The Methods and Meta-Science Centre aims to develop and promote evidence-based methods and structures for robust and reproducible research. As articulated in the University Research Strategy 2020-2025, it has become increasingly apparent that how research is done is as important as what is done. Our activities and research promote quality and integrity, encourage large-scale collaboration, promote environments that support creative methodological and statistical innovation, and nurture early-career researchers. In addition to empirical research and methods development, we have a strong commitment to education, such as the open source PsyTeachR book series and the M&Ms Seminar Series.

Our centre consists of four working groups with overlapping members, aims and activities.

Digital Health and Behaviour Change

This working group studies fundamental processes associated with health behaviour and behaviour change. Domains of interest include eating, drinking, stress, trichotillomania, social isolation, sustainable behaviour, and climate anxiety. Our work ranges from establishing the cognitive, affective, and behavioural processes that underlie health behaviour and behaviour change to developing assessment and intervention tools informed by both theory and empirical evidence. Themes central to our work include situated cognition, goal pursuit, and habitual behaviour. We develop digital tools related to assessment and intervention. We often work with partners in industry and the third sector, and also have a strong interdisciplinary orientation, working with collaborators in a variety of other disciplines, such as public health, nutrition, and computer science.

Leads: Dr Esther K. Papies and Prof. Lawrence W. Barsalou

Open Research

This working group creates open learning materials and studies meta-scientific aspects of the research process. Core activities of the group are the PsyTeachR online book series for teaching reproducible research (co-led by the Pedagogy Centre) and the Coding Club for developing computational skills in a fun way. Members work with or advise a variety of partners, such as the UK Reproducibility Network, ORCiD, UKRIO, and the American Psychological Association.

Lead: Lisa DeBruine

Statistics and Mathematical Psychology

This working group investigates novel hierarchical statistical models combining analytical work, simulation studies and applications to different domains of cognitive psychology and neuroscience (e.g., decision making, learning and memory, theory of mind). Examples are the development of Bayesian hierarchical models, hierarchical linear models with mixed effects and other model generalizations. The main goal is to establish new quantitative methods that bridge gaps between existing approaches or expand statistical models in terms of generalizability and robustness. A primary effort is to make these methods and tools open-access by documenting source code, packages and data on open-access repositories (e.g., OSF, GitHub).  

Leads: Martin Lages and Dale Barr

Language Methods

This working group is concerned with (primarily) basic research in the area of language comprehension and production, from word-level (lexical and phonological) processing to processing at the sentence and even discourse level. Research on language processing has spearheaded important methodological developments in both design and analysis of psychological experiments in general. For instance, psycholinguists were among the first to identify the problem of simultaneous generalization of findings to both the participant and the item populations that experiments typically draw from, leading to the wide adoption of generalized linear mixed effects modelling techniques in recent years, which increasingly find applications in other areas of psychology as well. The language methods group considers a wide variety of interdisciplinary methodological approaches to language research, ranging from machine learning techniques in computational linguistics to behavioural (e.g., priming, eye-tracking) and neuroscientific methods (e.g., EEG, fMRI, MEG).

Leads: Christoph Scheepers and Sara Sereno

Selected Publications

  • Taylor, J. E., Rousselet, G., Scheepers, C., & Sereno, S. C. (in press). Rating norms should be calculated from cumulative link mixed effects models. Behavior Research Methods.  
  • Curley, L. J., Munro, J., Turner, J., Frumkin, L. A., Jackson, E. and Lages, M. (2022) Proven and not proven: a potential alternative to the current Scottish verdict system. Behavioral Sciences and the Law, 
  • Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. M. (2022). Data Visualization Using R for Researchers Who Do Not Use R. Advances in Methods and Practices in Psychological Science.
  • Papies, E.K., Barsalou, L.W., Claassen, M.A., Davis, T., Farrar, S.T., Gauthier, E., Rodger, A., Tatar, B., Wehbe, L.H., & Werner, J. (2022). Grounding Motivation for Behaviour Change. In B. Gawronski (Ed.), Advances in Experimental Social Psychology (Vol. 65, in press). Academic Press,
  • DeBruine, L. M., & Barr, D. J. (2021). Understanding Mixed-Effects Models Through Data Simulation. Advances in Methods and Practices in Psychological Science.
  • Papies, E. K., Claassen, M. A., Rusz, D., & Best, M. (2021). Flavours of desire: Establishing and understanding representations of appetitive stimuli and their motivational implications. Journal of Experimental Psychology: General, in press.