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CogNosco Lab

Hosted in the Department of Computer Science of the University of Exeter, CogNosco Lab focuses on next-generation models for understanding cognition through data science, AI and complex networks. Our multilayer cognitive networks unveil predictive and quantitative patterns in fields like language learning, mindset reconstruction, clinical impairments and knowledge modelling in online systems.

The group is led by Dr. Massimo Stella, faculty at CEMPS, University of Exeter, UK.

Our lab develops cognitive data science frameworks for interpreting and understanding cognition.

Our ideas, models and data insights are powered by:

Mutlilayer Cognitive Networks

Cognitive networks are distributed models of conceptual knowledge representing how concepts, ideas and words are linked with each other. The science of multilayer networks makes it possible to build large-scale models of human knowledge structured across multiple types of conceptual associations from semantics, phonology, memory, syntactics, visual cues, affect and many other cognitive relationships.

Our research aims to determine how this multilayer network structure influences language acquisition, use and decline.

Areas of application:

Predicting early language learning

Understanding language pathologies

Creativity and multilayer networks


Natural Language Processing

Natural language processing (NLP) is a quickly growing field aimed at identifying key patterns expressed through language like stances, attitudes and beliefs.

In our research, we adopt and develop tools for performing NLP to access the structure of semantic and syntactic relationships giving shape to a given stance, i.e. a portrayal of an entity in terms of specific conceptual associations.  Relying on semantic frame theory and the psychology of emotions, this approach powers interpretable models of text analysis.

Areas of application:

Stance detection in social media

Semantic frames and the gender gap

Key perceptions of STEM subjects

Emotional Profiling and AI

Emotional profiling extends the understanding of sentiment across multiple dimensions. Whereas sentiment maps the pleasantness of language, a dimension known as “valence” in psycholinguistics, emotional profiling can detect states as complex and nuanced as shades of concern, anger, disgust or joy.

We combine machine learning and emotional datasets in order to understand how different emotions populate specific semantic frames. In our data-informed tools, we are supported by complexity science and psychology.

Areas of application:

Emotional profiling in suicide notes

Data visualisation and emotions

Emotional reactions to COVID-19


Our Team at CogNosco Lab:

Dr. Massimo Stella

Dr. Massimo Stella

Group Lead

My research focuses on cognitive networks, language processing and knowledge modelling.

Simmi Marina Joseph

Simmi Marina Joseph

Research Assistant

My research interest is in Natural Language Processing, specifically in areas such as knowledge engineering and digital humanities.

Salvatore Citraro

Salvatore Citraro

Visiting PhD Student (Co-Advised)

I am visiting CogNosco Lab from the KDD Lab at ISTI-CNR and University of Pisa (Main Supervisor: Dr. Giulio Rossetti). My research interest is in computational linguistics and network science.

Mingting Hong

Mingting Hong

MSc Student in Computer Science

My research interests are in Natural Language Processing and Emotional Profiling.
Owen Saunders

Owen Saunders

MSc Student in Computer Science

My research interests are in Natural Language Processing and Cognitive Science, particularly the imitation and prediction of an agent, for simulating intelligence.

Active Research Collaborations with:

Active Research Collaborations with:

Active Research Collaborations with:

Active Research Collaborations with:

External Collaborating PhD students:

Alfonso Semararo and Salvatore Vilella – PhD Candidates in Computer Science, ARCS Group, University of Turin (Supervisor: Giancarlo Ruffo).


Past Lab Members:

Asra Fatima – Now Data Scientist at CitiBank

Martyna Wozniak – Now Data Scientist in Warsaw



Undergraduate students:

Kieran Brian

Personal project: Textual forma mentis networks in climate change discourse.

Oliver Baker

Personal project: Predicting aphasia data with multiplex lexical networks.

Jude Warner-Willich

Personal project: Hypergraphs for modelling semantic memory.

Harry Wang

Personal project: Investigating the structure of clinical narratives with NLP.

Ellie Strange

Personal project: Machine learning and network data for predicting age of acquisition.

James Butler

Personal project: Developing a web interface for visualising cognitive networks and delivering interactive stance detection.

Our research portfolio at CogNosco Lab:


Fatima, A., Li, Y., Hills, T. T., & Stella, M. (2021). DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning. Big Data and Cognitive Computing, 5(4), 77.

Teixeira, A. S., Talaga, S., Swanson, T. J., & Stella, M. (2021). Revealing semantic and emotional structure of suicide notes with cognitive network science. Scientific Reports, 11(1), 1-15.

Stella, M. (2020). Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media. PeerJ Computer Science, 6, e295.

Stella, M., & Zaytseva, A. (2020). Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth. PeerJ Computer Science, 6, e255.

Stella, M. (2020). Social discourse and reopening after COVID-19. First Monday, 25(11).

Stella, M., Restocchi, V., & De Deyne, S. (2020). # lockdown: Network-enhanced emotional profiling in the time of Covid-19. Big Data and Cognitive Computing, 4(2), 14.

Stella, M., De Nigris, S., Aloric, A., & Siew, C. S. (2019). Forma mentis networks quantify crucial differences in STEM perception between students and experts. PloS one, 14(10), e0222870.


Stella, M., Beckage, N. M., & Brede, M. (2017). Multiplex lexical networks reveal patterns in early word acquisition in children. Scientific reports, 7(1), 1-10.
Stella, M., Ferrara, E., & De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49), 12435-12440.
Stella, M., Beckage, N. M., Brede, M., & De Domenico, M. (2018). Multiplex model of mental lexicon reveals explosive learning in humans. Scientific reports, 8(1), 1-11.
Stella, M., & Kenett, Y. N. (2019). Viability in multiplex lexical networks and machine learning characterizes human creativity. Big Data and Cognitive Computing, 3(3), 45.


Citraro, S., Vitevitch, M., Stella, M. & Rossetti, G. (2022). Feature-rich multiplex lexical networks reveal mental strategies of early word learning. arXiv preprint arXiv:2201.05061.
Joseph, S. M., Citraro, S., Morini, V., Rossetti, G. & Stella, M. (2021). Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities. arXiv preprint arXiv:2110.15269.
Stella, M., Kapuza, A., Cramer, C., & Uzzo, S. (2020). Mapping computational thinking mindsets between educational levels with cognitive network science. arXiv preprint arXiv:2007.09402.


Join us at CogNosco Lab, University of Exeter, UK

CogNosco is Latin for “I know”, a name that fits our scope on cognitive data science and knowledge modelling.

Why the capital letter in Nosco? Because it also means “with us”, a reminder that we are keen on growing and exploring cognitive data science together with prospective MSc students, PhD candidates, postdocs and collaborators.

Please get in touch for any potential exciting collaboration.

BSc and MSc students can join us for supervision and internships. Please check our page for potential vacancies.

For prospective PhD students, you might want to check the PhD programme in Computer Science at the University of Exeter, and also programmes promoted by the College of Engineering, Mathematics and Physical Sciences in Exeter like the Doctoral Training Partnership (the deadline is in January every year but it requires getting in touch ahead of time for a project proposal) and the China Scholarship Council Partnership (the deadline is in January every year, up to 15 PhD studentships available for outstanding students from China). More PhD opportunities include external funding like:

Wellcome Trust Funding for PhD students;

STFC postgraduate studentship for careers in STEM.

For postdocs there might the possibility to kickstart collaborations through international funding opportunities like:

EPSRC Fellowships (Data Science and AI are strategic areas of EPSRC for 2021);

Economic and Social Research Council Fellowships (within 12 months of passing your viva);

Marie Curie Fellowships (the UK is an Associate Country eligible for EU funding);

For visiting scholars, please check these opportunities:

Royal Society Wolfson Visiting Fellowship (for research visits of up to 12 months).

For early career researchers, please check these opportunities:

Young Researchers on Complex Systems Bridge Grants – Small funding for national and international research visits.

More vacancies might open in the future and will be advertised here on this page.

Discover what we do at CogNosco Lab:

CogNosco Lab is actively involved in the organisation of these international research and outreach events:

Complexity and Cognition

CompCog – Complexity and Cognition is a satellite symposium at the Conference on Complex Systems bringing together scientists interested in modelling cognition as a complex system. The event is coordinated by our lead and by a team of young and senior professors working on cognitive networks and data. After a successful 1st edition in 2020, we were accepted for a full day at CCS2021.

Digital Colloquia on Cognitive Network Science

A series of informal seminar talks promoting #diversity in data science, with a focus on cognitive data and models. This training opportunity is tailored around students who want to pursue a career in data science, exposing them to diversity in scientific disciplines, in academia and industry, in science dissemination and learning.

Network Science and Education

 NetSciEd is an initiative started by and mainly promoted by US professionals and professors. Our lead joined it as he believes in the importance of promoting data and network literacy for innovating education and improving the perception of “hard” STEM disciplines like science and physics. The next satellite of NetSciEd is led by Dr. Evelyn Panagakou and is in June 2021 at Networks 2021.

Research spotlight: Our signature models at CogNosco

Multiplex lexical networks are multilayer representations of concepts and associations in the human mind. Nodes represent single concepts, e.g. “say” or “drama”. The same set of nodes is replicated across different layers, e.g. free associations or synonyms. Words are connected according to specific links on each layer, e.g. words might be synonym or sound similarly. Multiplexity originates from the possibility of connecting words through multiple links at the same time. This led to more accurate/predictive models of early language learning, either without or with machine learning, and clinical impairment in aphasia compared to baseline models considering layers in isolation.

Behavioural forma mentis networks are cognitive networks of free associations, i.e. indicating how words remind of each other when being read. Whereas network structure indicates patterns of semantic memory in the human mind, nodes are attributed also a sentiment label, i.e. are they perceived as positive, negative or neutral by individuals? Both labels and connections are extrated from a behavioural task, e.g. asking for students to read a word, write down the first associates that came to their minds and rate them all in terms of sentiment. In this way, behavioural forma mentis networks represent the structure and the perceived emotions of knowledge in people’s minds.

Our research used these networks to investigate how high-school students and STEM researchers perceived STEM subjects, identifying a cognitive dissonance. In fact, students perceived mathematical jargon as negative and eliciting anxiety, yet they strongly appreciated the general idea of science. Forma mentis networks were also used to detect mindset change in trainees and define computational thinking on a cognitive basis.

NB: “Forma mentis” is Latin for “mindset”. Here at CogNOSCO Lab we have a knack for language in general, including language from the past!

News and Lab Outputs:

November 2021: New pre-print co-led by our student Simmi Marina Joseph together with Salvatore Citraro, Virginia Morini and Giulio Rossetti of KDD Lab, CNR, Italy and University of Pisa:

October 2021: New pre-print led by our student Asra Fatima, in collaboration with Ying Li and Thomas T. Hills:


September 2021: New paper out on Scientific Reports, in collaboration with Andreia Sofia Teixeira, Szymon Talaga and Trevor J Swanson:

September 2021: Our lab members Asra Fatima, Simmi Marina Joseph and Martyna Wozniak had 3 abstracts accepted for presentation at CompCog21, satellite of CCS2021!

May 2021: Oral presentation accepted at Networks 2021, a joint Sunbelt and Netsci conference.

May 2021: Prof. Yoed N. Kenett (Technion, Israel) visited our lab by giving an invited lecture about cognitive network science.


March 2021: A new pre-print out: Cognitive network science investigating emotional perceptions of COVID-19 vaccines on social media. ArXiv version here.

December 2020: We are live, hello world!