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Bradley C. Love
Love, B.C. (2017)
URL: https://osf.io/skg2y/
Mack, M.L., Preston, A., Love, B.C. (2016)
URL: https://osf.io/62rgs/
Bobadilla-Suarez, S., & Love, B.C. (2015)
URL: https://osf.io/xnbz4/
Bradley C. Love
Welcome
I am a senior research scientist at Los Alamos National Laboratory. I was a professor at University of Texas at Austin and at University College London, as well as a fellow at The Alan Turing Institute for data science and AI and the European Lab for Learning & Intelligent Systems (ELLIS). I have made contributions in cognitive science and computational neuroscience. After embarking on the BrainGPT project, I became very interesting in extending modern AI techniques, such as large language models, to build systems that can accelerate scientific discovery.
Publications
2025 & in press
Yáñez, F., Luo, X.,Minero, O.V., & Love, B. C.
(2025).
Confidence-weighted integration of human and machine judgments for superior decision-making.
Patterns (in press).
https://doi.org/2408.08083
Ilia Sucholutsky, **lots of people**, Thomas L Griffiths
(2025).
Getting aligned on representational alignment.
TMLR.
https://doi.org/2310.13018
Xin-Ya Zhang, Sebastian Bobadilla-Suarez, Xiaoliang Luo, Marilena Lemonari, Scott L. Brincat, Markus Siegel, Earl K. Miller, Bradley C. Love
(2025).
Adaptive stretching of representations across brain regions and deep learning model layers.
Nature Communications (in press).
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
(2025).
Overview of leakage scenarios in supervised machine learning.
Journal of Big Data.
https://doi.org/10.1186/s40537-025-01193-8
Luo, X.,Ramscar, M, & Love, B. C.
(2025).
Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies.
arXiv.
https://doi.org/10.48550/arXiv.2505.08739
Luo, X., Mok, R.M., Roads, B.D., & Love, B. C.
(2025).
Coordinating multiple mental faculties during learning.
Scientific Reports.
https://doi.org/10.1038/s41598-025-89732-4
2024
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.
Nature Human Behaviour.
https://doi.org/10.1038/s41562-024-02046-9
Luo, X.,Ramscar, M, & Love, B. C.
(2024).
Beyond Human-Like Processing: Large Language Models Perform Equivalently on Forward and Backward Scientific Text.
arXiv.
https://doi.org/2411.11061
Bröker, F., Holt, L. L., Roads, B. D., Dayan, P., & Love, B. C.
(2024).
Demystifying unsupervised learning: how it helps and hurts.
Trends in Cognitive Sciences.
https://doi.org/10.1016/j.tics.2024.09.005
S Della Sala, J Bathelt, H Buchtel, A Tavano, C Press, B Love, I Croy, R Morris, S Kotz, M D Kopelman, M I Coco, P Reber, S J Forkel, S R Schweinberger
(2024).
The future of science publishing.
Cortex.
https://doi.org/10.1016/j.cortex.2024.10.005
Luo, X., Mok, R. M., & Love, B. C.
(2024).
The inevitability and superfluousness of cell types in spatial cognition.
eLife.
https://doi.org/10.7554/eLife.99047.1
Roads, B. D.., & Love, B. C.
(2024).
The Dimensions of Dimensionality.
Trends in Cognitive Science.
https://doi.org/www.sciencedirect.com/science/article/pii/S136466132400189X
Luo, X., Sun, G., & Love, B. C.
(2024).
Matching domain experts by training from scratch on domain knowledge.
arXiv.
https://doi.org/2405.09395
Broschard, M.B., Kim, J., Love, B.C., Halverson, H.E., & Freeman, J.H.
(2024).
Disrupting dorsal hippocampus impairs category learning in rats.
Neurobiology of Learning and Memory.
https://doi.org/10.1016/j.nlm.2024.107941
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
Love, B. C.
(2024).
Linking Models with Brain Measures.
An Introduction to Model-Based Cognitive Neuroscience.
https://doi.org/link.springer.com/chapter/10.1007/978-3-031-45271-0_2
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
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
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.
https://doi.org/10.1093/gigascience/giad071
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
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
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).
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
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
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.
Press
Selected mainstream media articles on the Love Lab’s work:
- How neuroscientists are using AI (The Transmitter)
- Can Large Language Models Eliminate Biases in Health Care? (Neurology Today)
- AI beats neuroscientists’ predictions (Nature Neuroscience)
- Podcast with Nicky Cartridge on how AI will impact science and healthcare
- Some comments to The Guardian on how puzzles affect or don’t affect cognition, focused on aging.
- Sabine Hossenfelder on BrainGPT paper.
- Index to BrainGPT press on first paper.
- Dan Shipper’s AI and I Podcast (10-7-2024).
- Love & Philosophy, Beyond Dichotomy Podcast (24-5-2024).
- Foresight Neurotech program (28-8-2024).
- 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 concepts.
- 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
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).
Decoding the brain's algorithm for categorization from its neural implementation
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
Data from: Bobadilla-Suarez, S. & Love, B. C. (2018) Fast or Frugal, but not both: Decision Heuristics under Time Pressure.
Tweets
Funded by
