Research Interests

My research has been focussed on understanding the mechanisms of association formation, a genuine interdisciplinary framework able to offer new insights into a variety of subjects, ranging from neural connectivity to computational neuroscience.

My interest is directed at studying phenomena that at face value are not susceptible to an associative analysis, and to the modifications of current associative theories required to accommodate them, allowing us to exploit the full power of this well specified learning paradigm. In particular, I have conducted research on:

(1) Rule acquisition in sequential discriminations

This research has shown that the ability to abstract across situations beyond the physical characteristics of stimuli, that is, to show rule-governed behaviour, is present in non-linguistic species as the rat and that well understood principles of conditioning might offer a basic mechanism to understand it (Mondragón, Murphy and Murphy, 2009; Murphy, Mondragón and Murphy, 2008).

(2) Context control and learning modulation

We have proposed that contextual control of learning could be better understood as a form of conditional learning, or "occasion setting" (Hall and Mondragón, 1998) and that this kind of functional hierarchical control, far from being confined to explicit discriminations, is an ubiquitous phenomenon that may arise in almost all learning situations (Mondragón, Bonardi & Hall, 2003).

(3) Associative analyses of perceptual learning

Our research has put forward the role played by the perceptual effectiveness of the uninformative common elements as foundation of the perceptual learning phenomenon (Hall and Mondragón, 2002; Mondragón and Murphy, 2010). This last paper reported the first study of perceptual learning with a standard appetitive Pavlovian conditioning in Skinner boxes.

(4) Computational neuroscience

I have collaborated with Dr Eduardo Alonso (City University London) developing computational tools for learning theories. We have built up a simulator of Rescorla and Wagner’s model (Mondragón, García Durán, and Alonso, 2009) and a temporal difference model of overshadowing in timing (Jennings, Alonso, Mondragón and Bonardi, 2011). We are currently implementing a Mackintosh (1975) and Pearce & Hall (1980) models simulator.

 

Publications

Alonso, E., Mondragón, E. & Fernández, A. (in press). A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions. Computer Methods and Programs in Biomedicine. doi: 10.1016/j.cmpb.2012.02.004.

Murphy, R.A., Schmeer, S., Vallée-Tourangeau, F., Mondragón, E. & Hilton, D. (2011). Making the illusory correlation effect appear and then disappear: The effects of increased learning. Quarterly Journal of Experimental Psychology, 64, 24-40. [pdf]

Mondragón, E. & Murphy, R. A. (2010). Perceptual learning in appetitive conditioning: Analysis of the Effectiveness of the Common Element. Behavioural Processes, 83, 247-256. [pdf]

Mondragón, E., Murphy, R. A. & Murphy, V.A. (2009). Rats do learn XYX rules. Animal Behaviour, 78, e3-e4. [pdf]

Murphy, R. A., Mondragón, E. & Murphy, V. A.(2009).Covariation, Temporal Order and Generalization:Building Blocks of Causal Cognition. International Journal of Comparative Psychology, 22, 61-74. [pdf]

Murphy, R.A., Mondragón, E. & Murphy, V. A. (2008). Rule learning by rats. Science, 319(5871), 1849-1851. [pdf]

Murphy, R.A., Mondragón, E., Murphy, V.A. & Fouquet, N. (2004). Serial order of conditional stimuli as a discriminative cue for Pavlovian conditioning. Behavioural Processes, 67, 303-311. [pdf]

Mondragón, E., Bonardi, C. & Hall, G. (2003). Negative priming and occasion setting in an appetitive Pavlovian procedure. Learning and Behavior, 31, 281-291. [pdf]

Mondragón, E. & Hall, G. (2002). Analysis of the perceptual learning effect in flavour aversion learning: Evidence for stimulus differentiation. Quarterly Journal of Experimental Psychology, 55B , 153-169. [pdf].

 

Books

Alonso, E. & Mondragón, E. (Eds.). (2011). Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications.Hershey, PA: IGI Global. [pdf]

 

Book Chapters

Jennings, D.J., Alonso, E., Mondragón, E. & Bonardi, C. (2011). Temporal uncertainty during overshadowing: A temporal difference approach. In E. Alonso and E. Mondragón (Eds.), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. Hershey, PA: IGI Global. [pdf]

Alonso, E. & Mondragón, E. (2011). Associative Learning and Behaviour: A Mathematical Model with Symmetries. In E. Alonso and E. Mondragón (Eds.), Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. Hershey, PA: IGI Global. [pdf]

Alonso, E. & Mondragón, E. (2004). Agency, Learning and Animal-Based Reinforcement Learning. In M. Nickles, M. Rovatsos & G. Weiss (Eds.), Agents and Computational Autonomy: Potential, Risks, and Solutions , LNAI 2969, pp.1-6. Berlin: Springer-Verlag. [pdf]

Hall, G. & Mondragón, E. (1998). Contextual control as Occasion Setting. In N. A. Schmajuk & P. C. Holland (Eds.), Occasion Setting: Associative learning and cognition in animals. Washington DC: American Psychological Association. [pdf]

 

Recent Conference Proceedings

Alonso, E., Fairbank, M. and Mondragón, E. (2012). Conditioning for Least Action. In N. Rußwinkel, U.  Drewitz, J. Dzaack, H. van Rijn and Frank Ritterthe. Proceedings 11th International Conference on Cognitive Modeling (ICCM-12): Berlin, Germany: Universitaetsverlag der TU Berlin.  April 13-15.

Alonso, E. and Mondragón, E. (2012). Uses, Abuses and Misuses of Computational Models in Classical Conditioning. In N. Rußwinkel, U.  Drewitz, J. Dzaack, H. van Rijn and Frank Ritterthe. Proceedings 11th International Conference on Cognitive Modeling (ICCM-12): Berlin, Germany: Universitaetsverlag der TU Berlin.  April 13-15.

Alonso, E., Mondragón, E. & Kjall-Ohlsson, N. (2012). Internally Driven Q-learning: Convergence and Generalization Results. In J. Filipe and A. Fred (Eds.) Proceedings of The Fourth International Conference on Agents and Artificial Intelligence (ICAART-2012): Vol. 1. (pp. 491-494). Vilamoura, Portugal: SciTe Press.

Alonso, E., Mondragón, E. & Kjäll-Ohlsson, N. (2006). Pavlovian and Instrumental Q-Learning: A Rescorla & Wagner-based approach to generalization in Q-learning. In T. Kovacs & J. Marshall (Eds.), Proceedings of AISB'06: Adaptation in Artificial and Biological Systems, 23-29. [pdf]

Jennings, D. J., Alonso, E., Mondragón, E. & Bonardi, C. (2006). Temporal uncertainty during overshadowing. In T. Kovacs & J. Marshall (Eds.), Proceedings of AISB'06: Adaptation in Artificial and Biological Systems, 64-65. [pdf]

Alonso, E. & Mondragón, E. (2005). Associative Learning and Reinforcement Learning: Where Animal Learning and Machine Learning Meet. In Z. Guessoum & E. Alonso (Eds.), Proceedings of the Fifth Symposium on Adaptive Agents and Multi-Agent Systems (AAMAS-05), 87-99. [pdf]