Journals

Chaigneau, S. E., Marchant, N., Canessa, E., & Aldunate, N. (in press). A Mathematical model of semantic access in Lexical and Semantic decisions. Language and Cognition.

Toro-Hernández, F., Migeot, J., Marchant, N., Olivares, D., Ferrante, F., González-Gómez, R., González Campo, C., Fittipaldi, S., Rojas-Costa, G., Moguilner, S., Slachevsky, A., Chaná Cuevas, P., Ibáñez, A., Chaigneau, S., & García, A. (2024). Neurocognitive correlates of semantic memory navigation in Parkinson’s disease. npj Parkinson’s Disease, 10, 15. DOI: 10.1038/s41531-024-00630-4

Ramos, D., Moreno, S., Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). AC-PLT: An algorithm for computer-assisted coding of semantic property listing data. Behavior Research Methods. DOI: 10.3758/s13428-023-02260-9

Marchant, N., Quillien, T., & Chaigneau, S. E. (2023). A context-dependent Bayesian account for causal-based categorization. Cognitive Science, 47(1). DOI: 10.1111/cogs.13240

Marchant, N., & Chaigneau, S. E. (2022). On the importance of feedback for categorization: Revisiting category learning experiments using an Adaptive Filter model. Journal of Experimental Psychology: Animal Learning & Cognition, 48(4), 295-306. DOI: 10.1037/xan0000339

Marchant, N., & Chaigneau, S. E. (2022). Computational Cognitive models of Categorization: Predictions under conditions of classification uncertainty. Psykhe. DOI: 10.7764/psykhe.2021.37971

Marchant, N., Canessa, E., & Chaigneau, S. E. (2022). An Adaptive Linear Filter model of procedural category learning. Cognitive Processing. DOI: 10.1007/s10339-022-01094-1

CogSci Proceedings

Marchant, N., Quillien, T., & Chaigneau, S. E. (2023). Uncertainty can explain apparent mistakes in causal reasoning. Proceedings of the 45th Annual Meeting of the Cognitive Science Society. URL: escholarship.org/uc/item/6j55z9dd

Marchant, N., Zhao, B., Bramley, N. R., Morales, D., & Chaigneau, S. E. (2022). Categorizing perceived causal events. Proceedings of the 44th Annual Meeting of the Cognitive Science Society. URL: escholarship.org/uc/item/0gj4r0k2

Marchant, N., & Chaigneau, S. E. (2021). Designing probabilistic category learning experiments: The probabilistic prototype distortion task. Proceedings of the 43rd Annual Conference of the Cognitive Science Society. URL: escholarship.org/uc/item/0cs145c6

Marchant, N., & Chaigneau, S. E. (2020). Modulating coherence effect in causal-based processing. Proceedings of the 42nd Annual Conference of the Cognitive Science Society.

Book chapters

Marchant, N., Canessa, E., & Chaigneau, S. E. (2023).Challenges from Probabilistic Learning for Models of Brain and Behavior. In: Veloz, T., Khrennikov, A., Toni, B., Castillo, R.D. (eds) Trends and Challenges in Cognitive Modeling. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. DOI: 10.1007/978-3-031-41862-46

Canessa, E., Chaigneau, S. E., & Marchant, N. (2023). Use of Agent-Based Modeling (ABM) in Psychological Research. In: Veloz, T., Khrennikov, A., Toni, B., Castillo, R.D. (eds) Trends and Challenges in Cognitive Modeling. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. DOI: 10.1007/978-3-031-41862-42