Dr Esther Mondragón
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About
Overview
I am a computational cognitive neuroscientist, working primarily in bio-inspired Artificial Intelligence and, more in particular, in developing reinforcement learning models of cognition and on how such models can be applied to build AI technology and solve AI problems. My main interest is in modelling learning processes, with a theoretical emphasis on associative principles. This theoretical framework has proved able to offer new insights at different levels of natural and artificial cognition, from neural connectivity to machine learning.
I am a member of CitAI, the Artificial Intelligence Research Centre at City, and the chair of the SST's Research Degrees Committee.
Recently, my work has been more inclined to classical areas of AI, namely, learning representations in Reinforcement learning [e.g., Dean, A., Alonso, E., & Mondragón, E. (2025) and Catarau-Cotutiu, C., Mondragón, E., & Alonso, E. (2026)] and the integration of Hebbian learning and deep learning models [e.g., Jimenez Nimmo, J. & Mondragón, E. (2025)].
I am also keen on assessing the role of associative mechanisms in representational learning, especially on the potential contribution of deep learning architectures to model such a process, as introduced in Mondragón, Alonso, & Kokkola (2017), creativity and exploring the role of episodic memory in AI and on producing computational models capable of capturing the rich theoretical background of associative learning.
Modeling research
1. A general model of associative learning, the Double Error Dynamic Asymptote (DDA) model, instantiated in a fully-connected network (Kokkola, Mondragón, &Alonso, 2019). The model's representational architecture explicitly incorporates interactions between so-called neutral stimuli (including the context) with the traditional Pavlovian structure, physically present or associatively retrieved from memory. The DDA model provides a systematic account of learning phenomena that emerge naturally from the synergy of the model’s unique features, including a double error term, a revaluation attentional rate and, critically, a dynamic asymptote that determines the direction of learning.
2. An integrative model of classical conditioning and timing that combines drift diffusion processes with an error correction mechanism (Luzardo, Alonso, & Mondragón, 2017). The Rescorla-Wagner Drift-Diffusion Model (RWDDM) integrates the flexibility, computational economy and timescale invariance of a noisy linear accumulator to represent time and the Rescorla-Wagner rule, which enables it to account for phenomena which collectively pose a challenge to either approach separately.
3. We have proposed an extension of the Temporal Difference learning algorithm, the SSCC TD model, that introduces a novel configural stimulus representation for both simultaneous and serial compound stimuli, enabling the prediction of phenomena for which traditional Temporal Difference solutions are insufficient (Mondragón, Gray, Alonso, Bonardi & Jennings, 2014).
In terms of software development, we have computationally specified, extended, and built applications for several associative models such as Rescorla-Wagner’s –a simulator with provisions for context and configural cue learning (Alonso, Mondragón & Fernández, 2012; Mondragón, Alonso, Fernández & Gray, 2013; RW Simulator) and for multiple reinforcers (Chung, Mondragón, & Alonso, 2018; RW Plus Simulator), Harris elemental model with an integral buffer (Anandasivathas, Mondragón, & Alonso, 2018), SOP (Byers, Mondragón, & Alonso, 2017), Pearce configural model (Gheorghescu, Mondragón, & Alonso, 2017) and the Replaced Elements Model (Ghorashi, Mondragón, & Alonso, 2017) to mention a few.
Although I am currently dedicated to modelling, I have over 15 years of experience as an experimentalist with extensive laboratory work.
I direct the virtual Centre for Computational and Animal Learning Research, which 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.
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.
I currently supervise several PhD students: Baris Cekic, Bean Opperman, Alexander Dean, Abraham Chakawa, and Bochen Zhao. New PhD students are welcome!
At City, University of London, I have supervised several doctoral students to successful completion: Corina Cătărău-Cotuțiu (A model of functional creativity for generalisation enhancement), Alexander McCaffrey (Free Energy Principle as Drive for Adaptive Cognitive Architectures), Esther Mulwa (Building An Associative Learning Model Using Deep Learning), Niklas Kokkola (A Double-Error Correction Computational Model of Learning) and André Luzardo (The Rescorla-Wagner Drift-Diffusion Model).
Qualifications
- BSc, MSc, PhD Psychology, University of the Basque Country, Spain
Administrative roles
- Chair of the School of Science and Technology Research Degrees Committee, November 2022 - present
- Member of the Doctoral College Board of Studies, November 2022 - present
- Director MSc Artificial Intelligence, September 2020 - present
- Senior Tutor for Research (STR) for Artificial Intelligence and Adaptive Systems, Department of Computer Science
- Member of the SST Board of Studies, Dec 2022 – present
Memberships of professional organisations
- Member, Spanish Society for Comparative Psychology (SEPC), 1991 - present
- Senior member, The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB).
- Member, Cognitive Science Society
- Member, Pavlovian Society
- International Affiliate and member of Division 6: Society for Behavioral Neuroscience and Comparative Psychology to the American Psychological Association (APA)
Research students
Abraham Chakawa
Attendance: April 2023 - present, full-time
Thesis title: Improving Predictive Models with Casual Structure Learning
Role: 1st Supervisor
Mpagi Kironde
Attendance: April 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: October 2020 - September 2024
Thesis title: Study of Algebraic Structures in Continual Representational Learning
Role: 1st Supervisor
Alexander J. McCaffrey
Attendance: October 2020 - September 2024
Thesis title: Free Energy Principle as Drive for Adaptive Cognitive Architectures
Role: 2nd Supervisor
Corina Cătărău-Cotuțiu
Attendance: October 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
- Catarau-Cotutiu, C., Mondragón, E. and Alonso, E. (2026). A representational framework for learning and encoding structurally enriched trajectories in complex agent environments. Neural Networks, 200. doi:10.1016/j.neunet.2026.108868
- Dean, A., Alonso, E. and Mondragón, E. (2025). Algebras of actions in an agent's representations of the world. Artificial Intelligence, 348, pp. 104403-104403. doi:10.1016/j.artint.2025.104403
- Jimenez Nimmo, J. and Mondragon, E. (2025). Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks. Neural Networks, 190
- McCaffrey, A., Alonso, E. and Mondragón, E. (2025). Predictive Improvement through Latent Space Optimisation. The 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025) 19-23 May, Detroit, USA.doi:10.5555/3709347.3743973
- Mondragón, E. (2024). Mediated learning: A computational rendering of ketamine-induced symptoms. Behavioral Neuroscience, 138(3), pp. 178-194. doi:10.1037/bne0000591
- 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
- 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
- Luzardo, A., Alonso, E. and Mondragón, E. (2017). A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing. . doi:10.1101/184465
- 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), pp. e102469-e102469. doi:10.1371/journal.pone.0102469
- 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
- Alonso, E. and Mondragón, E. (2010). Computational neuroscience for advancing artificial intelligence: Models, methods and applications.
Chapters (9)
- Alonso, E. and Mondragón, E. (2024). NFTs 101. NFTs, Creativity and the Law (pp. 1-19). Routledge.
- Bonardi, C., Cheung, T.H.C., Mondragón, E. and Tam, S.K.E. (2016). Timing and Conditioning. (pp. 348-379). Wiley. ISBN 9781118650943.
- 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. ISBN 9781609600211.
- Alonso, E. and Mondragón, E. (2011). Computational Models of Learning and Beyond. Computational Neuroscience for Advancing Artificial Intelligence (pp. 316-332). IGI Global. ISBN 9781609600211.
- In Alonso, E. and Mondragón, E. (Eds.), (2011). Computational Neuroscience for Advancing Artificial Intelligence. In IGI Global. ISBN 9781609600211.
- Alonso, E. and Mondragón, E. (2010). Preface. (pp. xii-xiv).
- 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.
- 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.
- 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.
Conference papers and proceedings (14)
- Opperman, B., Alonso, E. and Mondragon, E. (2026). Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries. The 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), Workshop on Adaptive and Learning Agents (ALA) 25-29 May, Paphos, Cyprus.
- Mir, A., Alonso, E. and Mondragón, E. (2024). DiT-Head: High Resolution Talking Head Synthesis Using Diffusion Transformers. 16th International Conference on Agents and Artificial Intelligence 24-26 February.doi:10.5220/0012312200003636
- Cătărău-Cotuţiu, C., Mondragón, E. and Alonso, E. AIGenC: AI Generalisation via Creativity. .doi:10.1007/978-3-031-49011-8_4
- 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 20-25 June, Nashville, Tennessee.
- (2014). Computational Models of Classical Conditioning - A Qualitative Evaluation and Comparison. International Conference on Agents and Artificial Intelligence 6-8 March.doi:10.5220/0004903105440547
- (2014). Quantum Probability in Operant Conditioning - Behavioral Uncertainty in Reinforcement Learning. International Conference on Agents and Artificial Intelligence 6-8 March.doi:10.5220/0004903205480551
- Alonso, E. and Mondragón, E. Quantum probability in operant conditioning: Behavioral uncertainty in reinforcement learning. .doi:10.5220/0004903205480551
- Alonso, E., Sahota, P. and Mondragón, E. Computational models of classical conditioning: A qualitative evaluation and comparison. .doi:10.5220/0004903105440547
- Alonso, E. and Mondragón, E. Associative reinforcement learning: A proposal to build truly adaptive agents and multi-agent systems. .
- Alonso, E. and Mondragon, E. (2012). Uses, Abuses and Misuses of Computational Models in Classical Conditioning. 11th International Conference on Cognitive Modeling 13-15 April, Berlin, Germany.
- (2012). INTERNALLY DRIVEN Q-LEARNING - Convergence and Generalization Results. International Conference on Agents and Artificial Intelligence 6-8 February.doi:10.5220/0003736404910494
- Alonso, E., Fairbank, M. and Mondragon, E. (2012). Conditioning for Least Action. 11th International Conference on Cognitive Modeling 13-4 January, Berlin, Germany.
- Alonso, E., Mondragón, E. and Kjäll-Ohlsson, N. Pavlovian and Instrumental Q-learning: A Rescorla-Wagner-based approach to generalization in Q-learning. .
- Alonso, E. and Mondragón, E. Agency, Learning and Animal-Based Reinforcement Learning. .doi:10.1007/978-3-540-25928-2_1
Journal articles (15)
- 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
- 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
- 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
- Alonso, E. and Mondragón, E. (2014). What Have Computational Models Ever Done for Us? International Journal of Artificial Life Research, 4(1), pp. 1-12. doi:10.4018/ijalr.2014010101
- 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
- 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
- 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
- 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
- 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
- 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
- Mondragón, E., Murphy, R.A. and Murphy, V.A. (2009). Rats do learn XYX rules. Animal Behaviour, 78(4), pp. e3-e4. doi:10.1016/j.anbehav.2009.07.013
- Murphy, R., Mondragon, E. and Murphy, V.A. (2009). Covariation, Structure and Generalization: Building Blocks of Causal Cognition. International Journal of Comparative Psychology, 22(1), pp. 61-74
- 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
- Mondragón, E., Bonardi, C. and Hall, G. (2003). Negative priming and occasion setting in an appetitive Pavlovian procedure. Learning & Behavior, 31(3), pp. 281-291. doi:10.3758/bf03195989
- 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
- Mondragon, E. and Alonso, E. Hall and Rodríguez model as a particular case of the Pearce and Hall model: A formal analysis. St Albans, UK: Centre for Computational and Animal Learning Research.
Software (12)
- Fixman, M., Abati, A., Jiménez Nimo, J., Lim, S. and Mondragón, E. (2026). PALMS: Pavlovian Associative Learning Models Simulator. https://github.com/cal-r/PALMS-Simulator.: CAL-R.
- 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).
- 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).
- Anandasivathas, T., Mondragon, E. and Alonso, E. (2017). Harris Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Byers, J., Mondragon, E. and Alonso, E. (2017). SOP Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- 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).
- 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).
- Grikietis, R., Mondragon, E. and Alonso, E. (2016). PEARCE & HALL MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- 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).
- 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).
- 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).
- 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)
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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)
- Reviewer of grant proposals for the US National Science Foundation (NSF) .
- Reviewer of grant proposals for the Biotechnology and Biological Sciences Research Council (BBSR). .
- Reviewer of grant proposals for the National Science Centre Poland , Narodowe Centrum Nauki, NCN, Poland .
- 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 .