I am a postdoctoral researcher (postdoc) in the Cognition and Plasticity research group led by Gesa Hartwigsen at the Max Planck Institute for Human Cognitive and Brain Sciences.

I received a Ph.D. in Computer Science from the School of Electronic Engineering and Computer Science at Queen Mary University of London, under the supervision of Massimo Poesio in the CogSci group.

Very broadly, I am interested in what goes on in the brain when we think about things in the world, and the way in which we remember them (good ol’ semantics).

I investigate brain - electroencephalography (EEG), magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS) - and behavioural data using cutting-edge methods from machine learning, Artificial Intelligence (AI), and in particular Natural Language Processing (NLP) - (large) language models, language statistics and distributions, corpora.

My Ph.D. project revolved around the semantics of individual entities and proper names (entities referred to by proper names, such as ’Yasujiro Ozu’ or `Cafe Oto’). They are particularly interesting when compared with generic stuff such as trees, windows, birds - think about how easy and embarrassing it is to forget somebody’s name! In jargon, their meaning is extremely fine- grained and social in nature. Because of these challenges, I looked at how these are represented in brains and machines, and I try to bring together both cognitive and computational approaches.

Bio

During my Ph.D., I have also been a visiting Ph.D. student at the Language, Learning and Reading lab at SISSA in Trieste, and at the COLT group at Universitat Pompeu Fabra in Barcelona.

Also, while on a break from my Ph.D., I worked as a Junior Research Scientist at SAP Security Research in Mougins, France, on applying NLP techniques for de-identification and data anonymization.

I hold a BA in Philosophy from the University of Torino and a M.Sc. in Cognitive Science from the University of Trento.

On the side, I also co-manage/am part of SØVN Records, a small DIY label of experimental and avant-garde music - if you feel adventurous, check out a compilation we recently released!

Publications

2024

• Bruera, A., Poesio, M. (2024). EEG Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity. Journal of Cognitive Neuroscience 2024.

• Bruera, A., Zamani, F., Poesio, M. (2024). Polysemy through the lens of psycholinguistic variables: a dataset and an evaluation of static and contextualized language models. In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), Association for Computational Linguistics.

2023

• Bruera, A., Tao, Y., Anderson, A., Cokal, D., Haber, J., Poesio, M. (2023). Modelling brain representations of words’ concreteness in context using GPT-2 and human ratings, Cognitive Science, DOI: 10.1111/cogs.13388

2022

• Bruera, A. & Poesio, M. (2022). Exploring the representations of individual entities in the brain combining EEG and distributional semantics, Frontiers in Artificial Intelligence, 25, 2022

• Bruera, A., Aldà, F., Di Cerbo, F. (2022). Generating Realistic Synthetic Curricula Vitae for Machine Learning Applications under Differential Privacy, Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference, June 2022, Marseille

Preprints

2023

• Bruera, A., & Poesio, M. (2023). Family Lexicon: using language models to encode memories of personally familiar and famous people and places in the brain. bioRxiv, 2023-08.

2021

• Bruera, A. & Herbelot, A., (2021). Novel Aficionados and Doppelgängers: a referential task for semantic representations of individual entities, arXiv preprint arXiv:2104.10270

Datasets

Datasets for most of the papers are available on my OSF profile

Patent Application

2023

• Francesco, A. L. D. A., Bruera, A., & Di Cerbo, F. (2023). Generating user-based training data for machine learning U.S. Patent Application No. 17/716,368