# Concepts Learned Visually by Infants Can Contribute to Visual Learning and Understanding in AI Models Source: [Concepts Learned Visually by Infants Can Contribute to Visual Learning and Understanding in AI Models]() Collection index: [llms.txt]() Data updated: 2026-03-28T05:00:04.913Z Authors: Shify Treger, Shimon Ullman Published: 2025-03-05T10:40:19.000Z ## Abstract Early in development, infants learn to extract surprisingly complex aspects of visual scenes. This early learning comes together with an initial understanding of the extracted concepts, such as their implications, causality, and using them to predict likely future events. In many cases, this learning is obtained with little or no supervision, and from relatively few examples, compared to current network models. Empirical studies of visual perception in early development have shown that in the domain of objects and human-object interactions, early-acquired concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events in dynamic visual scenes. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data), and that the combination of early and new concepts shapes the representation of the concepts acquired by the model and improves its generalization. We further compare advanced vision-language models to a human study in a task that requires an understanding of the behavior of animate vs. inanimate agents, with results supporting the contribution of early concepts to visual understanding. We finally briefly discuss the possible benefits of incorporating aspects of human-like visual learning into computer vision models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Behavioral Prediction on Woodward-like Behavioral Prediction Animate](): Behavioral Prediction; Woodward-like Behavioral Prediction Animate; Accuracy: not recorded. - [Behavioral Prediction on Woodward-like Behavioral Prediction Non-animate](): Behavioral Prediction; Woodward-like Behavioral Prediction Non-animate; Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()