ArticleScientific reports2024
Large language models predict human sensory judgments across six modalities.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Contextualized sensorimotor norms: Multi-dimensional measures of sensorimotor strength for ambiguous English words, in context.Behavior research methods · 2026Article
- The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based participatory research design.Rehabilitation psychology · 2026Article
- A flexible behavioral method for measuring human and artificial intelligence alignment using representational similarity analysis.iScience · 2026Article
- Understanding large language models demands distinguishing human projection from machine cognition.Communications psychology · 2026Review
- How well do large language models mirror human cognition of word concepts?: A comparison of psychological ratings for early-acquired English words.Behavior research methods · 2026Article
- ChatGPT does not replicate human moral judgments: the importance of examining metrics beyond correlation to assess agreement.Scientific reports · 2025Article
- Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts.Nature human behaviour · 2025Article
- Correspondence of high dimensional emotion structures elicited from video clips between humans and multimodal LLMs.Scientific reports · 2025Article
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- Attention heads of large language models.Patterns (New York, N.Y.) · 2025Review
- Leveraging Large Language Models to Estimate Clinically Relevant Psychological Constructs in Psychotherapy Transcripts.Computational psychiatry (Cambridge, Mass.) · 2025Article
- From text to motion: grounding GPT-4 in a humanoid robot "Alter3".Frontiers in robotics and AI · 2025Article
- Gromov-Wasserstein unsupervised alignment reveals structural correspondences between the color similarity structures of humans and large language models.Scientific reports · 2024Article
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Authors and funding
5 authors.
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Abstract
Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art large language models can unlock new insights into this problem by providing a lower bound on the amount of perceptual information that can be extracted from language. Specifically, we elicit pairwise similarity judgments from GPT models across six psychophysical datasets. We show that the judgments are significantly correlated with human data across all domains, recovering well-known representations like the color wheel and pitch spiral. Surprisingly, we find that a model (GPT-4) co-trained on vision and language does not necessarily lead to improvements specific to the visual modality, and provides highly correlated predictions with human data irrespective of whether direct visual input is provided or purely textual descriptors. To study the impact of specific languages, we also apply the models to a multilingual color-naming task. We find that GPT-4 replicates cross-linguistic variation in English and Russian illuminating the interaction of language and perception.
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