Love Lab

Lab Manager
University College London
Experimental Psychology
26 Bedford Way
WC1H 0AP
London, UK

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About

Welcome to the UCL Love Lab, a learning and decision making lab based at UCL Experimental Psychology department and headed by Prof. Brad Love.

We are interested in understanding how humans learn and make decisions through formal modelling and behavioural approaches combined with functional neuroimaging. For more details refer to our publications

Our lab motto is "Inclusive, Productive, Accountable" or IPA for short, cheers!

People

The UCL Love Lab consists of postdocs and PhD students. See below for more details.

Principal Investigator

Bradley C. Love
Bradley C. Love

Bradley Love is Professor of Cognitive and Decision Sciences at UCL. He integrates approaches from Experimental Psychology, Machine Learning, and Neuroscience to understand the mechanisms supporting human learning and decision making.

Research Scientist

Olivia Guest
Olivia Guest

My research involves computational modelling of categorisation and semantic memory. I am interested in the representation of categories and concepts in healthy participants, patient groups, infants and children, and animal models.

Postdocs

Sebastian Bobadilla-Suarez
Sebastian Bobadilla-Suarez

During my PhD, I worked on various different topics such as heuristics and biases in decision-making as well as on models of similarity for neuroimaging data. Currently, I am working on relating models of similarity from fMRI data to models of similarity derived from artificial neural networks. Other efforts include optimizing parameter estimates for GLMs on fMRI data and biomarker exploration thereof.

Kurt Braunlich
Kurt Braunlich

Broadly, I combine neuroimaging (primarily fMRI), behavioral experimentation, mathematical modeling, and modern “machine-learning” approaches to investigate biological mechanisms underlying our ability to draw meaningful information from the world around us. My work is closely related to the fields of categorization and decision-making.

Robert Mok
Robert Mok

I am interested in how the brain constructs mental representations of the world and how this enables complex thought and behaviour. My current research focusses on category learning in the brain using fMRI (including multivariate pattern analysis), cognitive modelling, and behavioural methods. Recently, I have been thinking about these questions in terms of concepts and abstract thought — how does the brain organise abstract information during learning? I am intrigued by recent work that suggests the brain might construct a 'cognitive map' for coding different types of task-relevant information, including regions in the medial temporal lobe and prefrontal cortex.

Brett Roads
Brett Roads

The goal of my research is to boost human learning and decision making using formal models of cognition. I use formal models ranging from highly constrained psychological models to relatively unconstrained artificial neural networks. My current research focuses on developing psychological stimulus representations and category learning models in order to make more accurate predictions of learning outcomes.

Nicholas Sexton
Nicholas Sexton

I'm interested in neural network models and human high-level cognition, and making links between the two. Currently I'm exploring an embedding spaces perspective on mental representations. Specifically, linking human brain data and neural network models by comparing their representations in embedding space. Mostly, my methods involve deep neural network models trained end-to-end on naturalistic datasets (e.g., categorisation of real-world images). My PhD was on modelling backward inhibition within human task switching (the n-2 repetition cost), and I remain interested in mechanistic accounts of human cognitive control processes.

PhD Students

Adam Hornsby
Adam Hornsby

My research focuses on human learning, decision making and the ways it can be improved. For example – in one of my projects – I am trying to understand how consumers develop preferences and forage for information in complex environments. A key aim of this research is to inform the design of systems that aid decision making, such as recommender systems. Across all of my work, I’m keen to be data-driven, using cognitive models and machine learning wherever possible.

Xiaoliang (Ken) Luo
Xiaoliang (Ken) Luo

My research focuses on relating neural embedding spaces to embedding spaces extracted from real-world images. By using unsupervised deep learning approaches such as auto-encoders, I aim to shed some light on our understanding of human cognition.

Press

Selected mainstream media articles on the Love Lab’s work:

See the Publications section for more details.

Resources

Selected datasets, stimuli, and other resources from the lab. For code, also see the lab GitHub account — for data, also see Brad's OSF profile.

Beetle Stimuli
Beetle Stimuli
Love, B.C. (2017)
URL: https://osf.io/skg2y/

Stimulus set for use in categorization tasks based on illustration work by Frances Fawcett. Please cite one or more of these papers to acknowledge use:

Decoding the brain's algorithm for categorization from its neural implementation
Decoding the brain's algorithm for categorization from its neural implementation
Mack, M.L., Preston, A., Love, B.C. (2016)
URL: https://osf.io/62rgs/

Data from: Mack, M. L., Preston, A. R., & Love, B. C. (2013). Decoding the brain’s algorithm for categorization from its neural implementation.

Heuristics under Time Pressure
Heuristics under Time Pressure
Bobadilla-Suarez, S., & Love, B.C. (2015)
URL: https://osf.io/xnbz4/

Data from: Bobadilla-Suarez, S. & Love, B. C. (2018) Fast or Frugal, but not both: Decision Heuristics under Time Pressure.

Publications

2018

Schulz, E., Bhui, R., Love, B.C., Brier, B., Todd, M.T., Gershman, S.J. (2018). Exploration in the wild. bioRxiv. https://doi.org/10.1101/492058
Hornsby, A. N., Evans, T., Riefer, P. S., Prior, R., & Love, B. C. (2018). Conceptual Organization is Revealed by Consumer Activity Patterns. arXiv. https://doi.org/1810.08577
Bobadilla-Suarez, S., Ahlheim, C., Mehrotra, A., Panos, A., & Love, B. C. (2018). Measures of neural similarity. bioRxiv. https://doi.org/10.1101/439893
Inhoff, M.C., Libby, L.A., Noguchi, T., & Love, B. C. (2018). Dynamic integration of conceptual information during learning. bioRxiv. https://doi.org/10.1101/280362
Bobadilla-Suarez, S. & Love, B. C. (2018). Fast or Frugal, but not both: Decision Heuristics under Time Pressure. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/xlm0000419

2017

Parpart, P., Jones, M., & Love, B. C. (2017). Heuristics as Bayesian inference under extreme priors. Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2017.11.006
Guest, O., Kanayet, F.J., & Love, B. C. (2017). Gerrymandering and Computational Redistricting. arXiv. https://doi.org/1711.04640
De Martino, B., Bobadilla-Suarez, S., Nouguchi, T., Sharot, T., & Love, B. C. (2017). Social Information is Integrated into Value and Confidence Judgments According to its Reliability. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.3880-16.2017
Mack, M. L., Preston, A. R., & Love, B. C. (2017). Medial prefrontal cortex compresses concept representations through learning. Pattern Recognition in Neuroimaging. https://doi.org/10.1109/PRNI.2017.7981500
Spiers, H.J., Love, B. C., Le Pelley, M. E., Gibb, C. E., & Murphy, R. A. (2017). Anterior Temporal Lobe Tracks the Formation of Prejudice. Journal of Cognitive Neuroscience. https://doi.org/10.1162/jocn_a_01056
Riefer, P.S., Prior, R., Blair, N., Pavey, G., & Love, B. C. (2017). Coherency Maximizing Exploration in the Supermarket. Nature Human Behaviour. https://doi.org/10.1038/s41562-016-0017

2016

Palmeri, T. J., Love, B. C., & Turner, B. M. (2016). Model-based cognitive neuroscience. Journal of Mathematical Psychology. https://doi.org/10.1016/j.jmp.2016.10.010
Mack, M. L., Love, B. C., & Preston, A. R. (2016). Dynamic updating of hippocampal object representations reflects new conceptual knowledge. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.1614048113
Love, B. C. (2016). Will AI spell the end of humanity? The tech industry wants you to think so. The Register and The Conversation. More
Blanco, N. J., Love, B. C., Ramscar, M., Otto, A. R., Smayda, K., & Maddox, W. T. (2016). Exploratory Decision-Making as a Function of Lifelong Experience, not Cognitive Decline. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0000133
Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2016). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology. https://doi.org/10.1016/j.jmp.2016.01.001

2015

Love, B. C., Kopec, L., & Guest, O. (2015). Optimism Bias in Fans and Sports Reporters. PLOS ONE. https://doi.org/10.1371/journal.pone.0137685
Parpart, P., Schulz, E., Speekenbrink, M., & Love, B. C. (2015). Active learning as a means to distinguish among prominent decision strategies. Proceedings of the 37th Annual Meeting of the Cognitive Science Society.
Love, B. C. (2015). Gaming improves your brain power - reality or hype?. The Conversation.
Gureckis, T. M. & Love, B. C. (2015). Computational Reinforcement Learning. Oxford Handbook of Computational and Mathematical Psychology. https://doi.org/isbnsearch.org/isbn/9780199957996
Blanco, N. J., Love, B. C., Cooper, J.A., McGeary, J.E., Knopik, V.S., & Maddox, W. T. (2015). A Frontal Dopamine System for Reflective Exploratory Behavior. Neurobiology of Learning and Memory. https://doi.org/10.1016/j.nlm.2015.05.004

2014

Hornsby, A. N. & Love, B. C. (2014). Improved Classification of Mammograms Following Idealized Training. Journal of Applied Research in Memory and Cognition. https://doi.org/10.1016/j.jarmac.2014.04.009
Davis, T., Xue, G., Love, B. C., Preston, A. R., & Poldrack, R. A. (2014). Global Neural Pattern Similarity As A Common Basis For Categorization and Recognition Memory. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.3376-13.2014
Otto, A. R., Knox, W. B., Markman, A. B., & Love, B. C. (2014). Physiological and behavioral signatures of reflective exploratory choice. Cognitive, Affective, & Behavioral Neuroscience. https://doi.org/10.3758/s13415-014-0260-4
Ramscar, M., Hendrix, P., Love, B. C., & Baayen, H. (2014). Learning is not decline: The mental lexicon as a window into cognition across the lifespan. The Mental Lexicon. https://doi.org/10.1075/ml.8.3.08ram

2013

Love, B. C. (2013). Categorization. Oxford Handbook of Cognitive Neuroscience. https://doi.org/10.1093/oxfordhb/9780199988709.001.0001
Blanco, N. J., Otto, A. R., Maddox, W. T. Maddox, Beevers, C. G., & Love, B. C. (2013). The influence of depression symptoms on exploratory decision-making. Cognition. https://doi.org/10.1016/j.cognition.2013.08.018
Giguère, G. & Love, B. C. (2013). Limits in decision making arise from limits in memory retrieval. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.1219674110

2012

Knox, W. B., Glass, B. D., Love, B. C., Maddox, W. T., & Stone, P. (2012). How humans teach agents. International Journal of Social Robotics. https://doi.org/10.1007/s12369-012-0163-x
Davis, T., Love, B. C., & Preston, A. R. (2012). Striatal and Hippocampal Entropy and Recognition Signals in Category Learning: Simultaneous Processes Revealed by Model-based fMRI. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/a0027865
Knox, W. B., Otto, A. R., Stone, P., & Love, B. C. (2012). The Nature of Belief-Directed Exploratory Choice in Human Decision-Making. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2011.00398
Dixit, V. V., Alsup, R.M., Waller, S. T., Love, B. C., & Tomlinson, M. T. (2012). A Static Model for Predicting Disrupted Network Behaviour. Proceedings of the 17th International Conference of Hong Kong Society for Transportation Studies.
Love, B. C. & Jones, M. (2012). Bayesian Learning. Encyclopedia of the Sciences of Learning. https://doi.org/10.1007/978-1-4419-1428-6_255

2011

Otto, A. R., Markman, A. B., & Love, B. C. (2011). Taking More, Now: The Optimality of Impulsive Choice Hinges on Environment Structure. Social Psychological and Personality Science. https://doi.org/10.1177/1948550611411311

2010

Gureckis, T. M. & Love, B. C. (2010). Towards a unified account of supervised and unsupervised learning. Journal of Experimental and Theoretical Artifical Intelligence. https://doi.org/10.1080/09528130210166097
Otto, A. R., Markman, A. B., Gureckis,T. M., & Love, B. C. (2010). Regulatory Fit and Systematic Exploration in a Dynamic Decision-Making Environment. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/a0018999
Sloman, S. A., Love, B. C., & Ahn, W. K. (2010). Feature centrality and conceptual coherence. Cognitive Science. https://doi.org/10.1207/s15516709cog2202_2
Larkey, L. B. & Love, B. C. (2010). CAB: Connectionist analogy builder. Cognitive Science. https://doi.org/10.1207/s15516709cog2705_5
Sakamoto, Y. & Love, B. C. (2010). Learning and Retention through Predictive Inference and Classification. Journal of Experimental Psychology: Applied. https://doi.org/10.1037/a0021610
Hoffman, A. B., Love, B. C., & Markman, A. B. (2010). Selective Attention by Structural Alignment: An Eyetracking Study. Proceedings of the Annual Meeting of Cognitive Science Society.

2009

Otto, A. R., Markman, A. B., Love, B. C., & Gureckis,T. M. (2009). When Things Get Worse before They Get Better: Regulatory Fit and Average- Reward Learning in a Dynamic Decision-Making Environment. Proceedings of the Annual Meeting of Cognitive Science Society.
Gureckis, T. M. & Love, B. C. (2009). Learning in Noise: Dynamic Decision-Making in a Variable Environment. Journal of Mathematical Psychology. https://doi.org/10.1016/j.jmp.2009.02.004
Love, B. C., Jones, M., Tomlinson, M. T., & Howe, M. (2009). Predicting Information Needs: Adaptive Display in Dynamic Environments. Proceedings of the Cognitive Science Society.
Love, B. C., Jones, M., Tomlinson, M. T., & Howe, M. (2009). Learning to Predict Information Needs: Context-Aware Display as a Cognitive Aid and an Assessment Tool. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.

2008

Davis, T. and Love, B. C., & Maddox, W. T. (2008). Two Pathways to Stimulus Encoding in Category Learning?. Memory & Cognition. https://doi.org/10.3758/MC.37.4.394
Davis, T. & Love, B. C. (2008). How Goals Shape Category Acquisition: The Role of Contrasting Categories. Proceedings of the Cognitive Science Society.
Love, B. C., Tomlinson, M., & Gureckis, T. M. (2008). The concrete substrates of abstract rule use. The Psychology of Learning and Motivation. https://doi.org/10.1016/S0079-7421(08)00005-4
Maddox, W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is less: Feedback effects in perceptual category learning. Cognition. https://doi.org/10.1016/j.cognition.2008.03.010
Tomlinson, M. T. & Love, B. C. (2008). Monkey See, Monkey Do: Learning Relations through Concrete Examples. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X08003762

2007

Gureckis, T. M. & Love, B. C. (2007). Behaviorism Reborn? Statistical Learning as Simple Conditioning. Procedings of the Twenty-Ninth National Conference on Artificial Intelligence.
Davis, T., Love, B. C., & Maddox, W. T. (2007). Translating From Perceptual to Cognitive Coding. Procedings of the Twenty-Ninth National Conference on Artificial Intelligence.
Tomlinson, M. & Love, B. C. (2007). Relation-Based Categories Are Easier to Learn than Feature-Based Categories. Procedings of the Twenty-Ninth National Conference on Artificial Intelligence.
Rein, J. R., Love, B. C., & Markman, A. B. (2007). Feature Relations and Feature Salience in Natural Categories. Procedings of the Twenty-Ninth National Conference on Artificial Intelligence.

2006

Love, B. C. & Gureckis, T. M. (2006). Models in Search of a Brain. Cognitive, affective & behavioral neuroscience. https://doi.org/10.3758/CABN.7.2.90
Gureckis,T. M. & Love, B. C. (2006). Bridging Levels: Using a Cognitive Model to Connect Brain and Behavior in Category Learning. Procedings of the Twenty-Eighth National Conference on Artificial Intelligence.
Sakamoto, Y. & Love, B. C. (2006). Sizable Sharks Swim Swiftly: Learning Correlations through Inference in a Classroom Setting. Procedings of the Twenty-Eighth National Conference on Artificial Intelligence.
Gureckis, T. M. & Love, B. C. (2006). Tracking Variability in Learning: Contrasting Statistical and Similarity-Based Accounts. Procedings of the Twenty-Eighth National Conference on Artificial Intelligence.
Davis, T., Love, B. C., & Maddox, W. T. (2006). The Role of Similarity in Generalization. Procedings of the Twenty-Eighth National Conference on Artificial Intelligence.
Tomlinson, M. T. & Love, B. C. (2006). Learning Abstract Relations Through Analogy to Concrete Exemplars. Procedings of the Twenty-Eighth National Conference on Artificial Intelligence.
Tomlinson, M. T. & Love, B. C. (2006). From Pigeons to Humans: Grounding Relational Learning in Concrete Examples. Proceding of the Twenty-First AAAI Conference on Artificial Inteligence.
Jones, M., Love, B. C., & Maddox, W. T. (2006). Recency Effects as a Window to Generalization: Separating Decisional and Perceptual Sequential Effects in Category Learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/0278-7393.32.3.316

2005

2004

Sakamoto, Y. & Love, B. C. (2004). Schematic Influences on Category Learning and Recognition Memory. Journal of Experimental Psychology: General. https://doi.org/www.ncbi.nlm.nih.gov/pubmed/15584805
Sakamoto, Y., Matuska, T., & Love, B. C. (2004). Dimension-wide vs. exemplar-specific attention in category learning and recognition. Proceedings of the International Conference of Cognitive Modeling.
Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review. https://doi.org/dx.10.1037/0033-295X.111.2.309
Jones, M. & Love, B. C. (2004). Beyond common features: The role of roles in determining similarity. Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
Sakamoto, Y. & Love, B. C. (2004). Type/token information in category learning and recognition. Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.
Love, B. C. & Gureckis, T. M. (2004). The hippocampus: Where a cognitive model meets cognitive neuroscience. Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.

2003

Love, B. C. (2003). Concept learning. The Encyclopedia of Cognitive Science. https://doi.org/10.1002/0470018860.s00499
Gureckis, T. M. & Love, B. C. (2003). Human unsupervised and supervised learning as a quantitative distinction. nternational Journal of Pattern Recognition and Artificial Intelligence. https://doi.org/10.1142/S0218001403002587
Sakamoto, Y. & Love, B. C. (2003). Category structure and recognition memory. Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

2002

Gureckis, T. M. & Love, B. C. (2002). Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions. Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.
Yamauchi, T., Love, B. C., & Markman, A. B. (2002). Learning Nonlinearly Separable Categories by Inference and Classification. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10.1037/0278-7393.28.3.585
Love, B. C., Rouder, J. N., & Wisniewski, E. J. (2002). A structural account of global and local processing. Cognitive Psychology. https://doi.org/10.1006/cogp.1998.0697
Gureckis, T. M. & Love, B. C. (2002). Modeling unsupervised learning with SUSTAIN. Proceedings of the 15th international Florida artificial intelligence research society conference.

2001

Love, B. C. (2001). Uncovering analogy. Trends in Cognitive Sciences. https://doi.org/10.1016/S1364-6613(00)01747-2

2000

Love, B. C. (2000). A computational level theory of similarity. Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society.
Love, B. C. (2000). Learning at different levels of abstraction. Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society.
Love, B. C., Markman, A. B., & Yamauchi, T. (2000). Modeling classification and inference learning. Seventeenth National Conference on Artificial Intelligence (AAAI-2000).

1999

Love, B. C. (1999). Utilizing time: Asynchronous Binding. Advances in Neural Information Processing Systems 11.

1998

Love, B. C. & Medin, D. L. (1998). Modeling item and category learning. Proceedings of the Twentieth Annual Conference of the Cognitive Science Society.
Love, B. C. & Medin, D. L. (1998). SUSTAIN: A model of human category learning. Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98).
Wisniewski, E. J. & Love, B. C. (1998). Relations versus properties in conceptual combination. Journal of Memory and Language. https://doi.org/10.1006/jmla.1997.2550

1996

Love, B. C. (1996). Mutability, conceptual transformation, and context. Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society.

1995

Love, B. C. & Sloman, S. A. (1995). Mutability and the determinants of conceptual transformability. Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society.

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