# Learning as the Unsupervised Alignment of Conceptual Systems

arXiv: https://arxiv.org/abs/1906.09012 · HTML: https://mlsanity.com/paper/1906.09012/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Models](https://mlsanity.com/paper/2405.09605/) (2024)
- [Event knowledge in large language models: the gap between the impossible and the unlikely](https://mlsanity.com/paper/2212.01488/) (2022)
- [Dissociating language and thought in large language models](https://mlsanity.com/paper/2301.06627/) (2023)
- [Alignment with human representations supports robust few-shot learning](https://mlsanity.com/paper/2301.11990/) (2023)
- [Human-like object concept representations emerge naturally in multimodal large language models](https://mlsanity.com/paper/2407.01067/) (2024)
- [Dimensions underlying the representational alignment of deep neural networks with humans](https://mlsanity.com/paper/2406.19087/) (2024)
- [Probabilistic Analogical Mapping with Semantic Relation Networks](https://mlsanity.com/paper/2103.16704/) (2021)
- [Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization](https://mlsanity.com/paper/2310.12794/) (2023)
- [Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment](https://mlsanity.com/paper/2312.01592/) (2023)
- [Cross-Modal Alignment Learning of Vision-Language Conceptual Systems](https://mlsanity.com/paper/2208.01744/) (2022)
- [Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search](https://mlsanity.com/paper/2309.16141/) (2023)
- [Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition](https://mlsanity.com/paper/2304.02492/) (2023)
- [What Remains of Visual Semantic Embeddings](https://mlsanity.com/paper/2107.11991/) (2021)

## Abstract (arXiv)

Concept induction requires the extraction and naming of concepts from noisy perceptual experience. For supervised approaches, as the number of concepts grows, so does the number of required training examples. Philosophers, psychologists, and computer scientists, have long recognized that children can learn to label objects without being explicitly taught. In a series of computational experiments, we highlight how information in the environment can be used to build and align conceptual systems. Unlike supervised learning, the learning problem becomes easier the more concepts and systems there are to master. The key insight is that each concept has a unique signature within one conceptual system (e.g., images) that is recapitulated in other systems (e.g., text or audio). As predicted, children's early concepts form readily aligned systems.