[24] Angeliki Lazaridou, Alexander Peysakhovich, and Marco Baroni. Multi-agent cooperation and the emergence of (natural) language. In International Conference on Learning Representations, 2017.
[25] Angeliki Lazaridou, Anna Potapenko, and Olivier Tieleman. Multi-agent communication meets natural language: Synergies between functional and structural language learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7663-7674, Online, July 2020. Association for Computational Linguistics.
[26] Vladimir Iosifovich Levenshtein. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8):707-710, 1966. Doklady Akademii Nauk SSSR, V163 No4 845-848 1965.
[27] David Lewis. Convention: A philosophical study. John Wiley & Sons, 2008.
[28] Fushan Li and Michael Bowling. Ease-of-teaching and language structure from emergent communication. Advances in neural information processing systems, 32, 2019.
[29] Zechen Li and Anders Søgaard. QLEVR: A diagnostic dataset for quantificational language and elementary visual reasoning. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 980–996, Seattle, United States, July 2022. Association for Computational Linguistics.
[30] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019.
[31] Jesse Mu and Noah Goodman. Emergent communication of generalizations. In A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, 2021.
[32] Weili Nie, Zhiding Yu, Lei Mao, Ankit B Patel, Yuke Zhu, and Animashree Anandkumar. Bongard-logo: A new benchmark for human-level concept learning and reasoning. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
[33] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
[34] J. C. Raven and Australian Council for Educational Research. *Raven's progressive matrices* (1938): sets  $A$ ,  $B$ ,  $C$ ,  $D$ ,  $E$ . Australian Council for Educational Research Melbourne, 1938.
[35] Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, and Simon Kirby. Compositional languages emerge in a neural iterated learning model. In International Conference on Learning Representations, 2020.
[36] Mathieu Rita, Florian Strub, Jean-Bastien Grill, Olivier Pietquin, and Emmanuel Dupoux. On the role of population heterogeneity in emergent communication. In International Conference on Learning Representations, 2022.
[37] Mathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill, Olivier Pietquin, Emmanuel Dupoux, and Florian Strub. Emergent communication: Generalization and overfitting in lewis games. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
[38] David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. Analysing mathematical reasoning abilities of neural models. In International Conference on Learning Representations, 2019.
[39] R E Snow, P C Kyllonen, and B Marshalek. The topography of learning and ability correlations. Advances in the psychology of human intelligence, 2:47-103, 1984.