Overview | Research Directions | Members | Publications | Ongoing collaborations | Web

Overview

We have a broad interest in higher-level brain functions, their relation to lower-level computations, and the role of context.

Research Directions

  • Study of relation between knowledge represented in neural networks and knowledge in humans;
  • uncertainty and predictive processes: what are the principles that organize the brain’s responses to uncertainty, and how these support predictive processes;
  • neurobiology of language: organization and interactions between brain systems that support language comprehension.

Members and Lab Alumni

  • Uri Hasson, Principal Investigator
  • Daniil Kirillov (CIMeC Master's thesis student)
  • Greta Greco (CIMeC Master's internship)
  • Ekaterina Komarova (CIMeC Master’s inernship)
  • Roberto Savi (DISI Master's internship)
  • Valerio Siniscalco (undergraduate intern, Italian Erasmsus)
  • Alessia Bertotti (DISI undergraduate internship)
  • Icaro Re Depaolini (Research Assistant)

Recent Lab alumni:

  • 2025. Stella Fritz (Master's thesis under CIMeC program)
  • 2025. Gerrit Sanders (International Erasmus undergraduate internship)
  • 2024. Dario Pesenti (CIMeC Master's internship, now PhD student CIMeC)
  • 2024. Pooya Varnoosfaderaniv (DISI Master's internship)
  • 2024. Giulia Lund (DIPSCO Master's internship, now PhD student SISSA)
  • 2023. Natalia Flechas Manrique (CIMeC Master's internship)
  • 2023. Anna Bavaresco (Master's thesis under Data Science program, now PhD student Uni. Amsterdam)
  • 2022. Priya Tarigopula (Master's thesis under CIMeC program, now PhD student Uni. Oslo)

 Publications

For a complete list see Uri Hasson personal page

Master's students (in bold) are first authors and co-authors on these two recent publications:

  • Bavaresco, A., Truong, N., & Hasson, U. (2025). Modeling human concepts with subspaces in deep vision models. ACM Transactions on Interactive Intelligent Systems.
  • Flechas, M. N., & Bao, W., Herbelot, A., & Hasson, U. (2023). Enhancing Interpretability using human similarity judgements to prune word embeddings. Full paper presented at BlackboxNLP (EMNLP2023).
  • Tarigopula, P., Fairhall, S., & Bavaresco, A., & Truong, N., & Hasson, U. (2023). Improved prediction of behavioral and neural similarity spaces using pruned DNNs. Neural Networks, 168.
  • Bao, W., & Hasson, U. (2024). Identifying and interpreting non-aligned human conceptual representations using language modeling. Full paper presented at ICLR 2024 Workshop on Representational Alignment.
  • Truong, N., Pesenti, D., & Hasson, U. (2024). Explaining human comparisons using alignment-importance heatmaps. Full paper presented at ICLR 2024 Workshop on Representational Alignment.

Two Master's students contributed to this granted Patent application:

  • Metodo e mezzi per la produzione di sequenze a livelli richiesti di interessantezza / Hasson, Uri; Notaro, Giuseppe; Eperon, Alexander Charles Leslie; Sartorato, Nicola; Minati, Ludovico. - (2024).

Ongoing collaborations

Dr. Howard Nusbaum; the University of Chicago

Web

Research group webpage