10 citations · 26 across the 9 of their papers we have counts for
9 papers
The in-context inductive biases of vision-language models differ across modalities
Kelsey Allen, Ishita Dasgupta, Eliza Kosoy +1
Inductive biases are what allow learners to make guesses in the absence of conclusive evidence. These biases have often been studied in cognitive science using concepts or categori…
Decoupling the components of geometric understanding in Vision Language Models
Eliza Kosoy, Annya Dahmani, Andrew K. Lampinen +4
Understanding geometry relies heavily on vision. In this work, we evaluate whether state-of-the-art vision language models (VLMs) can understand simple geometric concepts. We use a…
Intrinsically-Motivated Humans and Agents in Open-World Exploration
Aly Lidayan, Yuqing Du, Eliza Kosoy +3
What drives exploration? Understanding intrinsic motivation is a long-standing challenge in both cognitive science and artificial intelligence; numerous objectives have been propos…
Children's Mental Models of Generative Visual and Text Based AI Models
Eliza Kosoy, Soojin Jeong, Anoop Sinha +2
In this work we investigate how children ages 5-12 perceive, understand, and use generative AI models such as a text-based LLMs ChatGPT and a visual-based model DALL-E. Generative…
Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?
Eunice Yiu, Eliza Kosoy, Alison Gopnik
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. We arg…
Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses
Eliza Kosoy, Emily Rose Reagan, Leslie Lai +2
Developmental psychologists have spent decades devising experiments to test the intelligence and knowledge of infants and children, tracing the origin of crucial concepts and capac…
Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu +7
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature…
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Eliza Kosoy, Adrian Liu, Jasmine Collins +7
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics,…
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Eliza Kosoy, Jasmine Collins, David M. Chan +6
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this ear…