|Dr. Frank Baughman|
I completed my PhD at Birkbeck in 2009, under the supervision of Prof Michael Thomas and Prof Denis Mareschal and have since taken up a post-doctoral position, within Prof Mike Anderson's Neurcognitive Development Unit at the University of Western Australia, Perth. The work I am involved in now is very much a continuation of the work I did in my PhD where the focus was on exploring questions surrounding the development of intelligence in children. Specifically, I am interested in variation in cognitive skills that may underlie very similar overall mental ability. That is, are there different ways of achieving the same goals depending on how intelligent you are? What do these different types of cognitive process look like? A pdf of my thesis can be found here. Prior to my PhD I did a MSc in Psychological Research Methods and a BSc in Psychology, also at Birkbeck (a fantastic university and "London's leading specialist in part-time university education by evening study"). My MSc thesis on modelling problem solving on the Tower of London task in young children, can be found here.
My current research approach involves a mix of experimental and computational methodologies. The experimental methods I use includes a mix of on-line and off-line tasks. On-line tasks are those designed to tap the kinds of implicit and automatic processes related to abstract, symbolic and strategic thinking - tasks that consist of obtaining speed and accuracy data (e.g., the Stroop task, semantic priming and lexical access tasks). Tasks of off-line cognition are those aimed at measuring explicit forms of knowledge gained through experience (e.g., Piagetian-style tasks such as balance-scale and conservation of number and liquid). This mix of online and offline tests enables a more sensitive reading of children's cognitive abilities (see Karmiloff-Smith, Tyler, Voice, Sims, Udwin, Howlin and Davies, 1998). The computational methods I use include dynamical systems theory, connectionist networks and symbolic systems. Each of these types of computational approaches are used to examine what effect variation to the parameters that govern a range of normative models has on overall performance. Such computational models have proved to be an increasingly useful tool in the exploration of cognitive development and its possible basis in the brain. In particular, connectionist models provide an account of cognitive development using abstractions derived from neurocomputational principles. By making explicit the mechanisms of change connectionist networks allow theories to be directly testable and thus represent an improvement over verbal models of cognitive development. Whilst the self-organising nature of a connectionist model may be consistent with the organisation of the brain, these do not have to be seen as 'precise renditions' of neural functioning. I use computational models to provide a link between cognitive and developmental accounts and neuroscience descriptions of development (see, e.g., McDermott, 1995; Mareschal & Thomas, 2001; Schultz, 2003; Thomas & Karmiloff-Smith, 2002).
Links to other labs:
Neurcognitive Development Unit (NDU) - - note, this site is currently under construction
Karmiloff-Smith, A., Tyler, L., K., Voice, K., Sims, K., Udwin, O., Howlin, P. & Davies, M. (1998). Linguistic dissociations in Williams syndrome: evaluating receptive syntax in on-line and off-line tasks. Neuropsychologia, 36, 4, 343-351.
Mareschal, D. & Thomas, M. S. C. (2001). Self-organisation in normal and abnormal cognitive development. In A. F. Kalverboer, & A. Gramsbergen (Eds.). Handbook of Brain and Behaviour in Human Development (pp.743-766). Kluwer Academic Press.
McDermott, D. (1995). Penrose is Wrong. PSYCHE, 2.
Shultz, T., R. (2003). Computational explorations of cognitive development. Proceedings of the Twenty-fifth Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Thomas, M. S. C. & Karmiloff-Smith, A. (2002). Modelling typical and atypical cognitive development. In U. Goswami (Ed.), Handbook of Childhood Development (pp. 575-599). Blackwells Publishers.
Publications / presentations
Thomas, M. S. C., Richardson, F. M., Forrester, N. A., & Baughman, F. D. (under revision). Modelling individual variability in cognitive development. Connection Science.
Thomas, M. S. C., McClelland, J. L., Richardson, F. M., Schapiro, A. C., & Baughman, F. D. (2009). Dynamical and Connectionist Approaches to Development: Toward a Future of Mutually Beneficial Co-evolution. In J.Spencer, M.S.C. Thomas & J.L. McClelland (Eds.), Toward a new unified theory of development: Connectionism and dynamical systems theory re-considered. Oxford: Oxford University Press.
Baughman, F. D., & Thomas, M. S. C. (2008). Specific Impairments in Cognitive Development: A Dynamical Systems Approach. Paper accepted to the 30th Annual Conference of the Cognitive Science Society, July 23-26, Washington, D.C, USA.
Baughman, F. D., & Cooper, R. P. (2007). Inhibition and young children's performance on the Tower of London task. Cognitive Systems Research 8(3): 216-226.
Richardson, F. M., Baughman, F. D., Forrester, N. A., & Thomas, M. S. C. (2006). Computational Modeling of Variability in the Balance Scale Task. Paper presented at the Proceedings of the 7th International Conference of Cognitive Modeling.
Richardson, F. M., Forrester, N., Baughman, F. D., & Thomas, M. S. C. (2006). Computational Modeling of Variability in the Conservation Task. Proceedings of the 28th Annual Conference of the Cognitive Science Society, 26–29.
Baughman, F. D., & Cooper, R. P. (2006). Inhibition and Young Children's Performance on the Tower of London Task. Paper accepted to the 7th International Conference of Cognitive Modeling.
Richardson, F. M., Baughman, F. D., Forrester, N., & Thomas, M. S. C. (2005). Computational Modeling of Variability in the Balance Scale Task. Paper accepted to the 7th International Conference of Cognitive Modeling.
Baughman, F. D. (2005). The role of inhibition in young children's performance on the Tower of London: A computational study. Unpublished Masters Thesis, University of London.
Papers relating to neurocomputational approaches to development, for the 6th IEEE International Conference on Development and Learning (2007)