Categorization, perception, and language

Latest papers

• Human-AI Perceptual Alignment by Playing Hues and Cues

  Nuria Alabau-Bosque, Jorge Vila-Tomás, Paula Daudén-Oliver, Pablo Hernández-Cámara, Valero Laparra, Jesús Malo · 10 August 2026

  Evaluating the perceptual alignment between Contrastive Vision-Language Models (CVLMs) and humans is typically constrained by traditional benchmarks that overlook fine-grained semantic and cultural nuances. In this work, we propose a novel evaluation framework that leverages the gamified, discrete c…

• Learning a Vector-Symbolic Model for Socio-Cultural Tasks

  Meera Ray, Swapnika Dulam, Christopher L. Dancy · 5 August 2026

  How can we better represent the impact of sociocultural structures on decision making in computational cognitive models? Modeling this impact requires traversing multiple levels of semantic representation, however it is not immediately clear to a modeler which levels of representation are most salie…

• Constructing Parallel Multidimensional Chromatic Lexicons for Corpus-Assisted Analysis of Russian and English Texts

  Larisa Nikitina · 4 August 2026

  This article addresses the relative scarcity of research tools for the corpus-assisted linguistic analysis of colour terms in literary texts. It describes the development of two multidimensional chromatic lexicons: one for Russian (224 entries) and one for English (141 entries). Lexicon construction…

• Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models

  Ayan Igali, Pakizar Shamoi · 16 July 2026

  Do vision models see colors the way humans do? Existing evaluations of color representations usually compare them with geometric spaces such as CIELAB or with discrete color labels. These references capture perceptual distance or category membership, but not the graded way in which people organize c…

• Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment

  Nuray Toganas, Adilet Yerkin, Elnara Kadyrgali, Muragul Muratbekova, Aron Karatayev, Pakizar Shamoi · 14 July 2026

  Color naming is an important part of human color perception. Its task is to allow people to describe continuous colors using discrete color categories. However, the boundaries between color categories are often unclear, and some colors may be perceived differently depending on their saturation and b…

• Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts

  Arianna Pera, Mauro Martino, Nima Dehmamy, Douglas Guilbeault, Luca Maria Aiello, Andrea Baronchelli · 9 July 2026

  Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures. However, words are effective as communication devices because they compress rich experiential variation into shared conventions, potentially obscuring hidden …

• Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

  Elnara Kadyrgali, Nuray Toganas, Muragul Muratbekova, Pakizar Shamoi · 12 May 2026

  Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured representations. In this paper, we present a focused analytical extension of the COLIBRI fuzzy color model by investigating perceptual asymmetry betw…

• Modeling Human-Like Color Naming Behavior in Context

  Yuqing Zhang, Ecesu Ürker, Tessa Verhoef, Gemma Boleda, Arianna Bisazza · 29 April 2026

  Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both learning and communicative pressures. The NeLLCom-Lex framework (Zhang et al., 2025) allows neural agents to develop pragmatic color naming behavior a…

• Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

  Yu Wang, Emmanuele Chersoni, Chu-Ren Huang · 29 April 2026

  Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "zhè/nà" in Chinese, as a novel probe for grounded knowledge. Using 6,400 responses from 320 native speake…

• Do LLMs Use Cultural Knowledge Without Being Told? A Multilingual Evaluation of Implicit Pragmatic Adaptation

  Mehwish Nasim, Sanjeevan Selvaganapathy, Neel Ganapathi Sabhahit, Marie Griesbach, Pranav Bhandari, Janina Lütke Stockdiek, Lennart Schäpermeier, Usman Naseem, Christian Grimme · 21 April 2026

  Many benchmarks show that large language models can answer direct questions about culture. We study a different question: do they also change how they speak when culture is only implied by the situation? We evaluate 60 culturally grounded conversational scenarios across five languages in three condi…

• Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data

  Aruzhan Sabitkyzy, Maksat Shagyrov, Pakizar Shamoi · 7 April 2026

  Is it coral, salmon, or peach? What seems like a simple color can have many names, and without a standard, these variations create confusion across design, technology, and communication. Color naming is a fundamental task across industries such as fashion, cosmetics, web design, and visualization to…

• Categorical Perception in Large Language Model Hidden States: Structural Warping at Digit-Count Boundaries

  Jon-Paul Cacioli · 31 March 2026

  Categorical perception (CP) -- enhanced discriminability at category boundaries -- is among the most studied phenomena in perceptual psychology. This paper reports that analogous geometric warping occurs in the hidden-state representations of large language models (LLMs) processing Arabic numerals. …

• Translation from the Information Bottleneck Perspective: an Efficiency Analysis of Spatial Prepositions in Bitexts

  Antoine Taroni, Ludovic Moncla, Frederique Laforest · 23 March 2026

  Efficient communication requires balancing informativity and simplicity when encoding meanings. The Information Bottleneck (IB) framework captures this trade-off formally, predicting that natural language systems cluster near an optimal accuracy-complexity frontier. While supported in visual domains…

• Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?

  Matteo Gay, Coleman Haley, Mario Giulianelli, Edoardo Ponti · 17 February 2026

  The Uniform Information Density (UID) hypothesis posits that speakers are subject to a communicative pressure to distribute information evenly within utterances, minimising surprisal variance. While this hypothesis has been tested empirically, prior studies are limited exclusively to text-only input…

• How Do People Quantify Naturally: Evidence from Mandarin Picture Description

  Yayun Zhang, Guanyi Chen, Fahime Same, Saad Mahamood, Tingting He · 11 February 2026

  Quantification is a fundamental component of everyday language use, yet little is known about how speakers decide whether and how to quantify in naturalistic production. We investigate quantification in Mandarin Chinese using a picture-based elicited description task in which speakers freely describ…

• ColorConceptBench: A Benchmark for Probabilistic Color-Concept Understanding in Text-to-Image Models

  Chenxi Ruan, Yihan Hou, Yu Xiao, Guosheng Hu, Wei Zeng · 26 January 2026

  Text-to-image (T2I) models have advanced considerably in generating high-quality images from textual descriptions. However, their ability to associate colors with concepts remains largely constrained to explicit color names or codes, while their capacity to handle \emph{implicit concepts}, such as e…

• ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

  Yijun Liang, Ming Li, Chenrui Fan, Ziyue Li, Dang Nguyen, Kwesi Cobbina, Shweta Bhardwaj, Jiuhai Chen, Fuxiao Liu, Tianyi Zhou · 11 November 2025

  Color plays an important role in human perception and usually provides critical clues in visual reasoning. However, it is unclear whether and how vision-language models (VLMs) can perceive, understand, and leverage color as humans. This paper introduces ColorBench, an innovative benchmark meticulous…

• Breaking Language Barriers or Reinforcing Bias? A Study of Gender and Racial Disparities in Multilingual Contrastive Vision Language Models

  Zahraa Al Sahili, Ioannis Patras, Matthew Purver · 28 October 2025

  Multilingual vision-language models (VLMs) promise universal image-text retrieval, yet their social biases remain underexplored. We perform the first systematic audit of four public multilingual CLIP variants: M-CLIP, NLLB-CLIP, CAPIVARA-CLIP, and the debiased SigLIP-2, covering ten languages that d…