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Dr Esther Mondragón

Lecturer in Artificial Intelligence

School of Mathematics, Computer Science and Engineering Department of Computer Science

Contact details

Address

Dr Esther Mondragón A309G, College Building
City, University of London
Northampton Square
London EC1V 0HB
United Kingdom

Personal links

About

Overview

I am a computational cognitive neuroscientist, working primarily in biological-based AI and, more in particular, in developing reinforcement learning models of the brain and on how such models can be applied to build AI technology and solve AI problems.


I am a member of CitAI, the Artificial Intelligence Research Centre at City, and the Director of the cutting-edge MSc in Artificial Intelligence.


I am interested in learning processes, with emphasis on fundamental mechanisms shared across species and as applied to the development of AI algorithms and architectures. The theoretical core of my research is based on associative learning, a genuinely interdisciplinary framework able to offer new insights into a variety of topics, from neural connectivity to machine learning. My training as an experimental psychologist with near 15 years of experience in behavioural neuroscience allows me to synthesise natural and artificial approaches to cognition.

Funding

INDUSTRIAL PhD SCHOLARSHIP, BOSCH AASS

PI/CIs: Esther Mondragón / Alex Ter-Sarkisov and Eduardo Alonso.
Funder/scheme: Innovate UK.
Duration: 4 years (2022-26).
Amount: £140,000.

DEEPSYNC:AUTOMATED VFX FOR VIDEO DUBBING

PI/CIs: Eduardo Alonso / Alex Ter-Sarkisov and Esther Mondragón.
Funder/scheme: Innovate UK.
Duration: 18 months (2021-23).
Amount: £143,000.

FREE ENERGY PRINCIPLE FOR ADAPTIVE COGNITIVE ARCHITECTURES

PI/CIs: Michaël Garcia-Ortiz / Esther Mondragón and Eduardo Alonso.
Funder/scheme: DSTL, UK-France Joint Research PhD Programme.
Duration: 3 years (2020-23).
Amount: £98,000.



Currently my research is centred on using deep learning architectures and techniques to model associative learning as introduced in Mondragón, Alonso, & Kokkola (2017).

I am supervising seven students: Esther Mulwa, Corina Cătărău-Cotuțiu, Kiran Ikram, Alexander Dean, Alexander McCaffrey, Mpagi Kironde and Alexandra Clay.

New PhD students are welcome!

At City, University of London, I supervised to completion André Luzardo (The Rescorla-Wagner Drift-Diffusion Model, Oct 2017) and Niklas Kokkola (A Double-Error Correction Computational Model of Learning, Nov. 2017).

I direct the Centre for Computational and Animal Learning Research that aims at encouraging interdisciplinary research in learning and cognition by strengthening collaboration between learning theorists, biologists, neuroscientists, cognitive scientists, mathematicians, software developers and computer scientists.

Qualifications

  1. BSc, MSc, PhD Psychology, University of the Basque Country, Spain

Administrative roles

  1. Director MSc Artificial Intelligence, Sep 2020 – present
  2. Senior Tutor for Research (STR) for Artificial Intelligence and Adaptive Systems, Department of Computer Science

Memberships of professional organisations

  1. Member, Spanish Society for Comparative Psychology (SEPC), 1991 – present
  2. Member, Cognitive Science Society
  3. Member, Pavlovian Society
  4. International Affiliate and member of Division 6: Society for Behavioral Neuroscience and Comparative Psychology to the American Psychological Association (APA)
  5. Senior member, The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB).

Research students

Alexandra Clay

Attendance: Oct 2021 – present

Thesis title: Episodic Memory and Emotions in Artificial Systems and their Applications

Role: 2nd Supervisor

Mpagi Kironde

Attendance: Apr 2021 – present, full-time

Thesis title: Associative Learning using a Deep neural network based on unsupervised representation

Role: 1st Supervisor

Alexander W.J. Dean

Attendance: Oct 2020 – Sep 2024

Thesis title: Study of Algebraic Structures in Continual Representational Learning

Role: 1st Supervisor

Alexander J. McCaffrey

Attendance: Oct 2020 – Sep 2024

Thesis title: Free Energy Principle as Drive for Adaptive Cognitive Architectures

Role: 2nd Supervisor

Kiran Ikram

Attendance: Oct 2020 – present

Thesis title: Mixed Motive Multi Agent Learning Systems

Role: 2nd Supervisor

Corina Cătărău-Cotuțiu

Attendance: Oct 2020 – present

Thesis title: Adaptive concept formation on a predictive cognitive framework

Role: 1st Supervisor

Esther Mulwa

Attendance: 2019 – present

Thesis title: Building an Associative Model Using Deep Learning

Role: 2nd Supervisor

André Luzardo

Attendance: 2014 – 2017

Thesis title: The Rescorla-Wagner Drift-Diffusion Model

Role: External Supervisor

Niklas Kokkola

Attendance: 2013 – 2017

Thesis title: A Double-Error Correction Computational Model of Learning

Role: External Supervisor

Publications

Featured publications

  1. Kokkola, N.H., Mondragón, E. and Alonso, E. (2019). A double error dynamic asymptote model of associative learning. Psychological Review, 126(4), pp. 506–549. doi:10.1037/rev0000147.
  2. Mondragón, E., Alonso, E. and Kokkola, N. (2017). Associative Learning Should Go Deep. Trends in Cognitive Sciences, 21(11), pp. 822–825. doi:10.1016/j.tics.2017.06.001.
  3. Mondragón, E., Gray, J., Alonso, E., Bonardi, C. and Jennings, D.J. (2014). SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning. PLoS ONE, 9(7). doi:10.1371/journal.pone.0102469.
  4. Murphy, R.A., Mondragón, E. and Murphy, V.A. (2008). Rule Learning by Rats. Science, 319(5871), pp. 1849–1851. doi:10.1126/science.1151564.

Publications by category

Book

  1. Alonso, E. and Mondragón, E. (2010). Computational neuroscience for advancing artificial intelligence: Models, methods and applications. ISBN 978-1-60960-021-1.

Chapters (8)

  1. Jennings, D.J., Alonso, E., Mondragón, E. and Bonardi, C. (2011). Temporal Uncertainty During Overshadowing. Computational Neuroscience for Advancing Artificial Intelligence (pp. 46–55). IGI Global.
  2. Alonso, E. and Mondragón, E. (2011). Computational Models of Learning and Beyond. Computational Neuroscience for Advancing Artificial Intelligence (pp. 316–332). IGI Global.
  3. (2011). Computational Neuroscience for Advancing Artificial Intelligence. In IGI Global. ISBN 978-1-60960-021-1.
  4. Alonso, E. and Mondragón, E. (2010). Preface. ISBN 978-1-60960-021-1.
  5. Alonso, E. and Mondragon, E. (2007). Associative learning and behaviour: An algebraic search for psychological symmetries. In Aurnague, M., Korta, K. and Larrazabal, J.M. (Eds.), Language, Representation and Reasoning (pp. 35–35). Leioa, Spain: UPV-EHU Press.
  6. Alonso, E. and Mondragon, E. (2004). Agency, Learning and Animal-Based Reinforcement Learning. In Nickles, M., Rovatsos, M. and Weiss, G. (Eds.), Agents and Computational Autonomy: Potential, Risks, and Solutions (pp. 1–1). Berlin: Springer-Verlag.
  7. Hall, G. and Mondragon, E. (1998). Contextual control as Occasion Setting. In Schmajuk, N.A. and Holland, P.C. (Eds.), Occasion Setting: Associative learning and cognition in animals (pp. 199–199). Washington DC: American Psychological Association.
  8. Bonardi, C., Cheung, T.H.C., Mondragón, E. and Tam, S.K.E. Timing and Conditioning. The Wiley Handbook on the Cognitive Neuroscience of Learning (pp. 348–379). John Wiley & Sons, Ltd.

Conference papers and proceedings (10)

  1. Ikram, K., Mondragon, E., Alonso, E. and Garcia-Ortiz, M. (2021). HexaJungle: a MARL Simulator to Study the Emergence of Language. Conference on Computer Vision and Pattern Recognition (CVPR 2021), Embodied AI Workshop 19-25 June, Nashville, TN (virtual).
  2. (2014). Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison. International Conference on Agents and Artificial Intelligence 6-8 March.
  3. Alonso, E. and Mondragón, E. (2014). Quantum probability in operant conditioning: Behavioral uncertainty in reinforcement learning.
  4. Alonso, E., Sahota, P. and Mondragón, E. (2014). Computational models of classical conditioning: A qualitative evaluation and comparison.
  5. Alonso, E. and Mondragón, E. (2013). Associative reinforcement learning: A proposal to build truly adaptive agents and multi-agent systems.
  6. (2012). INTERNALLY DRIVEN Q-LEARNING - Convergence and Generalization Results. International Conference on Agents and Artificial Intelligence 6-8 February.
  7. Alonso, E. and Mondragón, E. (2012). Uses, abuses and misuses of computational models in classical conditioning.
  8. Alonso, E., Fairbank, M. and Mondragón, E. (2012). Conditioning for least action.
  9. Alonso, E., Mondragón, E. and Kjäll-Ohlsson, N. (2006). Pavlovian and Instrumental Q-learning: A Rescorla-Wagner-based approach to generalization in Q-learning.
  10. Alonso, E. and Mondragón, E. (2004). Agency, Learning and Animal-Based Reinforcement Learning.

Journal articles (15)

  1. Alonso, E., Fairbank, M. and Mondragón, E. (2015). Back to optimality: a formal framework to express the dynamics of learning optimal behavior. Adaptive Behavior, 23(4), pp. 206–215. doi:10.1177/1059712315589355.
  2. Bonardi, C., Mondragón, E., Brilot, B. and Jennings, D.J. (2015). Overshadowing by fixed- and variable-duration stimuli. Quarterly Journal of Experimental Psychology, 68(3), pp. 523–542. doi:10.1080/17470218.2014.960875.
  3. Mondragón, E. and Hall, G. (2015). Analysis of the role of stimulus comparison in discrimination learning in Pigeons. Learning and Motivation, 49, pp. 14–22. doi:10.1016/j.lmot.2015.01.003.
  4. Alonso, E. and Mondragón, E. (2014). What Have Computational Models Ever Done for Us?: A Case Study in Classical Conditioning. International Journal of Artificial Life Research (IJALR), 4(1), pp. 1–12.
  5. Mondragón, E., Alonso, E., Fernández, A. and Gray, J. (2013). An extension of the Rescorla and Wagner Simulator for context conditioning. Computer Methods and Programs in Biomedicine, 110(2), pp. 226–230. doi:10.1016/j.cmpb.2013.01.016.
  6. Mondragón, E., Gray, J. and Alonso, E. (2013). A Complete Serial Compound Temporal Difference Simulator for Compound stimuli, Configural cues and Context representation. Neuroinformatics, 11(2), pp. 259–261. doi:10.1007/s12021-012-9172-z.
  7. Jennings, D.J., Alonso, E., Mondragón, E., Franssen, M. and Bonardi, C. (2013). The effect of stimulus distribution form on the acquisition and rate of conditioned responding: Implications for theory. Journal of Experimental Psychology: Animal Behavior Processes, 39(3), pp. 233–248. doi:10.1037/a0032151.
  8. Alonso, E., Mondragón, E. and Fernández, A. (2012). A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions. Computer Methods and Programs in Biomedicine, 108(1), pp. 346–355. doi:10.1016/j.cmpb.2012.02.004.
  9. Murphy, R.A., Schmeer, S., Vallée-Tourangeau, F., Mondragón, E. and Hilton, D. (2011). Making the illusory correlation effect appear and then disappear: The effects of increased learning. Quarterly Journal of Experimental Psychology, 64(1), pp. 24–40. doi:10.1080/17470218.2010.493615.
  10. Mondragón, E. and Murphy, R.A. (2010). Perceptual learning in an appetitive Pavlovian procedure: Analysis of the effectiveness of the common element. Behavioural Processes, 83(3), pp. 247–256. doi:10.1016/j.beproc.2009.12.007.
  11. Mondragón, E., Murphy, R.A. and Murphy, V.A. (2009). Rats do learn XYX rules. Animal Behaviour, 78(4). doi:10.1016/j.anbehav.2009.07.013.
  12. Murphy, R.A., Mondragon, E. and Murphy, V.A. (2009). Covariation, Structure and Generalization: Building Blocks of Causal Cognition. International Journal of Comparative Psychology, 22(1), pp. 61–61.
  13. Murphy, R.A., Mondragón, E., Murphy, V.A. and Fouquet, N. (2004). Serial order of conditional stimuli as a discriminative cue for Pavlovian conditioning. Behavioural Processes, 67(2), pp. 303–311. doi:10.1016/j.beproc.2004.05.003.
  14. Mondragón, E., Bonardi, C. and Hall, G. (2003). Negative priming and occasion setting in an appetitive Pavlovian procedure. Animal Learning & Behavior, 31(3), pp. 281–291. doi:10.3758/bf03195989.
  15. Mondragón, E. and Hall, G. (2002). Analysis of the Perceptual Learning Effect in Flavour Aversion Learning: Evidence for Stimulus Differentiation. The Quarterly Journal of Experimental Psychology Section B, 55(2b), pp. 153–169. doi:10.1080/02724990143000225.

Report

  1. Mondragon, E. and Alonso, E. (2016). Hall and Rodríguez model as a particular case of the Pearce and Hall model: A formal analysis. Centre for Computational and Animal Learning Research.

Software (11)

  1. Chung, B., Mondragon, E. and Alonso, E. (2018). Rescorla & Wagner Simulator+ © Ver. 5. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  2. Kokkola, N., Mondragon, E. and Alonso, E. (2018). DOUBLE ERROR DYNAMIC ASYMPTOTE MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  3. Anandasivathas, T., Mondragon, E. and Alonso, E. (2017). Harris Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  4. Byers, J., Mondragon, E. and Alonso, E. (2017). SOP Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  5. Gheorghescu, A., Mondragon, E. and Alonso, E. (2017). PEARCE MODEL SIMULATOR ver. 1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  6. Ghorashi, A., Mondragon, E. and Alonso, E. (2017). REPLACED ELEMENTS MODEL - REM SIMULATOR v.1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  7. Grikietis, R., Mondragon, E. and Alonso, E. (2016). PEARCE & HALL MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  8. Alexandrakis, D., Mondragon, E. and Alonso, E. (2015). RESCORLA & WAGNER SIMULATOR FOR ANDROID V.1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  9. Gray, J., Mondragon, E. and Alonso, E. (2013). SSCC TD MODEL SIMULATOR ver. 1.0. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  10. Mondragon, E., Gray, J. and Alonso, E. (2012). TEMPORAL DIFFERENCE MODEL SIMULATOR ver. 1.0. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
  11. Mondragon, E., Alonso, E., Fernandez, A. and Gray, J. (2012). RESCORLA & WAGNER MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).

Professional activities

Collaboration (academic)

  1. Researcher of the Knowledge Graphs (KGs) Interest group (Nov 2020 – present) at The Alan Turing Institute project
    Sponsored by The Alan Turing Institute

Other (4)

  1. Reviewer of grant proposals for the US National Science Foundation (NSF).
  2. Reviewer of grant proposals for the Biotechnology and Biological Sciences Research Council (BBSR)..
  3. Reviewer of grant proposals for the National Science Centre Poland, Narodowe Centrum Nauki, NCN, Poland.
  4. Ad hoc reviewer for Psychological Review, Scientific Reports – Nature, Cognitive Science, Behavior Research Methods, Journal of Experimental Psychology: Animal Learning and Cognition, Bulletin of Mathematical Biology, The Quarterly Journal of Experimental Psychology, PLOS ONE, Learning & Behavior, Animal Cognition, Psicológica, Open Journal of Experimental Psychology and Neuroscience, Cognitive Psychology.