Overview

This EACL 2026 tutorial will cover the foundations of brain–language model alignment and will then explore recent advances on scaling laws of language models for brain alignment, multilingual brain encoding, recent developments in fine-tuning language models with brain data, and advances in brain decoding using language models, including semantic reconstruction of continuous language from brain data. Participants will gain an overview of current naturalistic datasets, computational frameworks, and methods driving the emerging field of NeuroAI. ## Learning Objectives

• Understand the fundamental concepts of brain-AI alignment and encoding models
• Learn methodologies for comparing brain activity with model representations
• Gain insights into multilingual processing in both human brains and language models
• Master techniques for brain-informed model fine-tuning and evaluation
• Discover practical applications and future research directions in Neuro-AI

Target Audience

This tutorial is designed for researchers and practitioners with: - Basic understanding of deep learning and neural networks - Familiarity with transformer-based language models - Interest in interdisciplinary approaches to AI Our material assumes a basic knowledge of machine learning (e.g., foundations of linear models, regression, basic probability, and algebra) and deep learning, including familiarity with concepts such as word embeddings, Transformer models, and backpropagation. Some exposure to cognitive science or neuroscience, as well as programming experience in Python with libraries like PyTorch or scikit-learn, will also be helpful, though not strictly required.

Tutorial Schedule

Time	Topic	Speaker
9:00 - 9:45	Introduction to Brain Encoding and Decoding • Brain encoding modeling framework • Experimental design: Naturalistic language experiments • Feature spaces for language experiments • Model fitting and Visualization on brain flatmaps	Christine Tseng
9:45 - 10:30	Representations from LLMs for brain encoding models • Overview of contextual embeddings from LLMs • Contextual embeddings for brain encoding models • Analysis of the architectural features of LLMs which predict brain activtiy • Embeddings from multilingual LLMs	Mathis Lamarre
10:30 - 11:00	Coffee Break & Networking	-
11:00 - 11:40	Fine-tuning and scaling laws for language models with brain data • Fine-tuning with monolingual brain data • Fine-tuning with Bilingual brain data • Scaling laws for language models with brain data	Anuja Negi
11:40 - 12:20	Linguistic Brain Decoding • Semantic reconstruction of continuous language from non-invasive brain recordings • Auditory reconstruction from non-invasive brain	Subba Reddy Oota

Instructors

Tutorial Materials

Key Papers & References

This tutorial draws from cutting-edge research including:

Applications & Impact

Research Applications

• Developing more human-like AI systems
• Understanding model interpretability through neuroscience
• Designing better evaluation metrics
• Cross-modal learning and transfer

Industry Applications

• Enhanced multilingual systems for global markets
• Improved human-AI interaction
• Brain-inspired architectures for edge devices
• Cognitive load optimization in interfaces

Prerequisites

Technical Background:

• Familiarity with basic algebra
• Familiarity with PyTorch or TensorFlow
• Basic knowledge of transformers (BERT, GPT, etc.)
• Understanding of basic machine learning concepts

No Prior Neuroscience Background Required!

• All computational cognitive neuroscience concepts will be introduced from scratch
• Intuitive explanations with visual brainmaps
• Focus on the bridging between two systems

Registration & Contact

Tutorial Registration: Registration will be through the main EACL-2026 conference website.

Questions? Contact the instructor:

• Email: subbareddyoota@gmail.com
• Twitter: @subbareddy300
• GitHub: @subbareddy248

Stay Updated

Follow for updates on:

• Tutorial materials release
• Additional resources
• Post-tutorial discussions

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Looking forward to seeing you at EACL 2026! 🧠🤖