# Shifting Attention to You: Personalized Brain-Inspired AI Models

arXiv: https://arxiv.org/abs/2502.04658 · HTML: https://mlsanity.com/paper/2502.04658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Human alignment of neural network representations](https://mlsanity.com/paper/2211.01201/) (2022)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [The neuroconnectionist research programme](https://mlsanity.com/paper/2209.03718/) (2022)
- [Improving neural network representations using human similarity judgments](https://mlsanity.com/paper/2306.04507/) (2023)
- [Dimensions underlying the representational alignment of deep neural networks with humans](https://mlsanity.com/paper/2406.19087/) (2024)
- [Revealing interpretable object representations from human behavior](https://mlsanity.com/paper/1901.02915/) (2019)
- [Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data](https://mlsanity.com/paper/1606.02840/) (2016)
- [Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion](https://mlsanity.com/paper/2411.09723/) (2024)
- [The Universal Law of Generalization Holds for Naturalistic Stimuli](https://mlsanity.com/paper/2306.08564/) (2023)
- [Achieving More Human Brain-Like Vision via Human EEG Representational Alignment](https://mlsanity.com/paper/2401.17231/) (2024)
- [Do Invariances in Deep Neural Networks Align with Human Perception?](https://mlsanity.com/paper/2111.14726/) (2021)
- [Scale-specific dynamics of large-amplitude bursts in EEG capture behaviorally meaningful variability](https://mlsanity.com/paper/2003.06463/) (2020)
- [Computational limits to the legibility of the imaged human brain](https://mlsanity.com/paper/2309.07096/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment](https://mlsanity.com/paper/2505.14204/) (2025)

## Abstract (arXiv)

The integration of human and artificial intelligence offers a powerful avenue for advancing our understanding of information processing, as each system provides unique computational insights. However, despite the promise of human-AI integration, current AI models are largely trained on massive datasets, optimized for population-level performance, lacking mechanisms to align their computations with individual users' perceptual semantics and neural dynamics. Here we show that integrating human behavioral insights and millisecond scale neural data within a fine tuned CLIP based model not only captures generalized and individualized aspects of perception but also over doubles behavioral performance compared to the unmodified CLIP baseline. By embedding human inductive biases and mirroring dynamic neural processes during training, personalized neural fine tuning improves predictions of human similarity judgments and tracks the temporal evolution of individual neural responses. Our work establishes a novel, interpretable framework for designing adaptive AI systems, with broad implications for neuroscience, personalized medicine, and human-computer interaction.