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Contrasting Cognitive Styles in Vision-Language Models: Holistic Attention in Japanese Versus Analytical Focus in English

arXiv 2507.00700 ↗First posted 1 Jul 2025cs.CLcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

Cross-cultural research in perception and cognition has shown that individuals from different cultural backgrounds process visual information in distinct ways. East Asians, for example, tend to adopt a holistic perspective, attending to contextual relationships, whereas Westerners often employ an analytical approach, focusing on individual objects and their attributes. In this study, we investigate whether Vision-Language Models (VLMs) trained predominantly on different languages, specifically Japanese and English, exhibit similar culturally grounded attentional patterns. Using comparative analysis of image descriptions, we examine whether these models reflect differences in holistic versus analytic tendencies. Our findings suggest that VLMs not only internalize the structural properties of language but also reproduce cultural behaviors embedded in the training data, indicating that cultural cognition may implicitly shape model outputs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 8 of its 24 references that are in the map. First posted on arXiv 1 Jul 2025.

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8 of 24 references.

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  1. 2024Heron-Bench: A Benchmark for Evaluating Vision Language Models in Japanese11 Apr

Other references · 7

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2020End-to-End Object Detection with Transformerscited by 7,414
  4. 2025Gemma 3 Technical Reportcited by 1,824
  5. 2015Microsoft COCO Captions: Data Collection and Evaluation Servercited by 1,462
  6. 2024Evolutionary Optimization of Model Merging Recipescited by 208
  7. 2021You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detectioncited by 170

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  1. 2024See It from My Perspective: How Language Affects Cultural Bias in Image Understanding
  2. 2025Evaluation of Cultural Competence of Vision-Language Models
  3. 2025Cultural Awareness in Vision-Language Models: A Cross-Country Exploration
  4. 2024From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models
  5. 2025Toward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation
  6. 2025Uncovering Cultural Representation Disparities in Vision-Language Models
  7. 2024Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach
  8. 2025Multilingual Vision-Language Models, A Survey