BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain

• URL: http://arxiv.org/abs/2512.08560v2
• Date: Fri, 12 Dec 2025 06:48:23 GMT
• Title: BrainExplore: Large-Scale Discovery of Interpretable Visual Representations in the Human Brain
• Authors: Navve Wasserman, Matias Cosarinsky, Yuval Golbari, Aude Oliva, Antonio Torralba, Tamar Rott Shaham, Michal Irani,
• Abstract summary: We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex.<n>Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods.<n>Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning.
• Score: 33.91441575463702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding how the human brain represents visual concepts, and in which brain regions these representations are encoded, remains a long-standing challenge. Decades of work have advanced our understanding of visual representations, yet brain signals remain large and complex, and the space of possible visual concepts is vast. As a result, most studies remain small-scale, rely on manual inspection, focus on specific regions and properties, and rarely include systematic validation. We present a large-scale, automated framework for discovering and explaining visual representations across the human cortex. Our method comprises two main stages. First, we discover candidate interpretable patterns in fMRI activity through unsupervised, data-driven decomposition methods. Next, we explain each pattern by identifying the set of natural images that most strongly elicit it and generating a natural-language description of their shared visual meaning. To scale this process, we introduce an automated pipeline that tests multiple candidate explanations, assigns quantitative reliability scores, and selects the most consistent description for each voxel pattern. Our framework reveals thousands of interpretable patterns spanning many distinct visual concepts, including fine-grained representations previously unreported.

Related papers

• Learning Brain Representation with Hierarchical Visual Embeddings [30.701493890961284]
  We propose a brain-image alignment strategy that leverages pre-trained visual encoders with distinct inductive biases to capture hierarchical and multi-scale visual representations.<n>Our method achieves a favorable balance between retrieval accuracy and reconstruction fidelity.
  arXiv  Detail & Related papers  (2026-02-07T11:14:03Z)
• Toward Cognitive Supersensing in Multimodal Large Language Model [67.15559571626747]
  We introduce Cognitive Supersensing, a training paradigm that endows MLLMs with human-like visual imagery capabilities.<n>In experiments, MLLMs trained with Cognitive Supersensing significantly outperform state-of-the-art baselines on CogSense-Bench.<n>We will open-source the CogSense-Bench and our model weights.
  arXiv  Detail & Related papers  (2026-02-02T02:19:50Z)
• Latent Implicit Visual Reasoning [59.39913238320798]
  We propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision.<n>Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks.
  arXiv  Detail & Related papers  (2025-12-24T14:59:49Z)
• Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models [51.900488744931785]
  We introduce the Visual Graph Arena (VGA) to evaluate and improve AI systems' capacity for visual abstraction.<n>Humans achieve near-perfect accuracy across tasks, while models totally failed on isomorphism detection and showed limited success in path/cycle tasks.<n>By isolating the challenge of representation-invariant reasoning, the VGA provides a framework to drive progress toward human-like conceptualization in AI visual models.
  arXiv  Detail & Related papers  (2025-06-06T17:06:25Z)
• Exploring The Visual Feature Space for Multimodal Neural Decoding [5.19485079754946]
  We analyze different choices of vision feature spaces from pre-trained visual components within Multimodal Large Language Models (MLLMs)<n>We propose the Multi-Granularity Brain Detail Understanding Benchmark (MG-BrainDub)<n>This benchmark includes two key tasks: detailed descriptions and salient question-answering, with metrics highlighting key visual elements like objects, attributes, and relationships.
  arXiv  Detail & Related papers  (2025-05-21T17:01:08Z)
• Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models [57.58426038241812]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks.<n>These models still suffer from hallucinations when required to implicitly recognize or infer diverse visual entities from images.<n>We propose a novel visual question answering (VQA) benchmark that employs contextual reasoning prompts as hallucination attacks.
  arXiv  Detail & Related papers  (2024-12-29T23:56:01Z)
• Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models [10.615012396285337]
  We develop algorithms to enhance our understanding of visual processes by incorporating whole-brain activation maps. We first compare our method with state-of-the-art approaches to decoding visual processing and show improved predictive semantic accuracy by 43%.
  arXiv  Detail & Related papers  (2024-11-11T16:51:17Z)
• Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers [5.265058307999745]
  We introduce BrainSAIL, a method for linking neural selectivity with spatially distributed semantic visual concepts in natural scenes.<n>BrainSAIL exploits semantically consistent, dense spatial features from pre-trained vision models.<n>We validate BrainSAIL on cortical regions with known category selectivity.
  arXiv  Detail & Related papers  (2024-10-07T17:59:45Z)
• What Makes a Maze Look Like a Maze? [92.80800000328277]
  We introduce Deep Grounding (DSG), a framework that leverages explicit structured representations of visual abstractions for grounding and reasoning.<n>At the core of DSG are schemas--dependency graph descriptions of abstract concepts that decompose them into more primitive-level symbols.<n>We show that DSG significantly improves the abstract visual reasoning performance of vision-language models.
  arXiv  Detail & Related papers  (2024-09-12T16:41:47Z)
• Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction [8.63068449082585]
  Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition. Our framework integrates 3D brain structures with visual semantics using a Vision Transformer 3D. We have enhanced the fMRI dataset with diverse fMRI-image-related textual data to support multimodal large model development.
  arXiv  Detail & Related papers  (2024-04-30T10:41:23Z)
• Visual Superordinate Abstraction for Robust Concept Learning [80.15940996821541]
  Concept learning constructs visual representations that are connected to linguistic semantics. We ascribe the bottleneck to a failure of exploring the intrinsic semantic hierarchy of visual concepts. We propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces.
  arXiv  Detail & Related papers  (2022-05-28T14:27:38Z)
• Behind the Machine's Gaze: Biologically Constrained Neural Networks Exhibit Human-like Visual Attention [40.878963450471026]
  We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. We show that the proposed method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths.
  arXiv  Detail & Related papers  (2022-04-19T18:57:47Z)
• Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs [106.15931418425906]
  We present the first study focused on generating natural language rationales across several complex visual reasoning tasks. We present RationaleVT Transformer, an integrated model that learns to generate free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. Our experiments show that the base pretrained language model benefits from visual adaptation and that free-text rationalization is a promising research direction to complement model interpretability for complex visual-textual reasoning tasks.
  arXiv  Detail & Related papers  (2020-10-15T05:08:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.