Shifting Attention to You: Personalized Brain-Inspired AI Models

The integration of human and artificial intelligence represents a scientific opportunity to advance our understanding of information processing, as each system offers unique computational insights that can enhance and inform the other. The synthesis of human cognitive principles with artificial intelligence has the potential to produce more interpretable and functionally aligned computational models, while simultaneously providing a formal framework for investigating the neural mechanisms underlying perception, learning, and decision-making through systematic model comparisons and representational analyses. In this study, we introduce personalized brain-inspired modeling that integrates human behavioral embeddings and neural data to align with cognitive processes. We took a stepwise approach, fine-tuning the Contrastive Language-Image Pre-training (CLIP) model with large-scale behavioral decisions, group-level neural data, and finally, participant-level neural data within a broader framework that we have named CLIP-Human-Based Analysis (CLIP-HBA). We found that fine-tuning on behavioral data enhances its ability to predict human similarity judgments while indirectly aligning it with dynamic representations captured via MEG. To further gain mechanistic insights into the temporal evolution of cognitive processes, we introduced a model specifically fine-tuned on millisecond-level MEG neural dynamics (CLIP-HBA-MEG). This model resulted in enhanced temporal alignment with human neural processing while still showing improvement on behavioral alignment. Finally, we trained individualized models on participant-specific neural data, effectively capturing individualized neural dynamics and highlighting the potential for personalized AI systems. These personalized systems have far-reaching implications for the fields of medicine, cognitive research, human-computer interfaces, and AI development.

AI Literature Review Suite

The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files, as well as extracting content from articles. Semantic search queries are used for data retrieval, while text embeddings and summarization using LLMs present succinct literature reviews. Interaction with PDFs is enhanced through a user-friendly graphical user interface (GUI). The suite also features integrated programs for bibliographic organization, interaction and query, and literature review summaries. This tool presents a robust solution to automate and optimize the process of literature review in academic and industrial research.