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. These divergence-based measures index associative thinking—the ability to access and combine remote concepts in semantic space—an established facet of creative cognition. We benchmark performance on the Divergent Association Task (DAT) and across multiple creative-writing tasks (haiku, story synopses, and flash fiction), using identical, objective scoring. We found evidence that LLMs can surpass average human performance on the DAT, and approach human creative writing abilities, yet they remain below the mean creativity scores observed among the more creative segment of human participants. Notably, even the top performing LLMs are still largely surpassed by the aggregated top half of human participants, underscoring a ceiling that current LLMs still fail to surpass. We also systematically varied linguistic strategy prompts and temperature, observing reliable gains in semantic divergence for several models. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labor by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.

2026-01-20

Scientific Reports (publié)

Meditation induces shifts in neural oscillations, brain complexity, and critical dynamics: novel insights from MEG

Annalisa Pascarella

David Meunier

Jordan O'Byrne

Tarek Lajnef

Antonino Raffone

Roberto Guidotti

Vittorio Pizzella

Laura Marzetti

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We exam… (voir plus)ined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.

2025-11-22

Neuroscience of Consciousness (publié)

Oneiris: An AI-augmented Brain-Computer Interface for Exploring Personal and Collective Dreamscapes

Antoine Bellemare‐Pepin

Karim Jerbi CoCo Lab

Suzanne Kite

2025-11-04

Proceedings of the Conference on Animation and Interactive Art (publié)

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

2025-04-30

Communications Biology (publié)

Meditation induces shifts in neural oscillations, brain complexity and critical dynamics: Novel insights from MEG

Annalisa Pascarella

David Meunier

Jordan O’Byrne

Tarek Lajnef

Antonino Raffone

Roberto Guidotti

Vittorio Pizzella

Laura Marzetti

2025-03-19

bioRxiv (prépublication)

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (voir plus) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during NREM sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) during REM sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.

2025-01-05

bioRxiv (prépublication)

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (voir plus) especially during sleep. We analyzed sleep EEG in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during non-REM sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) whose sleep brain dynamics were less affected by caffeine. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance provides novel insights into the effects caffeine has on the sleeping brain.

2024-06-01

bioRxiv (prépublication)

Neuro-GPT: Towards A Foundation Model for EEG

Wenhui Cui

Woojae Jeong

Takfarinas Medani

Anand A. Joshi

Richard M. Leahy

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the p… (voir plus)ower of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

2024-05-27

2024 IEEE International Symposium on Biomedical Imaging (ISBI) (publié)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare‐Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare‐Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal.

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (voir plus)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs'semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence that LLMs can surpass average human performance on the Divergent Association Task, and approach human creative writing abilities, though they fall short of the typical performance of highly creative humans. Notably, even the top performing LLMs are still largely surpassed by highly creative individuals, underscoring a ceiling that current LLMs still fail to surpass. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labour by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)