Role
Dr Mark Andrews is an Associate Professor of Statistical Methods in the Department of Psychology. He teaches statistics to undergraduate and postgraduate students in the Psychology department, and has designed and is the course leader for the MSc in Behavioural Data Science. He teaches advanced training courses on statistical methods, data science, and machine learning using R and Python both within and beyond NTU. He is an NTU approved consultant on statistics and data science.
Career overview
Mark has a PhD and MSc in Cognitive Science from Cornell University. He was a postdoctoral research fellow in University College London, with positions first in the Gatsby Computational Neuroscience Unit and later in the Division of Psychology and Language Sciences.
Research areas
Mark's research interests are in statistical methods in the social and behavioural sciences, computational cognitive science and neuroscience, and the application of mathematical and statistical models to the understanding of human cognition and neuroscience.
External activity
Mark is a committee member of the Royal Statistical Society's section on teaching statistics, the Chair of the British Psychological Society's Mathematical, Statistical, and Computing Psychology section, and the deputy chair of the British Psychological Society's Statistics and Research Methods Advisory Panel.
Sponsors and collaborators
Mark has active collaborations with University of Leeds, Trinity College Dublin, Birmingham City University.
Publications
- Andrews, M., Justice, L. (2021). Statistical Analysis of Intervention Studies in Forensic Psychology. In Winder, B., Blagden, N., Hamilton, L., Scott, S. (Eds.), Forensic Interventions for Therapy and Rehabilitation: Case Studies and Analysis. Routledge.
- Andrews, M. (2021). Doing Data Science in R: An Introduction for Social Scientists. SAGE Publishing, London, UK.
- Jones, G., Cabiddu, F., Andrews, M., Rowland, C. (2021). Chunks of phonological knowledge play a significant role in children’s word learning and explain effects of neighborhood size, phonotactic probability, word frequency and word length. Journal of Memory and Language. 119, (104232).
- Wider, C., Mitra, S., Andrews, M., Boulton, H. (2020). Age-related differences in postural adjustments during limb movement and motor imagery in young and older adults. Experimental Brain Research. 238, (771-787).
- Pilling, M., Guest, D., Andrews, M. (2019). Perceptual Errors Support the Notion of Masking by Object Substitution. Perception. 48, (138-161).
- Andrews, M., Justice, L. (2019). The Replication Crisis. In Banyard, P., Norman, C., Dillon, G., Winder, B. (Eds.), Essential Psychology (3rd Ed). SAGE Publishing.
- Andrews, M. (2018). Some reflections on the replication crises: Reviews of "The Seven Deadly Sins of Psychology" and "Rigor Mortis". The Cognitive Psychology Bulletin. 3, (22-24).
- Andrews, M. (2017). Review of 'The Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice' by Chris Chambers. The Psychologist. 30, (58-59).
- Andrews, M. (2017). A Bayesian Model of Memory for Text. In Gunzelmann, G., Howes, A., Tenbrink, T., Davelaar, E. (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Cognitive Science Society.
- Andrews, M., Baguley, T. (2017). Bayesian Data Analysis. In Hopkins, B., Geangu, E., Linkenauger, S. (Eds.), The Cambridge Encyclopedia of Child Development (2nd Ed). Cambridge University Press.
- Baguley, T., Andrews, M. (2016). Handling Missing Data. In Robertson, J., Kaptein, M. (Eds.), Modern Statistical Methods for HCI. Springer.
- Andrews, M., Frank, S., Vigliocco, G. (2014). Reconciling Embodied and Distributional Accounts of Meaning in Language. Topics in Cognitive Science. 6(3), (359-370).
- Davies, J., Gander, P. E., Andrews, M., Hall, D. A. (2014). Auditory network connectivity in tinnitus patients: a resting-state fMRI study. International Journal of Audiology. 53(3), (192-198).
- Andrews, M. Frank, S. & Vigliocco, G. (In Press) Reconciling Embodied and Distributional Accounts of Meaning in Language. Topics in Cognitive Science
- Andrews, M. (In Press) Probabilistic Language Modeling with Hidden Stochastic Automata. Proceedings of the 35th Annual Meeting of the Cognitive Science Society.
- Vigliocco, G., Kousta, S., Vinson, D.P., Andrews, M., Del Campo, E. (2013). The Representation of Abstract Words: What Matters? Reply to Paivio's (2013) Comment on Kousta et al. (2011). Journal of Experimental Psychology: General. Vol. 142(1), pp. 288-291.
- Andrews, M., Baguley, T. (2013). Prior approval: The growth of Bayesian methods in psychology. British Journal of Mathematical & Statistical Psychology Vol. 66(1), pp. 1-7.
- Vinson, D., Andrews, M., Vigliocco, G. (2013). Giving words meaning: Why better models of semantics are needed in language production research. In V. Ferreira, M. Goldrick & M. Miozzo (Eds.), Oxford Handbook of Language Production.
- Vigliocco, G. & Andrews, M. (2012). The Limitations of the Distributional Hypothesis: Augmenting Distributional Statistics with Experiential Data. In Pier Marco Bertinetto, Valentina Bambini, Irene Ricci (eds.) Language and the Brain -- Semantics. Rome, Italy: Bulzoni.
- Andrews, M. (2011). A review of Doing Bayesian Data Analysis: A Tutorial Using R and BUGS by J. Kruschke. British Journal of Mathematical & Statistical Psychology Vol. 64(3), pp. 538-540.
- Kousta, S., Vigliocco, G., Vinson, D. P, Andrews, M. & Del Campo, E. (2011). The Representation of Abstract Words: Why Emotion Matters. Journal of Experimental Psychology-General, Vol. 140(1). pp. 14-34.
- Andrews, M. & Vigliocco, G. (2010) The Hidden Markov Topic Model: A Probabilistic Model of Semantic Representation. Topics in Cognitive Science, Vol. 2, pp. 101-113.
- Andrews, M. & Vigliocco, G. (2009) Learning Semantic Representations with Hidden Markov Topic Models. Proceedings of the 31st Annual Meeting of the Cognitive Science Society (This paper was awarded the annual prize for computational modeling of language at the Cognitive Science Conference.)
- Vigliocco, G., Meteyard, L., Andrews, M. & Kousta S. (2009) Toward a Theory of Semantic Representation. Language and Cognition, Vol. 1(2).
- Andrews, M., Vigliocco, G. & Vinson, D. (2009). Integrating Experiential and Distributional Data to Learn Semantic Representations. Psychological Review, Vol. 116(3), pp 463-498.
- Andrews, M. & Vinson, D. & Vigliocco, G. (2008). Inferring a Probabilistic Model of Semantic Memory from Word Association Norms. Proceedings of the 30th Annual Conference of the Cognitive Science Society.
- Andrews, M. & Vigliocco, G. & Vinson, D. (2007). Evaluating the Contribution of Intra-linguistic and Extra-linguistic Data to the Structure of Human Semantic Represenations. Proceedings of the 29th Annual Conference of the Cognitive Science Society.
- Andrews, M. & Vigliocco, G. & Vinson, D. (2005). Integrating Attributional and Distributional Information in a Probabilistic Model of Meaning Representation. In Timo Honkela, Ville Könönen, Matti Pöllä, and Olli Simula, editors, Adaptive Knowledge Representation and Reasoning. Pages 15-25.
- Andrews, M. & Vigliocco, G. & Vinson, D. (2005). The Role of Attributional and Distributional Information in Semantic Representation. Proceedings of the Twenty Seventh Annual Conference of the Cognitive Science Society.
- Andrews, M. & Salzberg, C. (2004) Sexual and Asexual Paradigms in Evolution: The Implications for Genetic Algorithms? Proceedings of the Genetics and Evolutionary Computation Conference (Gecco-04) .
- Andrews, M. (2003) Language Learning and Nonlinear Dynamical Systems., Ph.D Dissertation, Cornell University.
- Andrews, M. (2001) Processing and Recognition of Symbol Sequences, in K. S. Johanna D. Moore, ed., Proceedings of the Twenty Third Annual Conference of the Cognitive Science Society, pp. 61-65
Press expertise
Mark has expertise in the following areas:
- Statistical methods
- Machine learning
- Deep learning and artificial intelligence.