Questions for Uri Hasson concerning his talk on "Deep language models as a cognitive model for natural language processing in the human brain."

[jamesallenevans]

Post your questions for Uri Hasson about his talk and paper: Deep language models as a cognitive model for natural language processing in the human brain. Naturalistic experimental paradigms in cognitive neuroscience arose from a pressure to test, in real-world contexts, the validity of models we derive from highly controlled laboratory experiments. In many cases, however, such efforts led to the realization that models (i.e., explanatory principles) developed under particular experimental manipulations fail to capture many aspects of reality (variance) in the real world. Recent advances in artificial neural networks provide an alternative computational framework for modeling cognition in natural contexts. In this talk, I will ask whether the human brain's underlying computations are similar or different from the underlying computations in deep neural networks, focusing on the underlying neural process that supports natural language processing in adults and language development in children. I will provide evidence for some shared computational principles between deep language models and the neural code for natural language processing in the human brain. This indicates that, to some extent, the brain relies on overparameterized optimization methods to comprehend and produce language. At the same time, I will present evidence that the brain differs from deep language models as speakers try to convey new ideas and thoughts. Finally, I will discuss our ongoing attempt to use deep acoustic-to-speech-to-language models to model language acquisition in children.

[zcyou018]

Considering that deep language models and the human brain may share some computational principles, how can we leverage this similarity to improve our understanding of human cognition, particularly in the context of natural language processing, while still addressing the key differences between these models and human-centric attributes?

[anzhichen1999]

In your study, you align the computational principles of autoregressive deep language models (DLMs) with the cognitive processes of the human brain during language processing. Knowing that these models do not engage in semantic or syntactic analysis but rather rely on statistical language patterns learned from large data sets, how might this alignment influence our understanding of the neurobiological underpinnings of language?

[Dededon]

Hi Professor Hasson, thank you for your talk! I am interested in how current Large Language Models could be applied in reasoning of neural network research?

[Jessieliao2001]

Thanks for your sharing Prof. Hasson! I have a question: To what extent can deep language models serve as an accurate cognitive model for natural language processing in the human brain, considering both shared computational principles and disparities observed during language comprehension and production?

[bhavyapan]

Thank you for sharing your work! Does the insight from such studies vary with languages or can be applicable to specific settings only? I recall coming across this study where the authors found that contrary to predictions, the large-scale language models were shown to be ineffective for representing children’s utterances as they tried to use a deep learning-based language development screening model based on word and part-of-speech to investigate its effectiveness in detecting language learning impediments in children.

Oh, Byoung-Doo, Yoon-Kyoung Lee, Jong-Dae Kim, Chan-Young Park, and Yu-Seop Kim. 2022. "Deep Learning-Based End-to-End Language Development Screening for Children Using Linguistic Knowledge" Applied Sciences 12, no. 9: 4651. https://doi.org/10.3390/app12094651

[AnniiiinnA]

The study suggests that large-scale language models, although effective in general natural language processing tasks, do not perform as expected in representing the nuances of children's speech. Given the unique linguistic features of early language development, such as incomplete grammatical structures and limited vocabulary, could the adaptation of these models to focus more on phonetic and prosodic features rather than traditional lexical and syntactic elements improve their applicability in developmental language screening? What other modifications could be considered to enhance the performance of these models in the context of early language development?

[jiayan-li]

Hi Professor Hasson, I'm intrigued by how cognitive research intersects with various deep learning model architectures, such as LSTM and Transformers, along with techniques like prompt engineering tailored for Large Language Models (LLMs). Given the dynamic nature of NLP research within computer science, how do we navigate the integration of these diverse approaches into cognitive studies? Are we primarily focused on exploring the fundamental parallels with neural networks in our cognitive research endeavors?

[ymuhannah]

Thanks for sharing! One of your studies focuses on predictions and neural responses primarily within English-speaking contexts. How might these computational principles adapt or vary when applied to languages with significantly different syntactic or morphological structures, such as agglutinative languages like Turkish or languages with varying script like Chinese?

[XiaotongCui]

Thanks for sharing! Given the current findings, how should future research further explore and utilize deep language models to enhance our understanding of the mechanisms underlying human language processing?

[maxzhuyt]

Hi Prof. Hasson, thanks for sharing your fascinating research. What would be the link from modeling individual language processing to modeling collective cultural behaviors? How might language models assist in such endeavor?

[PaulaTepkham]

Thank you for sharing your paper with us! From my perspective, the most interesting question that we need to think further is that should we develop the computer to think like human or not. Since there are a variety of human thought and way of life. Some is so erratic. I still think that it is a good idea to be able to distinguish between computer thought and human thought. I am looking forward for the talk!

[shaangao]

Thank you so much for sharing your research with us, Uri! One major question regarding the human brain vs LLM comparison is the amount of training data available, i.e., LLMs are trained on a huge amount of data, whereas human children need to learn languages through a very limited amount of examples. I look forward to hearing about the findings of your language acquisition project, and more generally your take on developmentally plausible language models.

[Kevin2330]

Thank you, could you elaborate on which specific computational mechanisms are shared, and which are distinctly different when the brain processes new ideas compared to deep neural networks? Based on your findings, what are the next steps in refining these models to better align with human brain functions?

[ecg1331]

Thank you so much for sharing your research!

With the similarities you have pointed out between DLMs and the human brain, do you think there are currently more? And if not, do you think it's possible for DLMs to continue to develop to be more similar?

[kexinz330]

Thank you for sharing your research! You discuss the use of artificial neural networks to model cognition in natural contexts. Could you elaborate on how these models might be integrated into existing cognitive neuroscience methodologies to enhance our understanding of brain function?