Publications

Preprints

Biscione, V., Yin, D., Malhotra, G., Dujmovic, M., Montero, M.L., Puebla, G., Adolfi, F., Heaton, R.F., Hummel, J.E., Evans, B.D. and Habashy, K. (2024). MindSet: Vision. A toolbox for testing DNNs on key psychological experiments. arXiv

Puebla G. & Bowers, J. S. (2024). Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition Approach. arXiv

Puebla G. & Doumas, L. A. A. (2022). Learning Relational Rules from Rewards. arXiv

Journal articles

Fong F. T. K., Puebla, G.& Nielsen, M. (2024). The Role of Conventionality and Design in Children’s Function Judgments About Malfunctioning Artifacts. Journal of Experimental Child Psychology, 240, 105835. journal (open access)

Bowers J. S., Malhotra, G., Adolfi, F. G., Dujmović, M., Montero, M. L., Biscione, V., Puebla, G., Hummel, J. & Heaton, R. F. (2023). On the importance of severely testing deep learning models of cognition. Cognitive Systems Research, 82, 101158. journal

Bowers J. S., Malhotra, G., Dujmović, M., Montero, M. L., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J. E., Heaton, R., Evans, B. D., Mitchell, J. & Blything, R. (2023). Clarifying status of DNNs as models of human vision. Behavioral and Brain Sciences, 46, e415. journal

Bowers J. S., Malhotra, G., Dujmović, M., Montero, M. L., Tsvetkov, C., Biscione, V., Puebla, G., Adolfi, F., Hummel, J. E., Heaton, R., Evans, B. D., Mitchell, J. & Blything, R. (2023). Deep Problems with Neural Network Models of Human Vision. Behavioral and Brain Sciences, 46, e385. journal

Puebla G. & Bowers, J. S. (2022). Can deep convolutional neural networks support relational reasoning in the same-different task? Journal of Vision, 22(11), 1-18. journal (open access)

Doumas L. A. A., Puebla, G., Martin, A. E., & Hummel, J. E. (2022). A theory of relation learning and cross-domain generalization. Psychological Review, 129(5), 999–1041. pdf

Puebla G., Martin, A. E. & Doumas, L. A. A. (2021). The relational processing limits of classic and contemporary neural network models of language processing. Language, Cognition and Neuroscience, 36(2), 240-254. journal

Chaigneau S. E., Puebla, G. & Canessa, E. C. (2016). Why the designer’s intended function is central for proper function assignment and artifact conceptualization: Essentialist and normative accounts. Developmental Review, 41, 38-50. journal

Puebla G. & Chaigneau, S. E. (2014). Inference and coherence in causal-based artifact categorization. Cognition, 130(1), 50-65. journal

Chaigneau S. E. & Puebla, G. (2013). The Proper Function of Artifacts: Intentions, Conventions and Causal Inferences. Review of Philosophy and Psychology, 4(3), 391-406. journal

Conference proceedings

Biscione V., Yin, D., Malhotra, G., Dujmović, M., Montero, M. L., Puebla, G., Adolfi, F. G., Tsvetkov, C., Heaton, R. F., Hummel, J., Evans, B. D. & Bowers, J. S. (2023). Introducing the MindSet benchmark for comparing DNNs to human vision. 2023 Conference on Cognitive Computational Neuroscience. pdf

Puebla G. & Bowers, J. S. (2023). The role of object-centric representations, guided attention, and external memory on generalizing visual relations. 2023 Conference on Cognitive Computational Neuroscience. pdf

Puebla G. & Bowers, J. S. (2021). Can Deep Convolutional Neural Networks Learn Same-Different Relations? Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 1747-1752. pdf

Doumas L. A., Puebla, G., Martin, A. E. & Hummel, J. E. (2020). Relation learning in a neurocomputational architecture supports cross-domain transfer. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society, 932-937. pdf

Doumas L. A. A., Puebla, G., Hummel, J. E. & Martin, A. E. (2019). Predicate learning via neural oscillations supports one-shot generalization between video games. 2019 Conference on Cognitive Computational Neuroscience. pdf

Puebla G. & Chaigneau, S. E. (2019). A Piecemeal Processing Strategy Model for Causal-Based Categorization. Proceedings of the 41st Annual Virtual Meeting of the Cognitive Science Society, 2613-2619. pdf

Puebla G., Doumas, L. A. & Martin, A. E. (2019). The relational processing limits of classic and contemporary neural network models of language processing. 2019 Conference on Cognitive Computational Neuroscience. pdf

Doumas L. A., Hamer, A., Puebla, G. & Martin, A. E. (2017). A theory of the detection and learning of structured representations of similarity and relative magnitude. Proceedings of the 39th annual conference of the cognitive science society, 1955-1960. pdf

Puebla-Ramírez G. & Chaigneau, S. (2011). Is the Centrality of Design History Function an Effect of Causal Knowledge? Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, 1533-1538. pdf