@inproceedings{sun-etal-2026-language,
title = "Language Models as Windows into Human Language Processing: Neural Encoding, Decoding, and Brain Alignment with {NLP}",
author = "Sun, Jingyuan and
Zhang, Yunhao and
Wang, Yifan and
Cui, Yang",
editor = "Marasovi{\'c}, Ana and
Glava{\v{s}}, Goran",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-tutorials.2/",
pages = "2--3",
ISBN = "979-8-89176-463-7",
abstract = "Modern language models are not only tools for processing text; they are also becoming computational instruments for studying how humans understand, predict, and generate language. This tutorial introduces neural encoding and neural decoding to the NLP community through this broader perspective: how can NLP help us understand language processing in the human brain, and how can brain data help us evaluate and improve language models? We will present neural encoding as a way to test whether representations from language models{---}such as contextual embeddings, surprisal, attention patterns, and layer-wise features{---}can explain brain responses during reading or listening. We will then introduce neural decoding as the reverse problem: reconstructing linguistic or multimodal content from brain activity using retrieval, representation learning, and foundation-model-guided generation. Rather than framing decoding mainly as brain{--}computer interface technology, we will connect it to core NLP questions about meaning representation, generation under uncertainty, multimodal grounding, and semantic evaluation. The tutorial assumes no neuroscience background. It combines intuitive foundations, representative papers, practical modeling recipes, notebook-style demos, and discussion of mental privacy, consent, and responsible brain{--}language technologies."
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%0 Conference Proceedings
%T Language Models as Windows into Human Language Processing: Neural Encoding, Decoding, and Brain Alignment with NLP
%A Sun, Jingyuan
%A Zhang, Yunhao
%A Wang, Yifan
%A Cui, Yang
%Y Marasović, Ana
%Y Glavaš, Goran
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-463-7
%F sun-etal-2026-language
%X Modern language models are not only tools for processing text; they are also becoming computational instruments for studying how humans understand, predict, and generate language. This tutorial introduces neural encoding and neural decoding to the NLP community through this broader perspective: how can NLP help us understand language processing in the human brain, and how can brain data help us evaluate and improve language models? We will present neural encoding as a way to test whether representations from language models—such as contextual embeddings, surprisal, attention patterns, and layer-wise features—can explain brain responses during reading or listening. We will then introduce neural decoding as the reverse problem: reconstructing linguistic or multimodal content from brain activity using retrieval, representation learning, and foundation-model-guided generation. Rather than framing decoding mainly as brain–computer interface technology, we will connect it to core NLP questions about meaning representation, generation under uncertainty, multimodal grounding, and semantic evaluation. The tutorial assumes no neuroscience background. It combines intuitive foundations, representative papers, practical modeling recipes, notebook-style demos, and discussion of mental privacy, consent, and responsible brain–language technologies.
%U https://aclanthology.org/2026.emnlp-tutorials.2/
%P 2-3
Markdown (Informal)
[Language Models as Windows into Human Language Processing: Neural Encoding, Decoding, and Brain Alignment with NLP](https://aclanthology.org/2026.emnlp-tutorials.2/) (Sun et al., EMNLP 2026)
ACL