The Emergence of the Shape Bias Results from Communicative Efficiency
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Abstract (arXiv)
By the age of two, children tend to assume that new word categories are based on objects' shape, rather than their color or texture; this assumption is called the shape bias. They are thought to learn this bias by observing that their caregiver's language is biased towards shape based categories. This presents a chicken and egg problem: if the shape bias must be present in the language in order for children to learn it, how did it arise in language in the first place? In this paper, we propose that communicative efficiency explains both how the shape bias emerged and why it persists across generations. We model this process with neural emergent language agents that learn to communicate about raw pixelated images. First, we show that the shape bias emerges as a result of efficient communication strategies employed by agents. Second, we show that pressure brought on by communicative need is also necessary for it to persist across generations; simply having a shape bias in an agent's input language is insufficient. These results suggest that, over and above the operation of other learning strategies, the shape bias in human learners may emerge and be sustained by communicative pressures.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 13 Sep 2021.
Builds on
26 of 62 references.
References · 26
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
- 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
- 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
- 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
- 2021Understanding Robustness of Transformers for Image Classificationcited by 250
- 2016Multi-Agent Cooperation and the Emergence of (Natural) Languagecited by 181
- 2017Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbolscited by 123
- 2021Are Convolutional Neural Networks or Transformers more like human vision?cited by 109
- 2018Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Inputcited by 102
14 more
- 2017Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialogcited by 86
- 2020Compositional Languages Emerge in a Neural Iterated Learning Modelcited by 75
- 2016Reasoning About Pragmatics with Neural Listeners and Speakerscited by 68
- 2019Ease-of-Teaching and Language Structure from Emergent Communicationcited by 65
- 2018Emergent Communication through Negotiationcited by 62
- 2020Countering Language Drift with Seeded Iterated Learningcited by 57
- 2017Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Studycited by 46
- 2018How agents see things: On visual representations in an emergent language gamecited by 45
- 2019Emergent Linguistic Phenomena in Multi-Agent Communication Gamescited by 36
- 2018Assessing Shape Bias Property of Convolutional Neural Networkscited by 18
- 2020Co-evolution of language and agents in referential gamescited by 18
- 2019Emergence of Compositional Language with Deep Generational Transmissioncited by 17
- 2018Color naming reflects both perceptual structure and communicative needcited by 13
- 2019Miss Tools and Mr Fruit: Emergent communication in agents learning about object affordancescited by 11
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Citing papers per year: 2022: 5; 2023: 0; 2024: 5; 2025: 1; 2026: 2.
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- 2022Transformers in Medical Imaging: A Surveycited by 245
- 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classificationcited by 65
- 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
- 2024Emergent Language: A Survey and Taxonomycited by 12
- 2024A systematic framework for generating novel experimental hypotheses from language modelscited by 8
- 2025Adopting a human developmental visual diet yields robust, shape-based AI visioncited by 5
- 2024ELCC: the Emergent Language Corpus Collectioncited by 2
- 2022Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communicationcited by 1
- 2022A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relationscited by 1
- 2022Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methodscited by 0
- 2024Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scalecited by 0
- 2026Would you still call this Dax? Novel Visual References in VLMs and Humanscited by 0
- 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations5 Sep 2026
- 2026Would you still call this Dax? Novel Visual References in VLMs and Humans3 Jun 2026
- 2025Adopting a human developmental visual diet yields robust, shape-based AI vision3 Jul 2025
- 2024Emergent Language: A Survey and Taxonomy4 Sep 2024
- 2024A systematic framework for generating novel experimental hypotheses from language models9 Aug 2024
- 2024ELCC: the Emergent Language Corpus Collection4 Jul 2024
- 2024A Review of the Applications of Deep Learning-Based Emergent Communication3 Jul 2024
- 2024Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scale16 Jan 2024
- 2022A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations12 Dec 2022
- 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification23 Oct 2022
- 2022Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication3 Oct 2022
- 2022Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods2 Jun 2022
1 more
- 2022Transformers in Medical Imaging: A Survey24 Jan 2022
- 2022Transformers in Medical Imaging: A Surveycited by 245
- 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classificationcited by 65
- 2024A Review of the Applications of Deep Learning-Based Emergent Communicationcited by 17
- 2024Emergent Language: A Survey and Taxonomycited by 12
- 2024A systematic framework for generating novel experimental hypotheses from language modelscited by 8
- 2025Adopting a human developmental visual diet yields robust, shape-based AI visioncited by 5
- 2024ELCC: the Emergent Language Corpus Collectioncited by 2
- 2022Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communicationcited by 1
- 2022A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relationscited by 1
- 2022Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methodscited by 0
- 2024Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scalecited by 0
- 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulationscited by 0
- 2026Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations5 Sep 2026
- 2025Adopting a human developmental visual diet yields robust, shape-based AI vision3 Jul 2025
- 2024Emergent Language: A Survey and Taxonomy4 Sep 2024
- 2024A systematic framework for generating novel experimental hypotheses from language models9 Aug 2024
- 2024ELCC: the Emergent Language Corpus Collection4 Jul 2024
- 2024A Review of the Applications of Deep Learning-Based Emergent Communication3 Jul 2024
- 2024Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scale16 Jan 2024
- 2022A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations12 Dec 2022
- 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification23 Oct 2022
- 2022Contrastive Multimodal Learning for Emergence of Graphical Sensory-Motor Communication3 Oct 2022
- 2022Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods2 Jun 2022
- 2022Transformers in Medical Imaging: A Survey24 Jan 2022