Computational Psychology and Computational Methods Lab

Understanding Human Behaviour through Experiments, Data, and Computational Methods


About the Lab

The Computational Psychology and Computational Methods Lab (CPCM Lab) focuses on the use of computational approaches to study the human mind and behaviour.

Our work is cross-disciplinary and seeks to answer two questions:

  1. How can computational methods enhance our understanding of the human mind and behaviour?
  2. How can psychological research methods inform our understanding of computational model behaviour?

We collect data in psychological experiments and develop and apply techniques from natural language processing, machine learning, and statistical modelling to better understand human behaviour and psychological processes.

Our lab brings together researchers with backgrounds in Psychology, Computer Science, Neuroscience, Artificial Intelligence, Linguistics, Mathematics, Law, and Cognitive Science.

The CPCM Lab is based at the Department of Methodology and Statistics at Tilburg University.


Lab updates


People

Bennett Kleinberg
Bennett Kleinberg

Bennett Kleinberg

Associate Professor, Lab director

Computational methods in psychology, machine learning for behavioural data, and methodological foundations of computational social science.

Riccardo Loconte
Riccardo Loconte

Riccardo Loconte

Postdoc

Opportunities and challenges of automated verbal deception detection

This project investigates how and to what extent computational approaches stemming from machine learning and natural language processing can advance verbal deception detection at large-scale.

Sanne Peereboom
Sanne Peereboom

Sanne Peereboom

Doctoral Researcher

Assessing the artificial mind through the marriage of natural language processing and psychometrics

My project is focused on understanding generative language models through psychological measurement frameworks. My work focuses on if – and how – psychometric approaches can be used to validly assess the behaviour of these models

John Caffier
John Caffier

John Caffier

Doctoral Researcher

Computational methods to measure, understand, and influence prosocial behavior and trust

In our project, we apply and develop methods and tools to measure and model the dynamics of trust and prosocial behaviors - individually and at scale. Also, we explore how LLMs, apps, and other technologies, as well as humans, can actively influence these behaviors in potentially harmful or potentially constructive directions.

Rasoul Norouzi Nikjeh
Rasoul Norouzi Nikjeh

Rasoul Norouzi Nikjeh

Doctoral Researcher

Text-mining methods for theory development in psychological and social science research

My PhD project develops text mining methods to automatically detect and parse causal claims in social science texts. It turns unstructured prose into structured who-causes-what representations and encodes them as Directed Acyclic Graphs (DAGs). This lets researchers identify recurring causal patterns, generate testable hypotheses, and conduct transparent evidence synthesis and theory refinement.

Jennifer Chen
Jennifer Chen

Jennifer Chen

Doctoral Researcher

Adolescent-Specific Assessment and Psychotherapy (ASAP): Innovating Idiographic Methods for Youth-Tailored Care

Tijn van Hoesel
Tijn van Hoesel

Tijn van Hoesel

Doctoral Researcher

Spin: Questionable Research Practices in Scientific Reporting

Investigating the concept of spin (primarily found in biomedicine) and relating it to the concept of questionable research practices (primarily found in psychology). Investigating the prevalence and impact of spin in psychological research.

Weng Lam Ao
Weng Lam Ao

Weng Lam Ao

Doctoral Researcher

Understanding decision-making in transport behaviour through social media data

Jari Zegers
Jari Zegers

Jari Zegers

Thesis student / research assistant

Psychological theories of deception and deception detection

As part of my master’s thesis, this project aims to improve our understanding of deception and deception detection from a psychological perspective.

Lucca Pfründer
Lucca Pfründer

Lucca Pfründer

Thesis student

Misleading Deception Classifiers with Model-Based and Human Paraphrasing Attacks

This project investigates whether automated deception classifiers (machine learning) are vulnerable to intentional modification of credibility statements. We also investigate how humans and large language models think those systems understand credibility.

Qian Chen
Qian Chen

Qian Chen

Visiting Doctoral Researcher (Central China Normal University)

Decoding distorted interpretations of ambiguity from text data

Everyday life is full of ambiguous social situations, and biased / inflexible interpretations of these situations are linked to depression and anxiety. Our work focuses on leveraging linguistic indicators of interpretation processes to improve understanding, measurement, and intervention methods that are more ecologically valid and translatable to real-world mental health.

Jonas Festor
Jonas Festor

Jonas Festor

Research assistant

Simulated vs. genuine empathy

This project tries to disentangle human perceptions of LLM generated empathetic text from the ‘objective’ convincingness. This study builds on and tries to extend the investigation of stochastic empathy.

Ivo Snels
Ivo Snels

Ivo Snels

Research assistant

Simulated vs. genuine empathy

This project tries to disentangle human perceptions of LLM generated empathetic text from the ‘objective’ convincingness. This study builds on and tries to extend the investigation of stochastic empathy.


Research

The CPCM Lab investigates how computational methods can enhance our understanding of the human mind and behaviour, and how psychological research can inform our understanding of computational models.

Research Themes

Deception Detection

  • Integrating experimental data and computational methods to address the “hard problems” of deception research
  • Examining how human adversarial machine learning can inform cognitive deception theory

Methodological advancements

Developing the methods needed to advance computational psychology research

  • Secure and scalable methods for text anonymisation (e.g., Textwash)
  • Sample size estimation algorithms for supervised machine learning

Machine Beahviour

  • Understanding stochastic humanness of large language models through experimental research
  • Using formal psychometric modelling to study the behaviour of artificial intelligence models and how it reflects or diverges from human behaviour and cognition.

Computational Psychology with Natural Language Processing

Using computational text analysis to study and predict psychological constructs in humans (e.g., cynicism, emotion, deception)


PhD alumni of the lab


Joining the lab

How can I join the CPCM lab?

Lab members are typically postdocs, PhD students, thesis students or research interns/assistants. Thesis projects are advertised in the programmes we are involved in. There are various routes for PhD projects (e.g., a funded position via university employment, a joint PhD with another university, self-funding). All positions that are connected to employment (typically for 4 years) are publicly advertised. If you are interested in a research internship, please identify a topic you are interested in that aligns with the lab’s focus and is relevant to at least one other lab member. Reach out to Bennett via email then. Research internships should last at least 6 months since projects require a solid embedding in psychological research and computational methods..


Contact

Computational Psychology and Computational Methods Lab (CPCM Lab)
Dr. Bennett Kleinberg, Department of Methodology and Statistics, Tilburg University, The Netherlands

📧 [mailto:bennett.kleinberg@tilburguniversity.edu;]


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