A feature-rich framework
Network models of the mental lexicon that combine word properties, multiple relation types and time.
← Research
Research line
Cognitive NetSci
Feature-rich network models of the mental lexicon, language acquisition and emotional profiling of text.
Cognitive network science studies human cognition by modelling the mental lexicon, the store of words and concepts behind thought and communication, as a network.
Traditional psycholinguistics focuses on the properties of individual words, such as valence, arousal or concreteness. Cognitive network science looks at the structure that emerges from their connections. Feature-rich network models bring the two views together, relating word properties and connectivity to language functions and cognitive processes.
Three goals
Data-driven cognition
Tools and algorithms applied to psycholinguistic experiments, from free associations to early word learning.
Emotional profiling
Methods to extract emotional profiles from user-generated content, and to compare human and LLM associations.
The mental lexicon as a network
How the elements of a feature-rich network model map onto human cognition.
- System
- Human cognition, mental lexicon, semantic, phonological and syntactic memory.
- Nodes
- Concepts and words, or their sub-units such as phonemes and morphemes.
- Relations
- Free associations, synonyms and antonyms, hypernymy, shared features, phonological similarity, syntactic dependencies.
- Graph
- Words are linked when they share a linguistic similarity, are recalled together, or co-occur in sentences.
- Higher-order
- Co-occurrences and multi-response association tasks naturally form hyperedges, revealing structures such as knowledge gaps.
- Attributes
- Length, frequency, polysemy, valence, arousal, dominance and concreteness, related to connectivity through assortative mixing.
- Multilayer
- Associations, synonyms and phonological similarities as layers, analysed together to reveal patterns in word acquisition.
- Dynamic
- The lexicon grows as words are learned; acquisition can be modelled as a process, such as a random walk, on an evolving network.
Objectives
- 1
Develop a feature-rich network framework for the study of human cognition, the mental lexicon and language acquisition.
- 2
Pursue data-driven cognitive network science, applying these tools to data from psycholinguistic experiments.
- 3
Define sound methodologies to extract emotional profiles from user-generated content.
Related publications
All publications-
The role of System 1 and System 2 semantic memory structure in human and LLM biases
arXiv (Cornell University)@article{abramski2026role, title = {The role of System 1 and System 2 semantic memory structure in human and LLM biases}, author = {Abramski, Katherine and Rossetti, Giulio and Stella, Massimo}, journal = {arXiv (Cornell University)}, year = {2026}, doi = {10.48550/arxiv.2604.12816}, url = {https://doi.org/10.48550/arxiv.2604.12816}, publisher = {Cornell University}, } -
SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks
Computers in Human Behavior Reports, 21@article{citraro2026spreadpy, title = {SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks}, author = {Citraro, Salvatore and Haim, Edith and Carini, Alessandra and Siew, Cynthia S. Q. and Rossetti, Giulio and Stella, Massimo.}, journal = {Computers in Human Behavior Reports}, year = {2026}, volume = {21}, pages = {100940--100940}, doi = {10.1016/j.chbr.2026.100940}, url = {https://doi.org/10.1016/j.chbr.2026.100940}, publisher = {Elsevier BV}, } -
LLMs and Cognition
@incollection{rossetti2026llms, title = {LLMs and Cognition}, author = {Rossetti, Giulio and Abramski, Katherine and Cau, Erica and Pansanella, Valentina and Stella, Massimo}, year = {2026}, pages = {1--39}, doi = {10.1007/978-3-031-61050-9_22-1}, url = {https://doi.org/10.1007/978-3-031-61050-9_22-1}, } -
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and AI-simulated raters
Journal of Computational Social Science, 9(1)@article{haim2026forma, title = {Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and AI-simulated raters}, author = {Haim, Edith and Fischer, Natalie and Citraro, Salvatore and Rossetti, Giulio and Stella, Massimo}, journal = {Journal of Computational Social Science}, year = {2026}, volume = {9}, number = {1}, doi = {10.1007/s42001-025-00446-z}, url = {https://doi.org/10.1007/s42001-025-00446-z}, publisher = {Springer Nature}, } -
Beyond Creativity Scores: Exploring PRE–POST Differences in Teachers’ Semantic Networks After SCIP Training
Big Data and Cognitive Computing@article{haim2026creativity, title = {Beyond Creativity Scores: Exploring PRE–POST Differences in Teachers' Semantic Networks After SCIP Training}, author = {Haim, Edith and Haim, K. and Aschauer, W. and Rossetti, Giulio and Stella, Massimo}, journal = {Big Data and Cognitive Computing}, year = {2026}, } -
The “LLM World of Words” English free association norms generated by large language models
Scientific Data, 12(1)@article{abramski2025world, title = {The “LLM World of Words” English free association norms generated by large language models}, author = {Abramski, Katherine and Improta, Riccardo and Rossetti, Giulio and Stella, Massimo}, journal = {Scientific Data}, year = {2025}, volume = {12}, number = {1}, pages = {803--803}, doi = {10.1038/s41597-025-05156-9}, url = {https://doi.org/10.1038/s41597-025-05156-9}, publisher = {Nature Portfolio}, } -
A word association network methodology for evaluating implicit biases in LLMs compared to humans
arXiv preprint arXiv:2510.24488@article{abramski2025word, title = {A word association network methodology for evaluating implicit biases in LLMs compared to humans}, author = {Abramski, Katherine and Rossetti, Giulio and Stella, Massimo}, journal = {arXiv preprint arXiv:2510.24488}, year = {2025}, } -
Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements and future challenges
Psychonomic Bulletin & Review -
LLM generated word association norms
In Hybrid Human Artificial Intelligence@inproceedings{abramski2024llm, title = {LLM generated word association norms}, booktitle = {Hybrid Human Artificial Intelligence}, author = {Abramski, Katherine and Lavorati, Clara and Rossetti, Giulio and Stella, Massimo}, year = {2024}, } -
Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives
Computers in Human Behavior@article{abramski2024voices, title = {Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives}, author = {Abramski, Katherine and Ciringione, Luciana and Rossetti, Giulio and Stella, Massimo}, journal = {Computers in Human Behavior}, year = {2024}, } -
Cognitive Network Science Unveils Affective Bias in GPT Models Mirroring Math Anxiety in High-School Students
In Frontiers in artificial intelligence and applications@incollection{abramski2024cognitive, title = {Cognitive Network Science Unveils Affective Bias in GPT Models Mirroring Math Anxiety in High-School Students}, author = {Abramski, Katherine and Citraro, Salvatore and Lombardi, Luigi and Rossetti, Giulio and Stella, Massimo}, booktitle = {Frontiers in artificial intelligence and applications}, year = {2024}, doi = {10.3233/faia240221}, url = {https://doi.org/10.3233/faia240221}, } -
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Scientific Reports@article{citraro2022feature, title = {Feature-rich multiplex lexical networks reveal mental strategies of early language learning}, author = {Citraro, Salvatore and Vitevitch, Michael and Stella, Massimo and Rossetti, Giulio}, journal = {Scientific Reports}, year = {2023}, } -
The voices of rape: Cognitive networks reconstruct emotional perspectives of rape survivors’ passive and active narratives on Reddit
In International School and Conference on Network Science@inproceedings{abramski2023the, title = {The voices of rape: Cognitive networks reconstruct emotional perspectives of rape survivors' passive and active narratives on Reddit}, author = {Abramski, Katherine and Citraro, Salvatore and Rossetti, Giulio and Stella, Massimo}, booktitle = {International School and Conference on Network Science}, year = {2023}, } -
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students
Big Data and Cognitive Computing@article{abramski2023cognitive, title = {Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students}, url = {https://www.mdpi.com/2504-2289/7/3/124}, doi = {10.3390/bdcc7030124}, journal = {Big Data and Cognitive Computing}, author = {Abramski, Katherine and Citraro, Salvatore and Lombardi, Luigi and Rossetti, Giulio and Stella, Massimo}, year = {2023}, } -
Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning
New Ideas in Psychology, 71@article{Citraro2023bHypergraph, title = {Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning}, journal = {New Ideas in Psychology}, volume = {71}, pages = {101034}, year = {2023}, issn = {0732-118X}, doi = {https://doi.org/10.1016/j.newideapsych.2023.101034}, url = {https://www.sciencedirect.com/science/article/pii/S0732118X23000272}, author = {Citraro, Salvatore and Warner-Willich, Judy and Battiston, Federico and S.Q. Siew, Cynthia and Rossetti, Giulio and Stella, Massimo}, } -
Towards hypergraph cognitive networks as feature-rich models of knowledge
EPJ Data Science@article{citraro2023towards, title = {Towards hypergraph cognitive networks as feature-rich models of knowledge}, author = {Citraro, Salvatore and De Deyne, Simon and Stella, Massimo and Rossetti, Giulio}, year = {2023}, journal = {EPJ Data Science}, } -
Cognitive network neighbourhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Physica A: Statistical Mechanics and its Applications@article{marina2022cognitive, title = {Cognitive network neighbourhoods quantify feelings expressed in suicide notes and Reddit mental health communities}, author = {Marina Joseph, Simmi and Citraro, Salvatore and Morini, Virginia and Rossetti, Giulio and Stella, Massimo}, journal = {Physica A: Statistical Mechanics and its Applications}, year = {2022}, } -
Cognitive modelling with multilayer networks: Insights, advancements and future challenges
arXiv preprint arXiv:2210.00500@article{stella2022cognitive, title = {Cognitive modelling with multilayer networks: Insights, advancements and future challenges}, author = {Stella, Massimo and Citraro, Salvatore and Rossetti, Giulio and Marinazzo, Daniele and Kenett, Yoed and Vitevitch, Michael}, journal = {arXiv preprint arXiv:2210.00500}, year = {2022}, } -
Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities
https://arxiv.org/abs/2110.15269@article{joseph2021cognitive, title = {Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities}, author = {Joseph, Simmi Marina and Citraro, Salvatore and Morini, Virginia and Rossetti, Giulio and Stella, Massimo}, journal = {https://arxiv.org/abs/2110.15269}, year = {2021}, } -
A complex network approach to semantic spaces: How meaning organizes itself
SEBD, 7@article{citraro2019complex, title = {A complex network approach to semantic spaces: How meaning organizes itself}, author = {Citraro, Salvatore and Rossetti, Giulio}, journal = {SEBD}, volume = {7}, pages = {8}, year = {2019}, }