Trisevgeni Papakonstantinou

PhD Student, The Alan Turing Institute Enrichment Scheme and UCL

Trisevgeni’s primary focus is gaining a deeper understanding of how individuals revise their mental models of the world. This pursuit drives her current PhD studies at UCL’s Causal Cognition Lab. Her approach to investigating belief revision is characterised by a multidisciplinary perspective, incorporating experimental methods, computational modelling, natural language processing, and Bayesian updating. Trisevgeni’s research involves examining belief revision mechanisms both within controlled laboratory environments and in real-world contexts.

Trisevgeni is involved in the development and deployment of NLP methods within psychology research. She also demonstrates a keen interest in applying her findings to the field of public health. By exploring the practical applications of her substantive research, she aims to contribute valuable insights to address challenges in public health and other related domains.

At the Turing, Trisevgeni will be working on a project using naturalistic data to infer mental models and simulate the effects of various interventions. This research aims to build a better understanding of belief revision within interconnected systems of beliefs known as “mental models” or “intuitive theories.” These models or theories, resembling scientific frameworks, consist of concepts and causal links that shape individuals’ understanding of various domains. Trisevgeni intends to explore how these models are formed and how interventions on their components might impact the structure of intuitive theories, using real-world data.

The r/ChangeMyView subreddit provides a valuable naturalistic dataset for this research. r/ChangeMyView is a forum on Reddit where users post about views they hold and challenge others to change their minds. This dataset can be used to “crowdsource” the modelling of intuitive theories on a breadth of domains, ranging from climate change to AI responsibility. It also provides data on successful belief revision.

By utilising natural language processing and qualitative analysis methods, Trisevgeni aims to extract users’ individual beliefs, examine correlations within specific domains and build models based on these links. The second component of this project involves simulating the effects of interventions on the network, such as educational and coherence-based interventions. By identifying influential beliefs and effective interventions, this research will contribute to a deeper understanding of belief revision within larger networks and its practical applications.

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