Love Lab

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

Funded by

Tweets

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.

Postdocs

Daniel Barry
Daniel Barry

My research interests encompass the neural bases of human memory and imagination. To gain a better understanding of these processes, I combine functional MRI and MEG methods, with real-world behaviours. I am currently incorporating neuroscientific findings into the design of artificial neural networks, with a view to enhancing their performance and making them more human-like in their operation.

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 attention and similarity models for electro-physiological data. Other efforts include linking decision heuristics to principled statistical models and analysis of fMRI data related to subjective value and confidence.

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

Kaarina Aho
Kaarina Aho

As a student of the Ecological Brain DTP, I am interested in how humans acquire concepts in a largely unsupervised manner amidst the noise and complexity of the real world. Specifically, my research explores integration across conceptual spaces as a model for human concept formation. Using machine learning methods applied to multimodal naturalistic data, I aim to investigate how humans learn about the world outside of the lab.

Franziska Bröker
Franziska Bröker

My research focuses on semi-supervised learning, incorporating behavioural and computational approaches. In my current project I aim to better understand the role of feedback in category learning and how this is tied to mental representations. More generally, I am interested in building models that can capture learning in naturalistic setups in order to predict optimal learning conditions. I am a PhD student with the Gatsby Unit at UCL, but can currently be found at the MPI for Biological Cybernetics where I am supervised by Peter Dayan.

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

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. 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
Yáñez, F., Luo, X.,Minero, O.V., & Love, B. C. (2024). Confidence-weighted integration of human and machine judgments for superior decision-making. arXiv. https://doi.org/2408.08083
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
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

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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