Hello!
I'm a postdoc at Harvard.

I work with Sam Gershman in the Computational Cognitive Neuroscience Lab. I graduated from École Polytechnique (Paris) and completed my PhD at École Normale Supérieure (Paris) with Rava da Silveira. After that I was a postdoc with Mike Woodford in the Cognition and Decision Lab at Columbia University.

I'm interested in the cognitive and neural mechanisms underlying perception, representation, inference and decision-making. My goal is to develop a theoretical framework that is biologically plausible, grounded in sound computational principles, and backed by experiments; and that makes behavior understandable and predictable. My research is thus a combination of theory, experiments, and computational modeling, and it sits at the intersection of multiple scientific disciplines, from neuroscience to economics.

Here's a recent CV.

Publications & Manuscripts

2024 Prat-Carrabin, A., Woodford, M. Imprecise counting of observations in averaging tasks predicts primacy and recency effects. bioRxiv.
2024 Prat-Carrabin, A., Gershman, S. Bayesian estimation yields anti-Weber variability. bioRxiv.
2024 Prat-Carrabin, A., Woodford, M. Endogenous Precision of the Number Sense. eLife (Reviewed Preprint). (PDF on bioRxiv )
2024 Prat-Carrabin, A., Woodford, M. Imprecise Probabilistic Inference from Sequential Data. Psychological Review.
2024 Prat-Carrabin, A., Meyniel, F., Azeredo da Silveira, R. Resource-rational account of sequential effects in human prediction. eLife.
2022 Prat-Carrabin, A., Woodford, M. Efficient coding of numbers explains decisions bias and noise. Nature Human Behaviour.
2021 Prat-Carrabin, A., Woodford, M. Bias and variance of the Bayesian-mean decoder. In M. Ranzato et al., eds., Advances in Neural Information Processing Systems 34 (NeurIPS 2021).
2021 Prat-Carrabin, A., Wilson, R., Cohen, J.D., Azeredo da Silveira, R. Human Inference in Changing Environments with Temporal Structure. Psychological Review.
2021 Prat-Carrabin, A., Meyniel, F., Tsodyks, M., Azeredo da Silveira, R. Biases and Variability from Costly Bayesian Inference. Entropy.
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