# Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner

arXiv: https://arxiv.org/abs/1607.08723 · HTML: https://mlsanity.com/paper/1607.08723/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2016.

## Built on by (most cited first, 30 of 68)

- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://mlsanity.com/paper/2301.11796/) (2023)
- [Embodied AI Agents: Modeling the World](https://mlsanity.com/paper/2506.22355/) (2025)
- [The paradox of the compositionality of natural language: a neural machine translation case study](https://mlsanity.com/paper/2108.05885/) (2021)
- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [Language acquisition: do children and language models follow similar learning stages?](https://mlsanity.com/paper/2306.03586/) (2023)
- [Word Discovery in Visually Grounded, Self-Supervised Speech Models](https://mlsanity.com/paper/2203.15081/) (2022)
- [Context Limitations Make Neural Language Models More Human-Like](https://mlsanity.com/paper/2205.11463/) (2022)
- [Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge](https://mlsanity.com/paper/2210.15759/) (2022)
- [BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models](https://mlsanity.com/paper/2306.01506/) (2023)
- [Investigating Critical Period Effects in Language Acquisition through Neural Language Models](https://mlsanity.com/paper/2407.19325/) (2024)
- [Evaluating context-invariance in unsupervised speech representations](https://mlsanity.com/paper/2210.15775/) (2022)
- [From Babble to Words: Pre-Training Language Models on Continuous Streams of Phonemes](https://mlsanity.com/paper/2410.22906/) (2024)
- [Do Construction Distributions Shape Formal Language Learning In German BabyLMs?](https://mlsanity.com/paper/2503.11593/) (2025)
- [Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science](https://mlsanity.com/paper/2603.15381/) (2026)
- [Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition](https://mlsanity.com/paper/2502.04795/) (2025)
- [A systematic framework for generating novel experimental hypotheses from language models](https://mlsanity.com/paper/2408.05086/) (2024)
- [Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study](https://mlsanity.com/paper/2106.08686/) (2021)
- [How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings](https://mlsanity.com/paper/2109.10179/) (2021)
- [On "Scientific Debt" in NLP: A Case for More Rigour in Language Model Pre-Training Research](https://mlsanity.com/paper/2306.02870/) (2023)
- [From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense Consistency](https://mlsanity.com/paper/2404.12145/) (2024)
- [Unsupervised Word Discovery: Boundary Detection with Clustering vs. Dynamic Programming](https://mlsanity.com/paper/2409.14486/) (2024)
- [Pre-training LLMs using human-like development data corpus](https://mlsanity.com/paper/2311.04666/) (2023)
- [A Distributional Perspective on Word Learning in Neural Language Models](https://mlsanity.com/paper/2502.05892/) (2025)
- [What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training](https://mlsanity.com/paper/2506.00981/) (2025)
- [Unpacking Let Alone: Human-Scale Models Generalize to a Rare Construction in Form but not Meaning](https://mlsanity.com/paper/2506.04408/) (2025)
- [Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?](https://mlsanity.com/paper/2507.19204/) (2025)

## Abstract (arXiv)

During their first years of life, infants learn the language(s) of their environment at an amazing speed despite large cross cultural variations in amount and complexity of the available language input. Understanding this simple fact still escapes current cognitive and linguistic theories. Recently, spectacular progress in the engineering science, notably, machine learning and wearable technology, offer the promise of revolutionizing the study of cognitive development. Machine learning offers powerful learning algorithms that can achieve human-like performance on many linguistic tasks. Wearable sensors can capture vast amounts of data, which enable the reconstruction of the sensory experience of infants in their natural environment. The project of 'reverse engineering' language development, i.e., of building an effective system that mimics infant's achievements appears therefore to be within reach. Here, we analyze the conditions under which such a project can contribute to our scientific understanding of early language development. We argue that instead of defining a sub-problem or simplifying the data, computational models should address the full complexity of the learning situation, and take as input the raw sensory signals available to infants. This implies that (1) accessible but privacy-preserving repositories of home data be setup and widely shared, and (2) models be evaluated at different linguistic levels through a benchmark of psycholinguist tests that can be passed by machines and humans alike, (3) linguistically and psychologically plausible learning architectures be scaled up to real data using probabilistic/optimization principles from machine learning. We discuss the feasibility of this approach and present preliminary results.