Bradley C. Love

Bradley C. Love
University College London
Experimental Psychology
26 Bedford Way, Room 314
WC1H 0AP
London, UK

Funded by

Welcome

I am Professor of Cognitive and Decision Sciences in Experimental Psychology at UCL and a fellow at The Alan Turing Institute for data science and AI, as well as the European Lab for Learning & Intelligent Systems (ELLIS). My lab's research centers around human learning and decision making, integrating behavioural, computational, and neuroscience perspectives. Currently, we are focused on large-scale modelling of brain and behaviour using deep learning approaches, as well as using large language models to create BrainGPT, a tool to assist neuroscience researchers.

Publications

2024 & in press

Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N Apurva Ratan Murty, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love (2024). Large language models surpass human experts in predicting neuroscience results. arXiv. https://doi.org/2403.03230

Russell A Poldrack, **lots of people** & Krzysztof J Gorgolewski (2024). The Past, Present, and Future of the Brain Imaging Data Structure (BIDS). Imaging Neuroscience. https://doi.org/10.1162/imag_a_00103

Mack, M. L., Love, B. C., & Preston, A. R. (2024). Distinct hippocampal mechanisms support concept formation and updating. bioRxiv. https://doi.org/www.biorxiv.org/content/10.1101/2024.02.14.580181v1

Luo, X., Mok, R. M., & Love, B. C. (2024). The inevitability and superfluousness of cell types in spatial cognition. bioRxiv. https://doi.org/10.1101/2024.01.10.575026

Love, B. C. (2024). Linking Models with Brain Measures. An Introduction to Model-Based Cognitive Neuroscience. https://doi.org/www.amazon.co.uk/Introduction-Model-Based-Cognitive-Neuroscience-dp-3031452704/dp/3031452704/ref=dp_ob_title_bk

2023

Love, B.C., & Mok, R.M. (2023). You can't play 20 questions with nature and win redux. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X23001747

Xin-Ya Zhang, Sebastian Bobadilla-Suarez, Xiaoliang Luo, Marilena Lemonari, Scott L. Brincat, Markus Siegel, Earl K. Miller, Bradley C. Love (2023). Adaptive stretching of representations across brain regions and deep learning model layers. bioRxiv. https://doi.org/10.1101/2023.12.01.569615

Leonard Sasse, Eliana Nicolaisen-Sobesky, Juergen Dukart, Simon B Eickhoff, Michael Götz, Sami Hamdan, Vera Komeyer, Abhijit Kulkarni, Juha Lahnakoski, Bradley C Love, Federico Raimondo, & Kaustubh R Patil (2023). On Leakage in Machine Learning Pipelines. arXiv. https://doi.org/2311.04179

Ilia Sucholutsky, **lots of people**, Thomas L Griffiths (2023). Getting aligned on representational alignment. arXiv. https://doi.org/2310.13018

Aho, K., Roads, B.D., & Love, B. C. (2023). Signatures of cross-modal alignment in children's early concepts. PNAS. https://doi.org/10.1073/pnas.2309688120

Roads, B. D., & Love, B.C. (2023). Modeling Similarity and Psychological Space. Annual Review of Psychology. https://doi.org/10.1146/annurev-psych-040323-115131

Mok, R. & Love, B. C. (2023). A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies. Science Advances. https://doi.org/10.1126/sciadv.ade6903

Sami Hamdan, Bradley C. Love, Georg G. von Polier, Susanne Weis, Holger Schwender, Simon B. Eickhoff, Kaustubh R. Patil (2023). Confound-leakage: Confound Removal in Machine Learning Leads to Leakage. GigaScience (in press). https://doi.org/2210.09232

Nanda, V., Majumdar, A., Kolling, C., Dickerson, J. P., Gummadi, K. P., Love, B. C., & Weller, A. (2023). Do Invariances in Deep Neural Networks Align with Human Perception?. AAAI. https://doi.org/ojs.aaai.org/index.php/AAAI/article/view/26112

Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley C. Love, Adrian Weller (2023). Human-in-the-Loop Mixup. Uncertainty in Artificial Intelligence (UAI). https://doi.org/openreview.net/forum?id=BW6oQ0qZl0El

Broschard, M., Kim, J. Love, B.C., & Freeman, J. (2023). Dorsomedial striatum, but not dorsolateral striatum, is necessary for rat category learning. Neurobiology of Learning and Memory. https://doi.org/www.sciencedirect.com/science/journal/10747427

Luo, X., Mok, R.M., Roads, B.D., & Love, B. C. (2023). A controller-peripheral architecture and costly energy principle for learning. bioRxiv. https://doi.org/10.1101/2023.01.16.524194

2022

Dagaev, N., Roads, B. D., Luo, X., Barry, D.N., Patil, K.R. & Love, B. C. (2022). A Too-Good-to-be-True Prior to Reduce Shortcut Reliance. Pattern Recognition Letters. https://doi.org/www.sciencedirect.com/science/article/abs/pii/S0167865522003841

Aho, K., Roads, B. D. & Love, B. C. (2022). System alignment supports cross-domain learning and zero-shot generalisation. Cognition. https://doi.org/10.1016/j.cognition.2022.105200

Sexton, N. J. & Love, B. C. (2022). Reassessing hierarchical correspondences between brain and deep networks through direct interface. Reassessing hierarchical correspondences between brain and deep networks through direct interface. Science Advances. https://doi.org/www.science.org/doi/10.1126/sciadv.abm2219

Broker, F., Love, B. C. & Dayan, P. (2022). When unsupervised training benefits category learning. Cognition. https://doi.org/10.1016/j.cognition.2021.104984

Barry, D. N. & Love, B. C. (2022). A neural network account of memory replay and knowledge consolidation. Cerebral Cortex. https://doi.org/10.1093/cercor/bhac054

Bobadilla-Suarez, S., Jones, M. & Love, B. C. (2022). Robust priors for regularized regression. Cognitive Psychology. https://doi.org/10.1016/j.cogpsych.2021.101444

Hornsby, A. N. & Love, B. C. (2022). Sequential consumer choice as multi-cued retrieval. Science Advances. https://doi.org/10.1126/sciadv.abl9754

2021

Barry, D. N. & Love, B. C. (2021). Human learning follows the dynamics of gradient descent. PsyArXiv. https://doi.org/psyarxiv.com/75e4t/

Luo, X., Sexton, N. J. & Love, B. C. (2021). A Deep Learning Account of How Language Affects Thought. Language Cognition & Neuroscience. https://doi.org/www.tandfonline.com/doi/full/10.1080/23273798.2021.2001023

Broschard, M.B., Kim, J., Love, B.C., Wasserman, E.A., & Freeman, J.H. (2021). Prelimbic cortex maintains attention to category-relevant information and flexibly updates category representations. Neurobiology of Learning and Memory. https://doi.org/10.1016/j.nlm.2021.107524

Roads, B. D. & Love, B. C. (2021). Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Braunlich, K. & Love, B. C. (2021). Bidirectional Influences of Information-Sampling and Concept Learning. Psychological Review. https://doi.org/10.1037/rev0000287

Luo, X., Roads, B. D., & Love, B. C. (2021). The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks. Computational Brain & Behavior. https://doi.org/link.springer.com/article/10.1007%2Fs42113-021-00098-y

2020

Love, B. C. & Roads, B. D. (2020). Similarity as a window on the dimensions of object representation. Trends in Cognitive Science (spotlight). https://doi.org/www.sciencedirect.com/science/article/abs/pii/S1364661320303004?dgcid=author

Love, B. C. (2020). Linking Models with Brain Measures. OSF Preprints. https://doi.org/osf.io/s92zy/

Mok, R. & Love, B. C. (2020). Abstract neural representations of category membership beyond information coding stimulus or response. Journal of Cognitive Neuroscience. https://doi.org/10.1162/jocn_a_01651

Bobadilla-Suarez, S., Guest, O. & Love, B. C. (2020). Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain. Communications Biology. https://doi.org/10.1038/s42003-020-01315-3

Bobadilla-Suarez, S., Ahlheim, C., Mehrotra, A., Panos, A., & Love, B. C. (2020). Measures of neural similarity. Computational Brain & Behavior. https://doi.org/10.1007/s42113-019-00068-5

Love, B. C. (2020). Levels of Biological Plausibility. Philosophical Transactions of the Royal Society B. https://doi.org/10.1098/rstb.2019.0632

Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., . . . Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature. https://doi.org/10.1038/s41586-020-2314-9

Broschard, M., Kim, J. Love, B.C., & Freeman, J. (2020). Category learning in rodents using touchscreen-based tasks. Genes, Brain and Behavior. https://doi.org/10.1111/gbb.12665

Love, B. C. (2020). Model-based fMRI Analysis of Memory. Current Opinion in Behavioral Sciences. https://doi.org/10.1016/j.cobeha.2020.02.012

Hornsby, A. N. & Love, B. C. (2020). How Decisions and the Desire for Coherency Shape Subjective Preferences Over Time. Cognition. https://doi.org/10.1016/j.cognition.2020.104244

Roads, B. D., & Love., B. C. (2020). Learning as the unsupervised alignment of conceptual systems. Nature Machine Intelligence. https://doi.org/10.1038/s42256-019-0132-2

Mack, M. L., Preston, A. R., & Love, B. C. (2020). Ventromedial prefrontal cortex compression during concept learning. Nature Communications. https://doi.org/10.1038/s41467-019-13930-8

2019

Mok, R. & Love, B. C. (2019). A non-spatial account of place and grid cells based on clustering models of concept learning. Nature Communications. https://doi.org/10.1038/s41467-019-13760-8

Poldrack, R., .... (2019). The importance of standards for sharing of computational models and data. Computational Brain & Behavior volume. https://doi.org/link.springer.com/article/10.1007/s42113-019-00062-x

Hornsby, A. N., Evans, T., Riefer, P. S., Prior, R., & Love, B. C. (2019). Conceptual Organization is Revealed by Consumer Activity Patterns. Computational Brain & Behavior. https://doi.org/10.1007/s42113-019-00064-9

Guest, O., Kanayet, F.J., & Love, B. C. (2019). Gerrymandering and computational redistricting. Journal of Computational Social Science. https://doi.org/10.1007/s42001-019-00053-9

Schulz, E., Bhui, R., Love, B.C., Brier, B., Todd, M.T., Gershman, S.J. (2019). Structured, uncertainty-driven exploration in real-world consumer choice. Proceedings of the National Academy of Sciences (PNAS). https://doi.org/10.1073/pnas.1821028116

Guest, O. & Love, B. C. (2019). Levels of Representation in a Deep Learning Model of Categorization. bioRxiv. https://doi.org/10.1101/626374

Love, B. C. (2019). Do supermarkets know more about us than we do?. BBC Business.

Love, B. C. (2019). Model comparison, not model falsification. Behavioral and Brain Sciences. (commentary on Rahnev & Denison). https://doi.org/10.1017/S0140525X18001516

2018

Broschard, M., Kim, J., Love, B.C., Wasserman, E.A., Freeman, J.H. (2018). Selective attention in rat visual category learning. Learning & Memory. https://doi.org/10.1101/lm.048942.118

Inhoff, M.C., Libby, L.A., Noguchi, T., Love, B. C., & Ranganath C. (2018). Dynamic integration of conceptual information during learning. PLOS ONE. https://doi.org/10.1371/journal.pone.0207357

Braunlich, K. & Love, B. C. (2018). Occipitotemporal Representations Reflect Individual Differences in Conceptual Knowledge. Journal of Experimental Psychology: General. https://doi.org/psycnet.apa.org/doi/10.1037/xge0000501

Ahlheim, C. & Love, B. C. (2018). Estimating the functional dimensionality of neural representations. NeuroImage. https://doi.org/10.1016/j.neuroimage.2018.06.015

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

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

Mack, M. L., Love, B. C., & Preston, A. R. (2017). Building concepts one episode at a time: The hippocampus and concept formation. Neuroscience Letters. https://doi.org/10.1016/j.neulet.2017.07.061

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

Guest, O. & Love, B. C. (2017). What the Success of Brain Imaging Implies about the Neural Code. eLife. https://doi.org/10.7554/eLife.21397

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

Love, B. C. (2016). Cognitive Models as Bridge between Brain and Behavior. Trends in Cognitive Science. https://doi.org/10.1016/j.tics.2016.02.006

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.

Riefer, P.S. & Love, B. C. (2015). Unfazed by Both the Bull and Bear: Strategic Exploration in Dynamic Environments. Games. https://doi.org/10.3390/g6030251

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

Love, B. C. (2015). The Algorithmic Level Is the Bridge Between Computation and Brain. Topics in Cognitive Science. https://doi.org/10.1111/tops.12131

Newall, P. & Love, B. C. (2015). Nudging Investors Big and Small Toward Better Decisions. Decision. https://doi.org/10.1037/dec0000036

Love, B. C. (2015). Concepts, Meaning, and Conceptual Relationships. The Oxford Handbook of Cognitive Science. https://doi.org/10.1093/oxfordhb/9780199842193.013.12

2014

Patil, K. R., Zhu, J., Kopeć, Ł., & Love, B. C. (2014). Optimal Teaching for Limited-Capacity Human Learners. Advances in Neural Information Processing Systems 27.

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

Mack, M. L., Preston, A. R., & Love, B. C. (2013). Decoding the brain's algorithm for categorization from its nueral implementation. Current Biology. https://doi.org/10.1016/j.cub.2013.08.035

Glass, B. D., Maddox, W. T., & Love, B. C. (2013). Real-time strategy game training: Emergence of a cognitive flexibility trait. PLOS ONE. https://doi.org/10.1371/journal.pone.0070350

Love, B.C (2013). Grounding quantum probability in psychological mechanism. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X12003147

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

Love, B. C. (2013). Category Learning, Computational Perspectives. Encyclopedia of the Mind. https://doi.org/dx.10.4135/9781452257044

2012

Sanders, M., Davis, T., & Love, B. C. (2012). Are Better Examples Beautiful or Are Beautiful Examples Better? Exploring the Relationship Between Beauty and Category Structure. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-012-0356-1

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

Davis, T., Love, B. C., & Maddox, W. T. (2012). Age-related Declines in the Fidelity of Newly Acquired Category Representations. Learning & Memory. https://doi.org/10.1101/lm.024919.111

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

Jones, M. & Love, B. C. (2011). Pinning Down the Theoretical Commitments of Bayesian Cognitive Models. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X11001439

Jones, M. & Love, B. C. (2011). Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X10003134

Davis, T., Love, B. C., & Preston, A. R. (2011). Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members. Cerebral Cortex. https://doi.org/10.1093/cercor/bhr036

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

Goldwater, M. B., Tomlinson, M. T., Echols, C.H., & Love, B. C. (2011). Structural Priming as Structure-Mapping: ChildrenUse Analogies From Previous Utterances to GuideSentence Production. Cognitive Science. https://doi.org/10.1111/j.1551-6709.2010.01150.x

2010

Tomlinson, M. T. & Love, B. C. (2010). When Learning to Classify by Relations Is Easier than by Features. Thinking and Reasoning. https://doi.org/10.1080/13546783.2010.530464

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

Love, B. C. & Tomlinson, M. T. (2010). Mechanistic Models of Associative and Rule-Based Category Learning. The Making of Human Concepts. https://doi.org/10.1093/acprof:oso/9780199549221.003.04

Otto, A. R. and Love, B. C. (2010). You Don't Want To Know What You're Missing: When Information about Forgone Rewards Impedes Dynamic Decision Making. Judgement and Decision Making. https://doi.org/10.1017/S1930297500001984

Gureckis, T. M. & Love, B. C. (2010). Common mechanisms in infant and adult category learning. Infancy. https://doi.org/10.1207/s15327078in0502_4

Gureckis, T. M. and Love, B. C. (2010). Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior. Cognitive Science. https://doi.org/10.1111/j.1551-6709.2009.01076.x

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

Davis, T. & Love, B. C. (2009). Memory for Category Information Is Idealized through Contrast with Competing Options. Psychological Science. https://doi.org/10.1177/0956797609357712

Gureckis, T. M. & Love, B. C. (2009). Short-Term Gains, Long-Term Pains: How Cues about State Aid Learning in Dynamic Environments. Cognition. https://doi.org/10.1016/j.cognition.2009.03.013

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.

Davis, T., Love, B. C., & Maddox, W. T. (2009). Anticipatory Emotions in Decision Tasks: Covert Markers of Value or Attentional Processes?. Cognition. https://doi.org/10.1016/j.cognition.2009.04.002

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

Otto, A. R., Gureckis,T. M., Markman, A. B., & Love, B. C. (2009). Navigating through Abstract Decision Spaces: Evaluating the Role of State Generalization in a Dynamic Decision-Making Task. Psychonomic Bulletin & Review. https://doi.org/10.3758/PBR.16.5.957

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

Jones, M., & Love, B. C. (2008). Beyond common features: The role of roles in determining similarity. Cognitive Psychology. https://doi.org/dx.10.1016%2Fj.cogpsych.2006.09.004

Love, B. C. (2008). Prediction Markets are only Human: Subadditivity in Probability Judgments. Journal of Me. https://doi.org/bradlove.org/papers/election.pdf

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

Sakamoto, Y., Jones, M., & Love, B. C. (2008). Putting the Psychology Back into Psychological Models: Mechanistic versus Rational Approaches. Memory & Cognition. https://doi.org/10.3758/MC.36.6.1057

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.

Sakamoto, Y. & Love, B. C. (2006). Vancouver, Toronto, Montreal, Austin: Enhanced Oddball Memory through Differentiation, Not Isolation. Psychonomic Bulletin & Review. https://doi.org/link.springer.com/article/10.3758/BF03193872

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

Love, B. C. & Jones, M (2006). The Emergence of Multiple Learning Systems. Proceedings of the Annual Conference of the Cognitive Science Society.

2005

Love, B. C. (2005). Environment and Goals Jointly Direct Category Acquisition. Current Directions in Psychological Science. https://doi.org/10.1111/j.0963-7214.2005.00363.x

Love, B. C. & Gureckis, T. M. (2005). Modeling Learning Under the Influence of Culture. Categorization inside and outside the laboratory: Essays in honor of Douglas L. Medin. https://doi.org/10.1037/11156-013

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

Larkey, L. B. & Love, B. C. (2003). CAB: Connectionist analogy builder. Cognitive Science. https://doi.org/10.1207/s15516709cog2705_5

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

Love, B. C. & Markman, A. B. (2003). The nonindependence of stimulus properties in human category learning. Memory & Cognition. https://doi.org/10.3758/BF03196117

Love, B. C. (2003). The multifaceted nature of unsupervised category learning. Psychonomic Bulletin & Review. https://doi.org/10.3758/BF03196484

Sakamoto, Y. & Love, B. C. (2003). Category structure and recognition memory. Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

2002

Love, B. C. (2002). Comparing supervised and unsupervised category learning. Psychonomic Bulletin & Review. https://doi.org/10.3758/BF03196342

Love, B. C. (2002). Three deadly sins of category learning modelers. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X01510080

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.

Love, B. C. (2002). Similarity and Categorization: A review. AI Magazine. https://doi.org/www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1647/1545

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

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.

Love, B. C., Rouder, J. N., & Wisniewski, E. J. (1999). A structural account of global and local processing. Cognitive Psychology. https://doi.org/10.1006/cogp.1998.0697

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).

Sloman, S. A., Love, B. C., & Ahn, W. K. (1998). Feature centrality and conceptual coherence. Cognitive Science. https://doi.org/10.1207/s15516709cog2202_2

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

The Economist, “Artificial brains are helping scientists study the real thing” (24-5-2023).
Knowing Neurons Podcast (12-12-2022).
BBC’s from Nudge to Sludge.
Brain Inspired Podcast, How we learn comcepts.
Cognitive Revolution Podcast, Lifelong Exploration.
Changing Minds Webinar, Big data, smart analyses.
Royal Instituiton event on generalist AI.
BBC frontpage article on consumer decision making, here, March 2019.
British Neuroscience (BNA) Bulletin interview, Summer 2018.
10 Years of Nudge: Radio 4 .
AI hype: The Register.
Regulating AI, statement from Turing to House of Lords: Turing blog.
Psychology and Data Science: Turing blog.
W.E.I.R.D., typical experiment populations in Psychology: Aeon.
Electorial Gerrymandering: Seeker .
How we integrate others’ opinion to determine value of products: Daily Mail .
Human and Artificial Intelligence: Guardian science podcast.
How the brain represents concepts: Brains and Behaviour on Medium.
Explore/Exploit in the Supermarket: Nature Podcast, Daily Mail, Science Daily, Nature News and Views, Neuroscience News, Impact.
Data Science/Marketing: The Guardian.
Science of Persuasion: Daily Mail.
How brain acquires stereotypes: The Guardian.
How attention shapes category representations in brain: ScienceDaily.
Reinforcement Learning: BusinessWeek, ScienceDaily, Fox7 News (video).
Category Learning: UT Public Affairs, Psychology Today.
Improving Prediction/Cleaning Memory: Daily Mail, ScienceDaily, CBC News (audio), ABC (Spain), Veja (Brazil), ScienceNet (China).
Video Gaming and Cognitive Ability: Huffington Post, NBC Nightly News (Video), Psychology Today, Slashdot, CNET Australia, PCR, ScienceDaily, CNET, and more… Visit PlayIQ.com to evaluate yourself!
Sky News (23-8-2013) “Is the internet destroying our brains?” Answer: Not exactly. Video snippet from Swipe segment.
Brain Basis of Category Learning: ScienceDaily, Neuroscience News.
Developing Brain and Gaming: R7 Brazil (Video with Portuguese audio).
Optimism Bias: MinnPost, Newsweek, PHYS ORG, C.S. Monitor.

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

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:

Davis, T., Love, B.C., & Preston, A.R. (2012). Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members. Cerebral Cortex.
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.
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).

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