ArticlePerspectives on psychological science : a journal of the Association for Psychological Science2024
Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet).
Article in Perspectives on psychological science : a journal of the Association for Psychological Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- AI models as cultural beings: Investigating AI cultural biases and the impact of cultural alignment on human-AI creative collaboration.PNAS nexus · 2026Article
- Knowledge in Animals and Machines.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Sequence structure in children's speech reveals non-linear development of relations between word categories.Communications psychology · 2025Article
- The Effects of (Dis)similarities Between the Creator and the Assessor on Assessing Creativity: A Comparison of Humans and LLMs.Journal of Intelligence · 2025Article
- Large language models are proficient in solving and creating emotional intelligence tests.Communications psychology · 2025Article
- Culturally-attuned AI: Implicit learning of altruistic cultural values through inverse reinforcement learning.PloS one · 2025Article
- Dissociating language and thought in large language models.Trends in cognitive sciences · 2024Review
- Large language model answers medical questions about standard pathology reports.Frontiers in medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.