CURRICULUM VITAE
BRADLEY C. LOVE
(October, 2023)
Contact Information
Post University College
London
Experimental
Psychology
26 Bedford
Way, Room 314
London, UK
WC1H 0AP
E-mail b.love@ucl.ac.uk
Website http://bradlove.org
Citizenship UK and US
EDUCATION
Ph.D. in
Cognitive Psychology
Northwestern
University, Evanston, IL
B.S.
Cognitive and Linguistic Sciences
Brown University,
Providence, RI
POSITIONS
2020-2021 Programme Leader in Human-Machine
Teams at the Alan Turing Institute
2016 - Inaugural Turing Fellow
at the Alan Turing Institute
2011
- Professor of
Cognitive and Decision Sciences at University College London (UCL)
2010 - 2011 Full
Professor in Psychology
The
University of Texas at Austin
2005 –
2010 Associate Professor in Psychology
The
University of Texas at Austin
1999 –
2005 Assistant
Professor in Psychology
The
University of Texas at Austin
GRANTS, FELLOWSHIPS, AND HONORS
9/2023 Microsoft’s
Accelerating Foundation Models Research program, “BrainGPT: An open-source tool
to accelerate neuroscience research using Llama2 and LoRA”.
8/2022 ESRC Grant, “Next
Generation Psychological Embeddings”.
8/2020 Best paper award, Computational Brain & Behavior for
Hornsby et al. (2020).
9/2019 ELLIS.eu Fellow in the
Natural Intelligence Programme
5/2019 Royal Society Wolfson
Fellowship, “Integrating Embedding Spaces”.
10/2018 Turing Institute flagship
project with Intel.
7/2018 ESRC fellowship funding
for a PhD studentship in AI.
6/2016 National Institute of
Health P01 (linked R01s), “Linking Brain, Behavior, and Development: Integrative
Models of Category Learning.”
3/2016 Inaugural Fellow at the
Alan Turing Institute for data science.
6/2015 Wellcome Trust Senior
Investigator Award, “Neural and Computational Mechanisms of Categorisation.”
12/2014 Fellow, APS.
6/2014 The Leverhulme Trust, “Circumventing
Limits in Memory Retrieval.”
5/2014 Membership (elected) to
the Memory Disorders Research Society.
9/2012 IMPACT fellowship award
(dunnhumby corporation and UCL).
8/2012 Wellcome Trust New
Start Equipment Award.
1/2012 Fellow, Psychonomic Society.
5/2011 NIH proposal R21
MH091523-01A1 “Model-Based fMRI of Dynamic Category Learning: The Memory and
Attention Interface.”
5/2010 AFOSR Gant #FA9550-10-1-0268,
“A Dynamic Approach to Information Sampling and Learning.”
9/2009 NSF Grant #0927315 (Co-PI,
PI 2011-), “Predicting Disrupted Network Behavior.”
7/2009 ARL Grant #W911NF-09-2-0038, “A
Computational Learning Approach to Adaptive Information Displays for Enhancing
Soldier Performance.”
5/2007 AFOSR Grant #FA9550-07-1-0178,“Category
Learning by Clustering with Extension to Dynamic Environments.”
1/2007 ARL
Grant #W911NF-07-2-0023 Love (Co-PI), “Sustaining and Enhancing High Optempo
Performance of Soldiers in the Transformed Military.”
6/2004 AFOSR Grant #FA9550-04-1-0226, “Maximizing
the Benefits of Training by Example and Direct Instruction.”
4/2004 NSF CAREER Grant #0349101,“Flexible
learning inside and outside the classroom.”
12/2002 Awarded (along with Ahn, Goldstone, Markman,
and Wolff) by the APA to host a conference honouring Doug Medin.
6/2002 Admitted and attended the APA’s summer
institute in fMRI at Harvard-MGH.
2/2002 J. S. McDonnell Foundation grant titled
“Interdisciplinary Collaborative Consortium on the Cognitive Neuroscience of
Category Learning.” I am one of numerous
co-investigators (Mark Gluck is the PI).
5/2001 AFOSR Grant #F49620-01-1-0295, “Adaptive
Learning Across Task Environments.”
4/1996 Graduate Fellowship, NDSEG.
4/1996 National Science Foundation Graduate
Fellowship.
SERVICE TO FIELD
journals Acta Psychologica; Attention,
Perception, & Psychophysics; Artificial
Intelligence; Australian Journal of Psychology; Behavioral and Brain Sciences;
Behavior Research Methods; Cell Reports; Cerebral Cortex; Cognition; Cognitive
Psychology (Associate Editor 2014-2017);
Cognitive Science (Editorial Board
2006-2009); Current Directions in Psychological Science; eLife; Experimental
Psychology; Decision; Frontiers in Cognitive Science (Editorial Board 2012-2014); Frontiers in Developmental Psychology (Editorial Board 2010-2014); Human Brain
Mapping; International Journal of Science and Mathematics Education (Special Issue Editor, 2012-2014); JARMAC;
Journal of Experimental Child Psychology; Journal of Cognitive Psychology; Journal
of Experimental Psychology: General; Journal of Experimental Psychology: Human
Perception and Performance; Journal of Experimental Psychology: Language,
Cognition and Neuroscience; Learning, Memory, and Cognition (Editorial Board 2006-2009); Journal of
Experimental Social Psychology; Journal of Mathematical Psychology (Special Issue Editor 2014-2015);
Journal of Memory and Language; Journal of Neuroscience; Journal of Vision;
Journal of Vision; Language and Cognitive Processes; Language, Cognition and
Neuroscience; Memory & Cognition (Editorial
Board 2006-2009; Associate Editor 2009-2012); Nature; Nature
Communications; Nature Human Behaviour; Nature Machine Intelligence; Neural
Computation; Neural Networks; Neurobiology of Learning and Memory; NeuroImage; Neurons, Behavior,
Data analysis, and Theory; Perception & Psychophysics; Perspectives on Psychological
Science;PLoS Computational Biology; PLoS ONE; Proceedings of the National
Academy of Sciences; Psychological Bulletin; Psychological Review;
Psychological Science; Psychonomic Bulletin and Review (Editorial Board 2006-2010); Science; Scientfic Reports; Trends in
Cognitive Sciences; Quarterly Journal of Experimental Psychology; Visual
Cognition; Wiley Interdisciplinary Reviews.
conferences AAAI 2006 (senior program committee member); Biologically
Inspired Cognitive Architecture (BICA); FLAIRS; ICCM; ICONIP; NIPS; Awards
Chair of 2007 Cognitive Science Society annual conference; Co-Chair of 2008 Cognitive Science Society annual conference,
Cognitive Science Society Program Committee member (various years); Expert
Panel for IEEE VIS 2020 Workshop on Visualization Psychology; Organiser for ICML
2022 Workshop on Human-Machine Collaboration and Teaming.
grants AFOSR’s Perception
& Cognition Program; ANR (France); BBSRC; Canada Foundation for Innovation;
Deutsche Forschungsgemeinschaft (DFG) “Clusters of Excellence” panel member; EPSRC,
ESRC; ESRC Rapporteur; EU Human Brain Project panel member (2014); FONDECYT Sicologia (Chile); FNR (Luxembourg);
FNRS (Belgium); FWO (Belgium); Leverhulme Trust; MRC; NASA’s Intelligent
Systems (Human-Centered Computing); National Endowment for the Humanities; Israeli
Science Foundation panel member (2011),
National Endowment for the Humanities; NIMH Cognition, Language, and Perception
(Fellowship) panel member (2006-2007); National Science Foundation
(Cognitive Neuroscience); National Science Foundation Perception, Action, and
Cognition panel member (2005-2007);
National Science Foundation program
evaluator (2012) for UCSD
Science of Learning Center; NSERC
(Canada); National Science Foundation (Decision, Risk and Management Sciences);
Research Council of Leuven (Belgium); Royal Society; UKRI Future Leaders Fellowships;
University of Texas at Austin Research Internship (RI) fellowship, the Wellcome
Trust.
other Advisory board of
IEEE VIS2020 Workshop of Visualization Psychology; Assisting Brain Imaging Data Structure (BIDS) group extend standard to
computational modelling; Comment of House of Lords request for feedback
on how government should regulate Artificial Intelligence; Consultant for BBC
Horizon (2014-2015, Episode 19) “Are Video Games Really That Bad?”; Outside
evaluator on tenure and promotion cases, and Ph.D. dissertations. Consultant
for Charles A. Dana Center academic youth development program. Royal Society mentoring programme. Air
Force AMBR project expert panel member
(2002-2004), program committee member for FLAIRS 2002 Special Track “Categorization and Concept
Representation: Models and Implications”; Program evaluation for Oxford’s new Social Data Science programme; Program
evaluation for Kingston’s new Decision Sciences MSc programme; Consultant on
Scientific Content of BBC Horizon episode (2015); Programme evaluation for Oxford
Internet Institute’s newly proposed MSc (2017), Programme evaluation for Warwick
new Psychology and Data Science MSc (2016); Consultant for Ofgem, dunnhumby,
the Take Five (https://takefive-stopfraud.org.uk/)
public service; REF consultant
for WBS; Advisory Board NSF project Learning Preferences and Domain Differences
in Design Fixation (2020-2024).
UNIVERSITY SERVICE
Mentor
for Research Fellows at Turing (2017- )
Royal
Society Mentorship Scheme (2021- )
Faculty of
Brain Sciences IT Committee (2020- )
Deputy Chair (2014-2019)
Head
of the Cognitive Systems Area at Texas (2007-2011)
Many Masters and Ph.D.
committees.
Member of numerous committees.
ADVISING
Postdoctoral
Christiane
Ahlheim (2016-2018, now at Google)
Daniel
Barry (2019-2022, now an Editor at Nature Communications)
Sebastian
Bobadilla-Suarez (2017-2021, now at a startup)
Kurt
Braunlich (2016-2019, now at NIH)
Johan
Carlin (2016-2017, now Cambridge CBU)
Gyslain
Giguere (2009-2013, now a U. of Montreal)
Olivia
Guest (2017-2020, now Asst. Prof at Donders)
Aaron
Hoffman (2007- 2011)
Matthew
Jones (2003-2007, now U. of Colorado Assoc. Prof.)
Xiaolang
“Ken” Liu (2023-)
Mike
Mack (2011- 2016, now U. of Toronto Assoc. Prof)
Rob
Mok (2017-2020, Leverhulme Fellowship at Cambridge)
Brett
Roads (2018-)
Nick
Sexton (2019-2022)
Graduate
Eric
Abel (2009-2010)
Kaarina
Aho (2020- )
Sebastian
Bobadilla-Suarez (2013-2017, now at startup)
Franziska
Bröker (2019-2022, secondary advisor, now postdoc at CMU)
Nikolay
Dagaev (2021- )
Tyler
Davis (2005-2010, now Texas Tech Assoc. Prof.)
John
Dennis (2003-2004)
Brian
Glass (2011-2012)
Todd M. Gureckis
(2001-2005, now NYU Full Prof.)
Laura Holland (2008-2009)
Adam Hornsby (2016-2022,
senior data scientists at dunnhumby)
Lukas Kopec
(2012-2016)
Levi Larkey (2002-2003)
Xiaolang “Ken” Liu
(2018-2022)
Ross Otto (2007-2012,
now McGill Assoc. Prof.)
Katie Parker (2013-2016)
Paula Parpart (2012-2017,
now postdoc at Oxford)
Guglielmo Reggio
(2023-)
Peter Riefer (2012-2016,
senior data scientist at Deliveroo)
Yasuaki Sakamoto (2000-2005,
now Research Asst. Prof. at Stevens Institute of Technology)
Katherine Snyder
(2007-2008)
Marc Tomlinson
(2004-2010)
Anne Warlaumont
(2006-2007)
A lot (5+ per year)
of MSc and BSc students (2012-)
PUBLICATIONS
Aho,
K., Roads, B.D., & Love, B. C. (2023). Signatures of cross-modal alignment
in children's early concepts. PNAS (in press).
https://doi.org/10.31234/osf.io/v6fnq
Roads, B. D., & Love, B. C. (2024). Modeling Similarity and Psychological Space. Annual Review of Psychology, 75(1), annurev-psych-040323-115131. https://doi.org/10.1146/annurev-psych-040323-115131
Mok, R. M., & Love, B. C. (2023). A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies. Science Advances, 9(29), eade6903. https://doi.org/10.1126/sciadv.ade6903
Hamdan, S., Love, B. C., von Polier, G. G., Weis, S., Schwender, H., Eickhoff, S. B., & Patil, K. R. (2023). Confound-leakage: Confound removal in machine learning leads to leakage. GigaScience, 12, giad071. https://doi.org/10.1093/gigascience/giad071
Collins,
K. M., Bhatt, U., Liu, W., Piratla, V., Sucholutsky, I., Love, B. C., & Weller,
W. (2023). Human-in-the-Loop Mixup. Uncertainty in Artificial Intelligence
(UAI). https://openreview.net/forum?id=BW6oQ0qZl0El
Broschard,
M. B., Kim, J., Love, B. C., & Freeman, J. H. (2023). Dorsomedial striatum,
but not dorsolateral striatum, is necessary for rat category learning. Neurobiology
of Learning and Memory, 199, 107732. https://doi.org/10.1016/j.nlm.2023.107732
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? Proceedings
of the AAAI Conference on Artificial Intelligence, 37(8), 9277–9285.
https://doi.org/10.1609/aaai.v37i8.26112
Dagaev,
N., Roads, B. D., Luo, X., Barry, D. N., Patil, K. R., & Love, B. C.
(2023). A too-good-to-be-true prior to reduce shortcut reliance. Pattern
Recognition Letters, 166, 164–171. https://doi.org/10.1016/j.patrec.2022.12.010
Aho,
K., Roads, B. D., & Love, B. C. (2022). System alignment supports cross-domain
learning and zero-shot generalisation. Cognition, 227, 105200. https://doi.org/10.1016/j.cognition.2022.105200
Barry,
D. N., & Love, B. C. (2022). A neural network account of memory replay and
knowledge consolidation. Cerebral Cortex, bhac054. https://doi.org/10.1093/cercor/bhac054
Bobadilla-Suarez,
S., Jones, M., & Love, B. C. (2022). Robust priors for regularized
regression. Cognitive Psychology, 132, 101444.
Bröker,
F., Love, B. C., & Dayan, P. (2022). When unsupervised training benefits
category learning. Cognition, 221, 104984.
Hornsby,
A. N., & Love, B. C. (2022). Sequential consumer choice as multi-cued retrieval.
Science Advances, 8(8), eabl9754. https://doi.org/10.1126/sciadv.abl9754
Sexton,
N. J., & Love, B. C. (2022). Reassessing hierarchical correspondences
between brain and deep networks through direct interface. Science Advances,
8(28), eabm2219. https://doi.org/10.1126/sciadv.abm2219
Barry,
D. N., & Love, B. C. (2021). Human learning follows the dynamics of gradient
descent. PsyArxiv.
Braunlich,
K., & Love, B. C. (2021). Bidirectional influences of information sampling
and concept learning. Psychological Review. https://doi.org/10.1037/rev0000287
Broschard,
M. B., Kim, J., Love, B. C., & Freeman, J. H. (2021). Category learning in
rodents using touchscreen-based tasks. Genes, Brain and Behavior, 20(1),
e12665.
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, 185,
107524.
Love,
B. C. (2021). Levels of biological plausibility. Philosophical Transactions
of the Royal Society B, 376(1815), 20190632.
Love,
B. C., & Roads, B. D. (2021). Similarity as a Window on the Dimensions of
Object Representation. Trends in Cognitive Sciences, 25(2),
94–96.
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, 4(2), 213–230.
Luo,
X., Sexton, N. J., & Love, B. C. (2021). A deep learning account of how
language affects thought. Language, Cognition and Neuroscience, 1–10.
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, 3547–3557.
Smith,
F. B., Roads, B. D., Luo, X., & Love, B. C. (2021). Understanding top-down
attention using task-oriented ablation design. ArXiv Preprint ArXiv:2106.11339.
Bickford
Smith, F., Luo, X., Roads, B. D., & Love, B. C. (2020). The perceptual
boost of visual attention is task-dependent in naturalistic settings. ArXiv E-Prints,
arXiv-2003.
Bobadilla-Suarez,
S., Ahlheim, C., Mehrotra, A., Panos, A., & Love, B. C. (2020). Measures of
neural similarity. Computational Brain & Behavior, 3(4),
369–383.
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, 3(1), 1–9.
Botvinik-Nezer,
R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M.,
Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., & others. (2020).
Variability in the analysis of a single neuroimaging dataset by many teams. Nature,
582(7810), 84–88.
Hornsby,
A. N., Evans, T., Riefer, P. S., Prior, R., & Love, B. C. (2020). Conceptual
organization is revealed by consumer activity patterns. Computational Brain
& Behavior, 3(2), 162–173.
Hornsby,
A. N., & Love, B. C. (2020). How decisions and the desire for coherency
shape subjective preferences over time. Cognition, 200, 104244.
Love,
B. C. (2020a). Linking Models with Brain Measures. In An Introduction to
Model-Based Cognitive Neuroscience (to appear) (2nd ed.). OSF Preprints.
Love,
B. C. (2020b). Model-based fMRI analysis of memory. Current Opinion in
Behavioral Sciences, 32, 88–93.
Mack,
M. L., Preston, A. R., & Love, B. C. (2020). Ventromedial prefrontal cortex
compression during concept learning. Nature Communications, 11(1),
1–11.
Mok,
R. M., & Love, B. C. (2020). Abstract neural representations of category
membership beyond information coding stimulus or response. Journal of Cognitive
Neuroscience, 1–17.
Roads,
B. D., & Love, B. C. (2020). Learning as the unsupervised alignment of
conceptual systems. Nature Machine Intelligence, 2(1), 76–82.
Smith,
F. B., Luo, X., Roads, B. D., & Love, B. C. (2020). The perceptual boost of
visual attention is task-dependent in naturalistic settings. ArXiv Preprint
ArXiv:2003.00882.
Braunlich,
K., & Love, B. C. (2019). Occipitotemporal representations reflect
individual differences in conceptual knowledge. Journal of Experimental
Psychology: General, 148(7), 1192.
Broschard,
M. B., Kim, J., Love, B. C., Wasserman, E. A., & Freeman, J. H. (2019). Selective
attention in rat visual category learning. Learning & Memory, 26(3),
84–92.
Guest,
O., Kanayet, F. J., & Love, B. C. (2019). Gerrymandering and computational
redistricting. Journal of Computational Social Science, 2(2),
119–131.
Guest,
O., & Love, B. C. (2019). Levels of representation in a deep learning model
of categorization. BioRxiv, 626374.
Mok,
R. M., & Love, B. C. (2019). A non-spatial account of place and grid cells
based on clustering models of concept learning. Nature Communications, 10(1),
1–9.
Poldrack,
R. A., Feingold, F., Frank, M. J., Gleeson, P., de Hollander, G., Huys, Q. J.,
Love, B. C., Markiewicz, C. J., Moran, R., Ritter, P., & others. (2019).
The importance of standards for sharing of computational models and data. Computational
Brain & Behavior, 2(3), 229–232.
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, 116(28), 13903–13908.
Ahlheim,
C., & Love, B. C. (2018). Estimating the functional dimensionality of
neural representations. NeuroImage, 179, 51–62.
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, 44(1), 24.
Inhoff,
M. C., Libby, L. A., Noguchi, T., Love, B. C., & Ranganath, C. (2018). Dynamic
integration of conceptual information during learning. PloS One, 13(11),
e0207357.
Love,
B. C. (2018). Model comparison, not model falsification. Behavioral and
Brain Sciences, 41.
Mack,
M. L., Love, B. C., & Preston, A. R. (2018). Building concepts one episode
at a time: The hippocampus and concept formation. Neuroscience Letters, 680,
31–38.
Parpart,
P., Jones, M., & Love, B. C. (2018). Heuristics as Bayesian inference under
extreme priors. Cognitive Psychology, 102, 127–144.
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, 37(25), 6066–6074.
Guest,
O., & Love, B. C. (2017). What the success of brain imaging implies about
the neural code. ELife, 6(e21397), http-dx.
Love,
B. C. (2017). Concepts, meaning, and conceptual relationships. The Oxford Handbook
of Cognitive Science, 137–150.
Love,
B. C., Guest, O., Slomka, P., Navarro, V., & Wasserman, E. (2017). Deep Networks
as Models of Human and Animal Categorization. CogSci.
Mack,
M. L., Preston, A. R., & Love, B. C. (2017). Medial prefrontal cortex
compresses concept representations through learning. 2017 International
Workshop on Pattern Recognition in Neuroimaging (Prni), 1–4.
Palmeri,
T. J., Love, B. C., & Turner, B. M. (2017). Model-based cognitive neuroscience.
In Journal of Mathematical Psychology (Vol. 76, pp. 59–64). Academic
Press.
Parpart,
P., Schulz, E., Speekenbrink, M., & Love, B. C. (2017). Active learning
reveals underlying decision strategies. BioRxiv.
Riefer,
P. S., Prior, R., Blair, N., Pavey, G., & Love, B. C. (2017). Coherency-maximizing
exploration in the supermarket. Nature Human Behaviour, 1(1), 1–4.
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, 29(3), 530–544.
Turner,
B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2017).
Approaches to analysis in model-based cognitive neuroscience. Journal of
Mathematical Psychology, 76, 65–79.
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, 145(3),
284.
Love,
B. C. (2016). Cognitive models as bridge between brain and behavior. Trends
in Cognitive Sciences, 20(4), 247–248.
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, 113(46), 13203–13208.
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, 123, 84–91.
Gureckis,
T. M., & Love, B. C. (2015). Computational reinforcement learning. The
Oxford Handbook of Computational and Mathematical Psychology, 99–117.
Love,
B. C. (2015). The algorithmic level is the bridge between computation and
brain. Topics in Cognitive Science, 7(2), 230–242.
Love,
B. C., Kopeć, Lukasz, & Guest, O. (2015). Optimism bias in fans and sports
reporters. Plos One, 10(9), e0137685.
Love,
B. C., Ramscar, M., Griffiths, T. L., & Jones, M. (2015). Generative and
Discriminative Models in Cognitive Science. CogSci.
Newall,
P. W., & Love, B. C. (2015). Nudging investors big and small toward better
decisions. Decision, 2(4), 319.
Parpart,
P., Schulz, E., Speekenbrink, M., & Love, B. C. (2015). Active learning as
a means to distinguish among prominent decision strategies. CogSci.
Riefer,
P. S., & Love, B. C. (2015). Unfazed by both the bull and bear: Strategic
exploration in dynamic environments. Games, 6(3), 251–261.
Anderson,
O. R., Love, B. C., & Tsai, M.-J. (2014). Neuroscience perspectives for
science and mathematics learning in technology-enhanced learning environments.
In International Journal of Science and Mathematics Education (Vol. 12,
Issue 3, pp. 467–474). Springer Netherlands.
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, 34(22), 7472–7484.
Hornsby,
A. N., & Love, B. C. (2014). Improved classification of mammograms
following idealized training. Journal of Applied Research in Memory and
Cognition, 3(2), 72–76.
Love,
B. C. (2014). Categorization. Oxford Handbook of Cognitive Neuroscience.
Love,
B. C., Jarecki, J., Busemeyer, J. R., Taatgen, N. A., Griffiths, T. L., &
Mirjam, J. (2014). Moot Point Process Models. Proceedings of the Annual
Meeting of the Cognitive Science Society, 36(36).
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, 14(4), 1167–1183.
Parpart,
P., Jones, M., & Love, B. (2014). Heuristics as a special case of Bayesian
Inference. Proceedings of the Annual Meeting of the Cognitive Science
Society, 36(36).
Patil,
K. R., Zhu, J., Kopeć, Lukasz, & Love, B. C. (2014). Optimal teaching for limited-capacity
human learners. Advances in Neural Information Processing Systems, 27.
Riefer,
P. S., & Love, B. C. (2014). Choice exploration and exploitation in
purchase decisions: A longitudinal study of customers’ exploration and
exploitation of supermarket products. Proceedings of the Annual Meeting of
the Cognitive Science Society, 36(36).
Blanco,
N. J., Otto, A. R., Maddox, W. T., Beevers, C. G., & Love, B. C. (2013). The
influence of depression symptoms on exploratory decision-making. Cognition,
129(3), 563–568.
Giguère,
G., & Love, B. C. (2013). Limits in decision making arise from limits in
memory retrieval. Proceedings of the National Academy of Sciences, 110(19),
7613–7618.
Glass,
B. D., Maddox, W. T., & Love, B. C. (2013). Real-time strategy game
training: Emergence of a cognitive flexibility trait. PloS One, 8(8),
e70350.
Kopec,
L., & Love, B. C. (2013). Are forgetting processes crucial to category
learning? CogSci.
Kusev,
P., Love, B. C., & van Schaik, P. (2013). Decision-Network Context: Dynamics
and Learning in Preference Formation. Paper Presented at the 54th Annual
Meeting of the Psychonomic Society, Canada. Abstracts of the Psychonomic
Society.
Love,
B. C. (2013). Grounding quantum probability in psychological mechanism. Behavioral
and Brain Sciences, 36(3), 296–296.
Mack,
M. L., Preston, A. R., & Love, B. C. (2013). Decoding the brain’s algorithm
for categorization from its neural implementation. Current Biology, 23(20),
2023–2027.
Parpart,
P., Jones, M., & Love, B. C. (2013). When is it rational to rely on
heuristics? CogSci.
Ramscar,
M., Hendrix, P., Love, B., & Baayen, R. H. (2013). Learning is not decline:
The mental lexicon as a window into cognition across the lifespan. The
Mental Lexicon, 8(3), 450–481.
Richardson,
D. C., Riefer, P., Love, B., Lotto, B., Clarke, R. C., Dale, R., Rogers, J.,
& Ireland, J. (2013). Experiments in dynamic group action and decision
making: How crowds of people can walk a tightrope together and survive a zombie
attack. Proceedings of the Annual Meeting of the Cognitive Science Society,
35(35).
Sanders,
M., Davis, T., & Love, B. C. (2013). Is better beautiful or is beautiful
better? Exploring the relationship between beauty and category structure. Psychonomic
Bulletin & Review, 20(3), 566–573.
Davis,
T., Love, B. C., & Maddox, W. T. (2012). Age-related declines in the
fidelity of newly acquired category representations. Learning & Memory,
19(8), 325–329.
Davis,
T., Love, B. C., & Preston, A. R. (2012a). Learning the exception to the
rule: Model-based fMRI reveals specialized representations for surprising
category members. Cerebral Cortex, 22(2), 260–273.
Davis,
T., Love, B. C., & Preston, A. R. (2012b). 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, 38(4), 821.
Dixit,
V., Alsup, R., Waller, S., Love, B. C., & Tomlinson, M. (2012). A STATIC
MODEL FOR PREDICTING DISRUPTED NETWORK BEHAVIOUR. TRANSPORTATION & LOGISTICS
MANAGEMENT, 3–10.
Love,
B. C., & Jones, M. (2012). Bayesian Learning. In N. M. Seel (Ed.), Encyclopedia
of the Sciences of Learning (pp. 415–417). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_255
Eliasmith,
C., Griffiths, T., Hardcastle, V. G., Love, B., Bechtel, W., Cooper, R. P.,
& Peebles, D. (2012). Thirty years of Marr’s Vision: Levels of Analysis in
Cognitive Science. Proceedings of the Annual Meeting of the Cognitive Science
Society, 34(34).
Knox,
W. B., Glass, B. D., Love, B. C., Maddox, W. T., & Stone, P. (2012). How
humans teach agents. International Journal of Social Robotics, 4(4),
409–421.
Knox,
W. B., Otto, A. R., Stone, P., & Love, B. (2012). The nature of belief-directed
exploratory choice in human decision-making. Frontiers in Psychology, 2,
398.
Otto,
A. R., Markman, A. B., & Love, B. C. (2012). Taking more, now: The
optimality of impulsive choice hinges on environment structure. Social
Psychological and Personality Science, 3(2), 131–138.
Giguere,
G., & Love, B. C. (2011). Determinants of learning difficulty and boundary
uncertainty in unidimensional category learning. Proceedings of the Annual
Meeting of the Cognitive Science Society, 33(33).
Glass,
B. D., Tomlinson, M. T., Maddox, W. T., & Love, B. C. (2011). Becoming a
Gamer: Cognitive Effects of Real-Time Strategy Gaming. CogSci.
Goldwater,
M. B., Tomlinson, M. T., Echols, C. H., & Love, B. C. (2011). Structural
priming as structure-mapping: Children use analogies from previous utterances
to guide sentence production. Cognitive Science, 35(1), 156–170.
Jones,
M., & Love, B. C. (2011a). Bayesian fundamentalism or enlightenment? On the
explanatory status and theoretical contributions of Bayesian models of
cognition. Behavioral and Brain Sciences, 34(4), 169.
Jones,
M., & Love, B. C. (2011b). Pinning down the theoretical commitments of
Bayesian cognitive models. Behavioral and Brain Sciences, 34(4),
215–231.
Love,
B., & Spencer, J. (2011). Moving Beyond Where and What to How: Using Models
and fMRI to Understand Brain-Behavior Relations. Proceedings of the Annual
Meeting of the Cognitive Science Society, 33(33).
Davis,
T., & Love, B. C. (2010). Memory for category information is idealized
through contrast with competing options. Psychological Science, 21(2),
234–242.
Gureckis,
T. M., & Love, B. C. (2010). Direct associations or internal
transformations? Exploring the mechanisms underlying sequential learning
behavior. Cognitive Science, 34(1), 10–50.
Hoffman,
A., Love, B., & Markman, A. (2010). Selective Attention by Structural
Alignment: An Eyetracking Study. Proceedings of the Annual Meeting of the
Cognitive Science Society, 32(32).
Love,
B. C., & Tomlinson, M. (2010). Mechanistic models of associative and
rule-based category learning. The Making of Human Concepts, 53–74.
Otto,
A. R., & Love, B. C. (2010). You don’t want to know what you’re missing:
When information about forgone rewards impedes dynamic decision making. Judgment
and Decision Making, 5(1), 1.
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, 36(3),
797.
Sakamoto,
Y., & Love, B. C. (2010). Learning and retention through predictive
inference and classification. Journal of Experimental Psychology: Applied,
16(4), 361.
Tomlinson,
M. T., & Love, B. C. (2010). When learning to classify by relations is
easier than by features. Thinking & Reasoning, 16(4),
372–401.
Davis,
T., Love, B. C., & Maddox, W. T. (2009a). Anticipatory emotions in decision
tasks: Covert markers of value or attentional processes? Cognition, 112(1),
195–200.
Davis,
T., Love, B. C., & Maddox, W. T. (2009b). Two pathways to stimulus encoding
in category learning? Memory & Cognition, 37(4), 394–413.
Gureckis,
T., Love, B., Markman, A., & Otto, A. R. (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 the
Cognitive Science Society, 31(31).
Gureckis,
T. M., & Love, B. C. (2009a). Learning in noise: Dynamic decision-making in
a variable environment. Journal of Mathematical Psychology, 53(3),
180–193.
Gureckis,
T. M., & Love, B. C. (2009b). Short-term gains, long-term pains: How cues
about state aid learning in dynamic environments. Cognition, 113(3),
293–313.
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, 1351–1360.
Love,
B., & Sakamoto, Y. (2009). You Only Had to Ask Me Once: Long-term Retention
Requires Direct Queries During Learning. Proceedings of the Annual Meeting
of the Cognitive Science Society, 31(31).
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, 16(5),
957–963.
Sakamoto,
Y., & Love, B. C. (2009). You only had to ask me once: Long-term retention
requires direct queries during learning. Proceedings of the 31st Annual
Conference of the Cognitive Science Society. Amsterdam, Netherlands: Cognitive
Science Society.
Tomlinson,
M. T., Howe, M., & Love, B. C. (2009). Seeing the World through an Expert’s
Eyes: Context-Aware Display as a Training Companion. International
Conference on Foundations of Augmented Cognition, 668–677.
Davis,
T., & Love, B. C. (2008). How goals shape category acquisition: The role of
contrasting categories. Proceedings of the Annual Meeting of the Cognitive
Science Society, 30(30).
Love,
B. C., Jones, M., Tomlinson, M. T., & Howes, M. (2008). Predicting
information needs: Adaptive display in dynamic environments. Proceedings of
the Annual Meeting of the Cognitive Science Society, 30(30).
Love,
B. C., Tomlinson, M., & Gureckis, T. M. (2008). The concrete substrates of
abstract rule use. Psychology of Learning and Motivation, 49,
167–207.
Maddox,
W. T., Love, B. C., Glass, B. D., & Filoteo, J. V. (2008). When more is
less: Feedback effects in perceptual category learning. Cognition, 108(2),
578–589.
Sakamoto,
Y., Jones, M., & Love, B. C. (2008). Putting the psychology back into
psychological models: Mechanistic versus rational approaches. Memory &
Cognition, 36(6), 1057–1065.
Tomlinson,
M. T., & Love, B. C. (2008). Monkey see, monkey do: Learning relations
through concrete examples. Behavioral and Brain Sciences, 31(2),
150–151.
Davis,
T., Love, B. C., & Maddox, W. T. (2007). Translating From Perceptual to
Cognitive Coding. Proceedings of the Annual Meeting of the Cognitive Science
Society, 29(29).
Gureckis,
T. M., & Love, B. C. (2007). Behaviorism reborn? Statistical learning as
simple conditioning. Proceedings of the Annual Meeting of the Cognitive
Science Society, 29(29).
Jones,
M., & Love, B. C. (2007). Beyond common features: The role of roles in
determining similarity. Cognitive Psychology, 55(3), 196–231.
Love,
B. C., & Gureckis, T. M. (2007). Models in search of a brain. Cognitive,
Affective, & Behavioral Neuroscience, 7(2), 90–108.
Rein,
J. R., Love, B. C., & Markman, A. B. (2007). Feature relations and feature
salience in natural categories. Proceedings of the Annual Meeting of the
Cognitive Science Society, 29(29).
Tomlinson,
M. T., & Love, B. C. (2007). Relation-based categories are easier to learn
than feature-based categories. Proceedings of the Annual Meeting of the
Cognitive Science Society, 29(29).
Gureckis,
T. M., & Love, B. C. (2006). Bridging levels: Using a cognitive model to
connect brain and behavior in category learning. Proceedings of the Annual
Meeting of the Cognitive Science Society, 28(28).
Jones,
M., & Love, B. C. (2006). The emergence of multiple learning systems. Proceedings
of the Annual Meeting of the Cognitive Science Society, 28(28).
Jones,
M., Love, B. C., & Maddox, W. T. (2006a). Recency effects as a window to
generalization: Separating decisional and perceptual sequential effects in
category learning. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 32(2), 316.
Jones,
M., Love, B. C., & Maddox, W. T. (2006b). The role of similarity in
generalization. Proceedings of the Annual Meeting of the Cognitive Science
Society, 28(28).
Jones,
M., Love, B. C., & Sakamoto, Y. (2006). Tracking Variability in Learning:
Contrasting Statistical and Similarity-Based Accounts. Proceedings of the
Annual Meeting of the Cognitive Science Society, 28(28).
Love,
B. C. (2006). In Vivo or In Vitro: Cognitive Architectures and Task-Specific
Models. In Modeling Human Behavior With Integrated Cognitive Architectures
(pp. 369–382). Psychology Press.
Love,
B. C., & Sakamoto, Y. (2006). Sizable Sharks Swim Swiftly: Learning
Correlations through Inference in a Classroom Setting. Proceedings of the
Annual Meeting of the Cognitive Science Society, 28(28).
Love,
B. C., & Tomlinson, M. T. (2006). Learning Abstract Relations Through
Analogy to Concrete Exemplars. Proceedings of the Annual Meeting of the
Cognitive Science Society, 28(28).
Sakamoto,
Y., & Love, B. C. (2006a). Sizable sharks swim swiftly: Learning
correlations through inference in a classroom setting. Proceedings of the
28th Annual Conference of the Cognitive Science Society. Vancouver, Canada:
Cognitive Science Society.
Sakamoto,
Y., & Love, B. C. (2006b). Vancouver, Toronto, Montreal, Austin: Enhanced
oddball memory through differentiation, not isolation. Psychonomic Bulletin
& Review, 13(3), 474–479.
Sakamoto,
Y., Love, B. C., & Jones, M. (2006). Tracking variability in learning:
Contrasting statistical and similarity-based accounts. Proceedings of the
28th Annual Conference of the Cognitive Science Society. Vancouver, Canada:
Cognitive Science Society.
Tomlinson,
M., & Love, B. C. (2006). Learning abstract relations through analogy to
concrete exemplars. Proceedings of the 28th Annual Conference of the
Cognitive Science Society, 2269–2274.
Tomlinson,
M. T., & Love, B. C. (2006). From pigeons to humans: Grounding relational
learning in concrete examples. AAAI, 199–204.
Ahn,
W. E., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. E.
(2005). Categorization inside and outside the laboratory: Essays in honor of
Douglas L. Medin.
Gureckis,
T. M., & Love, B. C. (2005). A critical look at the mechanisms underlying
implicit sequence learning. Proceedings of the Annual Meeting of the
Cognitive Science Society, 27(27).
Jones,
M., Love, B. C., & Maddox, W. T. (2005). Stimulus generalization in
category learning. Proceedings of the Annual Meeting of the Cognitive
Science Society, 27(27).
Love,
B. C. (2005a). Environment and goals jointly direct category acquisition. Current
Directions in Psychological Science, 14(4), 195–199.
Love,
B. C. (2005b). Method and apparatus for incorporating decision making into
classifiers (USPTO Patent).
Love,
B. C., & Gureckis, T. M. (2005). Modeling Learning Under the Influence of
Culture. In Categorization inside and outside the laboratory: Essays in
honor of Douglas L. Medin. American Psychological Association.
Gureckis,
T. M., & Love, B. C. (2004). Common mechanisms in infant and adult category
learning. Infancy, 5(2), 173–198.
Love,
B. C., & Gureckis, T. M. (2004). The hippocampus: Where a cognitive model
meets cognitive neuroscience. Proceedings of the 26th Annual Conference of
Cognitive Science Society.
Love,
B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: a network model of
category learning. Psychological Review, 111(2), 309.
Sakamoto,
Y., & Love, B. C. (2004a). Schematic influences on category learning and
recognition memory. Journal of Experimental Psychology: General, 133(4),
534.
Sakamoto,
Y., & Love, B. C. (2004b). Type/Token Information in Category Learning and
Recognition. Proceedings of the Annual Meeting of the Cognitive Science
Society, 26(26).
Sakamoto,
Y., Matsuka, T., & Love, B. C. (2004). Dimension-Wide vs. Exemplar-Specific
Attention in Category Learning and Recognition. ICCM, 261–266.
Gureckis,
T. M., & Love, B. C. (2003a). Human unsupervised and supervised learning as
a quantitative distinction. International Journal of Pattern Recognition and
Artificial Intelligence, 17(05), 885–901.
Gureckis,
T. M., & Love, B. C. (2003b). Towards a unified account of supervised and
unsupervised category learning. Journal of Experimental & Theoretical
Artificial Intelligence, 15(1), 1–24.
Larkey,
L. B., & Love, B. C. (2003). CAB: Connectionist analogy builder. Cognitive
Science, 27(5), 781–794.
Love,
B. C. (2003a). Concept learning. The Encyclopedia of Cognitive Science, 1,
646–652.
Love,
B. C. (2003b). The multifaceted nature of unsupervised category learning. Psychonomic
Bulletin & Review, 10(1), 190–197.
Love,
B. C., & Markman, A. B. (2003). The nonindependence of stimulus properties
in human category learning. Memory & Cognition, 31(5),
790–799.
Sakamoto,
Y., & Love, B. C. (2003). Category structure and recognition memory. Proceedings
of the Annual Meeting of the Cognitive Science Society, 25(25).
Gureckis,
T. M., & Love, B. C. (2002a). Modeling Unsupervised Learning with SUSTAIN. FLAIRS
Conference, 163–167.
Gureckis,
T. M., & Love, B. C. (2002b). 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,
399–404.
Love,
B. C. (2002a). Comparing supervised and unsupervised category learning. Psychonomic
Bulletin & Review, 9(4), 829–835.
Love,
B. C. (2002b). Similarity and categorization: A review. AI Magazine, 23(2),
103–103.
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, 28(3), 585.
Love,
B. C. (2001). Uncovering analogy. Trends in Cognitive Sciences, 5(10),
454–455. https://doi.org/10.1016/S1364-6613(00)01747-2
Love,
B. C. (2001). Three deadly sins of category learning modelers. Behavioral
and Brain Sciences, 24(4), 687.
Love,
B. C. (2000a). A computational level theory of similarity. Proceedings of
the 22nd Annual Meeting of the Cognitive Science Society, 316–321.
Love,
B. C. (2000b). Learning at different levels of abstraction. Proceedings of
the Annual Meeting of the Cognitive Science Society, 22(22).
Love,
B. C., Markman, A. B., & Yamauchi, T. (2000). Modeling classification and
inference learning. AAAI/IAAI, 136–141.
Love,
B., Markman, A., & Yamauchi, T. (2000). Modeling inference and
classification learning. The National Conference on Artificial Intelligence
(AAAI2000), 13641.
Love,
B. C. (1999). Modeling human category learning [PhD Thesis].
Northwestern University.
Love,
B. C., Rouder, J. N., & Wisniewski, E. J. (1999). A structural account of
global and local processing. Cognitive Psychology, 38(2),
291–316.
Love,
B. C. (1998). Utilizing time: Asynchronous Binding. Advances in Neural
Information Processing Systems, 11.
Love,
B. C., & Medin, D. L. (1998a). Modeling item and category learning. Proceedings
of the 20th Annual Conference of the Cognitive Science Society, 639–644.
Love,
B. C., & Medin, D. L. (1998b). SUSTAIN: A model of human category learning.
Aaai/Iaai, 671–676.
Sloman,
S. A., Love, B. C., & Ahn, W.-K. (1998). Feature centrality and conceptual
coherence. Cognitive Science, 22(2), 189–228.
Wisniewski,
E. J., & Love, B. C. (1998). Relations versus properties in conceptual
combination. Journal of Memory and Language, 38(2), 177–202.
Love,
B. C. (1996). Mutability, conceptual transformation, and context. Proceedings
of the Eighteenth Annual Conference of the Cognitive Science Society, 459–463.
Love,
B. C., & Sloman, S. A. (1995). Mutability and the determinants of
conceptual transformability. Proc. 17th Annu. Conf. Cogn. Sci. Soc,
65–59
SELECTED POPULAR WRITINGS (see http://bradlove.org/#press
for press coverage)
The lab’s
blog, https://bradlove.org/blog/#blog
Love, B. C.
(2019). BBC homepage for a week (500k hits first day). Do supermarkets know
more about us than we do? https://www.bbc.co.uk/news/business-47357292
Love, B. C.
(2016). Will AI spell the end of humanity? The tech industry wants you to think
so. The Register from The Conversation, https://www.theregister.co.uk/2016/10/25/will_ai_spell_the_end_of_humanity_the_tech_industry_wants_you_to_think_so
Love, B. C. (2015).
Gaming improves your brain power – reality or hype? IFL from The Conversation,
https://theconversation.com/gaming-improves-your-brain-power-reality-or-hype-41002
INVITED TALKS
11/2023 Neurotech Panel, Foresight
Institute’s Vision Weekend.
11/2023 “The mind is
simultaneously discriminative and generative”. Workshop: Bayesian and Not so
Bayesian Belief Update in Economics, Neuroscience, and Machine Learning.
10/2023 “Embeddings
of and for the mind”, Birkbeck, University of London.
10/2023 “Embeddings
of and for the mind”, University of Bath.
10/2023 “Aligning embedding
spaces for model evaluation and learning”, Symposium on mathematics of
neuroscience, Neuromonster, Rhodes, Greece.
8/2023 “Advancing neuroscience
using large language models”, Foresight Institute. San Francisco, CA. https://youtu.be/7G4gR8LfI04?si=Z-HUpWvuVkIwF5_D
8/2023 “Comparing biological
and artificial networks: are we limited by tools, hypotheses, or data?” Workshop
at Cognitive Computational Neuroscience (CCN).
6/2023 Workshop
on the “Neurobiology of statistical learning”. Kavli Institute for Theoretical
Physics, Santa Barbara, USA.
5/2023 “Process
models to understand mind and brain”, University of Freiburg.
12/2022 “Aligning
Embedding Spaces”, Data science group at dunnhumby Ltd.
11/2022 “Evaluating
deep learning models as accounts of mind and brain”, Inter-CDT conference on AI
(symposium on computational neuroscience, Bristol.
10/2022 “Cross-domain
learning and zero-shot generalisation”, ELLIS Natural Intelligence Worksop,
Crete, Greece.
4/2022 “Medial
prefrontal cortex and the hippocampus support a domain-general learning
mechanism”, British Neuroscience Association.
4/2022 “Multilevel
theories for completeness”, Symposium at CNS on Marr’s levels, organised by Tomaso
Poggio.
4/2022 “Process
models to understand mind and brain”, University of Pennsylvania’s MindCORE
Seminar.
10/2021 “Embedding Spaces for Decision
Making”, University of Basel.
10/2021 “Embedding
Spaces for Decision Making”, Max Planck for Adaptive Rationality.
10/2021 “Embedding
Spaces for Decision Making” University of Zurich, Neuroeconomics.
9/2021 “Bridging
Brain and Behaviour with Process Models”, Max Planck for Human Cognitive and
Brain Sciences.
2/2021 "Integrating
Embedding Spaces", University of Tübingen.
3/2021 "Do
you make decisions or do your decisions make you?", Who's in control? UCL
human sciences symposium.
2/2021 "Integrating
Embedding Spaces", University of Trento.
2/2021 "Integrating
Embedding Spaces", University of Wisconsin at Madison.
11/2020 “Large-scale embeddings from human
behaviour”, The Alan Turing Institute.
9/2020 "Category
Learning as Compression", Max Plank Centre for Computational Psychiatry
and Ageing Research.
6/2020 “From
Bayesian models to heuristics and back again”, Max Planck Institute for
Intelligent Systems.
5/2020 Role
of Bayesian Models in Neuroscience. Neuromatch 2.0 (4.5k in attendance; https://www.crowdcast.io/e/neuromatch2/23)
5/2020 “Big data, smart analyses”, UCL
Changing Minds webinar.
5/2020 “A
clustering account of spatial and non-spatial concept learning”, University of
Bristol.
5/2020 “Category Learning as Compression”
Cognitive Neuroscience Society (CNS).
6/2023 Workshop on the “Neurobiology
of statistical learning”. Kavli Institute for Theoretical Physics, Santa
Barbara, USA.
10/2021 “Embedding
Spaces for Decision Making”, University of Basel
10/2021 “Embedding Spaces for
Decision Making”, Max Planck for Adaptive Rationality.
10/2021 “Embedding Spaces for
Decision Making” University of Zurich, Neuroeconomics.
9/2021 “Bridging Brain and Behaviour
with Process Models”, Max Planck for Human Cognitive and Brain Sciences
3/2021 "Do you make decisions
or do your decisions make you?", Who's in control? UCL human sciences
symposium.
2/2021 "Integrating
Embedding Spaces", University of Tübingen (Felix Wichmann).
2/2021 "Integrating
Embedding Spaces", University of Trento.
2/2021 "Integrating
Embedding Spaces", University of Wisconsin at Madison.
9/2020 "Category Learning
as Compression", Max Plank Centre for Computational Psychiatry and Ageing Research
6/2020 “From Bayesian models
to heuristics and back again” Max Planck Institute for Intelligent Systems.
5/2020 Role of Bayesian Models
in Neuroscience. Neuromatch 2.0 (4.5k in attendance; https://www.crowdcast.io/e/neuromatch2/23)
5/2020 “Big
data, smart analyses”, UCL Changing Minds webinar
5/2020 “A clustering account
of spatial and non-spatial concept learning” University of Bristol.
5/2020 “Category Learning as
Compression” Cognitive Neuroscience Society (CNS) symposium.
2/2020
“Concept learning
as compression”, Hamburg Center of NeuroScience at UKE.
1/2020 “A common mechanism for
spatial and concept learning”, Max Planck Institute for Human Cognitive and
Brain Sciences.
9/2019 ELLIS, “Top-down
attention in the human brain and convolutional networks,” Berlin.
7/2019 DoD Future Directions Workshop
on Human-Machine Learning, Arlington, VA.
7/2019 General AI discussion
panel, Royal Institution.
5/2019 “Levels of Representation
in a Deep Learning Model of Categorisation,” University of Bristol.
5/2019 “Concept Learning as
Compression,” Control Processes 2019, Brown University.
4/2019 “Coherency Seeking as a
Driver of Preferences,” Wharton Business School.
4/2019 Working group on Brain
Imaging Data Structure (BIDS) extension for computational modelling. Princeton
University.
3/2019 “A deep learning account
of shape and colour biases in categorisation,” SRCD Biennial, Baltimore.
3/2019 “Concept
Learning as Compression,” ICPS, Paris.
9/2018 Workshop presentation
at “Interpreting BOLD: Furthering the dialogue between cellular and cognitive
neuroscience” at Oxford.
9/2018
“Evaluation of the
predictive value of the HoNOS,” St Andrew's Healthcare.
6/2018 “Predicting when
consumers will be unpredictable “, Cheltenham Science Festival - How Predictable
Are You? Hosted by Hannah Fry.
6/2018 “Building useful representations
based on human activity patterns”, UBEL DTC, UCL Innovation event.
6/2018 “A deep learning account
of shape and colour biases in categorisation”, for Multi-Disciplinary
Developmental Dynamics (ETF2018).
5/2018 “Distinct Accumulation
and Aggregation Stages or Processes?”, Santa Fe Institute, working group on “Distributed
Decision Making: Universal features of decision making via collective computation”.
6/2018 “Concept Learning as Compression”,
Cambridge CBU.
5/2018 “Selective Attention for
Dimensionality Reduction”, SBDM, Symposium on Biology of Decision Making, Paris.
4/2018 “Concept Learning as Compression”,
Brain and Behaviour Institute at the Research Centre Jülich.
3/2018 "Attention as
Uncertainty-Minimising Information Sampling”, reinforcement learning workshop at
COSYNE in Colorado.
2/2018 “Heuristics as Bayesian
inference under extreme priors” keynote, for “Computational modeling of decision-making
across scales: from neural coding to Policy-making”, Paris.
8/2017 “Different Modes of
Exploration”, Invited to join a symposium at ICON, Amsterdam.
5/2017 “Exploration with Objective
and Subjective Awards”, Warwick Business School.
2/2017 “Predicting and
Understanding Consumer Behaviour”, Keynote, Microsoft Tech Days.
11/2016 “Predicting and Understanding
Human Behaviour”, keynote address at Big Data Analytics, London.
11/2016 “Tuning Conceptual
Knowledge through Hippocampal-Prefrontal Interactions”, University of Glasgow.
8/2016 “Coherency Maximizing
Exploration in the Supermarket”, Invited Symposium organised by Dan Bartels,
Int. Conference on Thinking.
6/2016 “Psychology meets Big Data
in the Supermarket”, Knowledge Exchange Event, British Museum.
3/2016 “People's Inductive
Biases in Learning and Decision Making”, Keynote at Visual Analytics event at
the Alan Turing Institute.
3/2016 “The Categorising Brain”,
University of Edinburgh.
3/2016 “The Categorising
Brain”, University of Sussex.
11/2015 Food Matters Live.
10/2015
“Optimal Teaching to
Infer the Nature of the Human Learner and Knowledge Organisation”, Conference
on Complex Systems.
8/2015 Ogilvy Change Summer School.
5/2015 “Do People and
Intelligent Machines Make Decisions in the Same Way?” Pint of Science, London.
5/2015 “Apparent attentional
limits during learning as limits in memory retrieval”, Workshop on Memory
Processes in Judgment and Decision Making, hosted by University of Basel.
4/2015 “Do we make food choices
rationally?” write-up in Lancet: http://t.co/rTrFo87FnJ, Edinburgh International
Science Festival.
3/2015 “Decoding the Brain's Algorithm
for Categorisation from its Neural Implementation”, University of Plymouth.
1/2015 “Decoding
the Brain's Algorithm for Categorisation from its Neural Implementation”,
Institute of Psychiatry, King's College London
1/2015 “Decoding the Brain's
Algorithm for Categorisation from its Neural Implementation”, 2015 EPS semantics
symposium.
9/2014 “Decoding the Brain's
Algorithm for Categorisation from its Neural Implementation”, NYU.
5/2014 “Exploration and Exploitation:
Converging Computational, Physiological, Psychiatric, Genetic, and Consumer-Choice
Perspectives”, University of Bristol.
9/2014 “Decoding the Brain's
Algorithm for Categorisation from its Neural Implementation,” NYU.
5/2014 “Exploration and Exploitation:
Converging Computational, Physiological, Psychiatric, Genetic, and Consumer-Choice
Perspectives,” University of Bristol.
3/2014 “Decoding the Brain's
Algorithm for Categorisation from its Neural Implementation,” University of Lueven.
2/2014 “Limits in decision
making arise from limits in memory retrieval,” University of Basel.
2/2014 “Gaming as a Convergence
Point of Cognitive Science Theory and Practice,” HULT International Business
School, London.
1/2014 “Decoding the Brain's
Algorithm for Categorisation from its Neural Implementation”, MRC-Cognition and
Brain sciences Unit at Cambridge University.
11/2013 “Improving Cognitive
Function Through Gaming”, Decision-making in neurological rehabilitation
Inaugural Symposium, Centre for Neurorehabilitation @UCLP.
8/2013 “Limits in Decision
Making Reflect Limits in Memory Retrieval”, dunnhumby Corportation, London, UK.
6/2013 AECT International Conference
on the Frontier in e-Learning Research, Taipei, Taiwan.
5/2013 “Limits in Decision Making
Reflect Limits in Memory Retrieval”, Workshop on Integrating Approaches to
Computational Cognition, Sponsored by the National Science Foundation,
Arlington, VA, USA.
3/2013 “Limits in Decision Making
Reflect Limits in Memory Literature”, Computational Models of Cognition Workshop,
Birkbeck.
2/2013 “Limits in Decision Making
Reflect Limits in Memory Literature”, London JDM group.
2/2013 “Linking Brain, Behaviour,
and Computation in Category Learning”, City University London
11/2012 “Cognitive Psychology in
Service of Retail”, dunnhumby corporation, London, UK.
9/2012 “Linking Brain,
Behaviour, and Computation in Category Learning”, Center for Cognitive Neuroscience.
University of Pennsylvania.
8/2012 Talks at National
Taiwan University of Science and Technology (NTUST), Taipei, Taiwan, and National
Central University (NCU), Jhongli City, Taiwan.
8/2012 Invited symposium, “Thirty
years of Marr's Vision: Levels of Analysis in Cognitive Science “, Annual
Meeting of the Cognitive Science Society, Sapporo, Japan.
6/2012 “Boosting Executive
Function through Video Game Training”, Cognitive Control and Associative Learning
workshop, Exeter, UK.
4/2012 “Linking Brain, Behaviour,
and Computation in Category Learning”, Swansea University.
3/2012 “Linking Brain, Behaviour,
and Computation in Category Learning”, Wellcome Functional Imaging Laboratory,
UCL.
3/2012 “Linking
Brain, Behaviour, and Computation in Category Learning”, University of Oxford.
3/2012 “Linking Brain, Behaviour,
and Computation in Category Learning”, University of Warwick.
2/2012 “Linking Brain, Behaviour,
and Computation in Category Learning”, Birkbeck, University of London.
12/2011 “Learning
the exception to the rule,” Department of Linguistics, University of Texas at
Austin
4/2011 Panellist, “Sustainable
Design Symposium 2011,” hosted by Kate Catterall.
2/2011 “The
Memory and Attention Interface,” Brown University.
2/2011 “Attention as a Consequence
of Dynamic Decision Making,” UNSW.
1/2011 “Attention as a Consequence
of Dynamic Decision Making,” UCL.
11/2010 “Looking
to Learn, Learning to Look: Attention Emerges from Cost Sensitive Information
Sampling”, Workshop on Persistent & Generative Cognitive Models, funded and
hosted by Air Force Research Laboratory (Mesa, AZ).
5/2010 “When Short- and Long-Term
Rewards Conflict,” Cognitive Science Program, Simon Fraser University.
3/2010 “Putting the Pieces
Together: Contributions and Interactions of Various Learning Systems,” University
of Iowa.
10/2009 “The
Bayesian Program as Progeny of Evolutionary Psychology and Behaviorism,” CDS
Pre-Conference talk, sponsored by the DELTA center and organized by John
Spencer.
8/2009 “The
not so abstract nature of concepts, rules, and grammar,” address to Max Planck
Institute for Psycholinguistics (Nijmegen, NL).
8/2009 “Connectionist
Perspectives on the Development of Category Learning Abilities,” development
and modelling symposium organized by Maartje Raijmakers, Amsterdam, The
Netherlands.
11/2008 “Category
Learning by Clustering with Extension to Dynamic Environments,” AFOSR Cognition
& Decision Program Workshop, Washington, D.C. Hosted by Jun Zhang.
8/2008 “Where
do we get new research ideas?” Connecting probabilistic models of cognition and
neural networks workshop, Hosted by Tom Griffiths and Jay McClelland, Berkeley,
CA.
6/2008 “The Role of Initial Conditions
in Concept Organization,” Concept Modelling Workshop, University of Lueven, Belgium.
5/2008 “Using Mechanistic
(non-rational) Models of Learning to Link Behavior, Brain, and Body,” Keynote,
Perceptual Expertise Network (PEN) Workshop XVI in Banff, Canada.
5/2008 “Using Mechanistic (non-rational)
Models of Learning to Link Behavior, Brain, and Body,” Department of Psychology,
Ohio State.
12/2007 “Anticipating
Information Needs: Adaptive Display in Dynamic Environments,” Sustaining
Performance Under Stress Symposium, Center for Strategic and Innovative
Technologies, Austin, TX.
9/2007 “Human Inference
Mechanisms,” Cowles Foundation for Research in Economics, Yale University, workshop
on "Analogies, Rules, and Probabilities."
3/2007 “Learning by Example
with Extension to Dynamic Environments,” AFOSR Cognition & Decision Program
Workshop, Washington, D.C.
2/2007 “The Emergence of
Multiple Learning Systems,” University of Arizona.
2/2007 “Putting the Psychology
Back Into Psychological Models,” AFOSR sponsored workshop in Dynamic Decision
Making, Dayton, OH.
11/2006 “The
Emergence of Multiple Learning Systems,” University of Louisiana.
7/2006 “The Emergence of Multiple
Learning Systems,” ICOM, Sydney, Australia.
7/2006 “Models
in Search of a Brain,” workshop, Margaret River, Australia.
6/2006 “The
Emergence of Multiple Learning Systems,” UWA, Australia.
4/2006 “The Emergence of Multiple
Learning Systems,” AFOSR Cognition & Decision Program Workshop, Dayton, OH.
4/2006 “The Emergence of Multiple
Learning Systems,” APA Convention Invited Division 3 speaker, New Orleans, LA.
10/2005 Speaker/Symposium
Organizer, “The Cognitive Neuroscience of Category Learning,” at the Computational
Cognitive Neuroscience Conference, Washington, D.C.
9/2005 “Acquiring Knowledge One
Cluster at a Time,” Department of Psychology, New York University, NYC.
7/2005 “Exemplar-based
relational category learning,” Annual Summer Interdisciplinary Conference (ASIC)
2005, Briançon, France.
6/2005 Workshop Participant,
NSF sponsored “Dynamical and Connectionist Accounts of Development,” University
of Iowa, organized by John Spencer and Jay McClelland.
5/2005 “A Clustering Account of
Human Categorization,” Department of Psychology, University of Sydney, Australia.
4/2005 “Cluster-based Modeling
of Human Learning: Joint Influences of Task and Environment,” AFOSR Perception
& Cognition Program Workshop, St. Augustine, FL.
4/2005 “Environment and goals
jointly direct category acquisition,” Department of Psychology, Texas A&M, College
Station, TX.
2/2005 Keynote speaker for Lake
Ontario Visionary Establishment Conference.
2/2005 “Beyond common features: The
role of roles in determining similarity. ” Department of Psychology, The University
of Western Ontario.
1/2005 “Clustering
Account of Human Learning” Department of Psychology, Stanford University.
1/2005 “Clustering Account of Human
Learning” Department of Psychology, UCSD.
10/2004 “Bridging Levels: A Cognitive
Model of Hippocampal Mediated Learning,” J. S. McDonnell
Foundation meeting on the cognitive neuroscience of category learning, New York
City, NY.
9/2004 “Bridging Levels: A
Cognitive Model of Hippocampal Mediated Learning” Department of Communication
Sciences and Disorders, The University of Texas at Austin.
6/2004 “Infants, amnesiacs,
aging, and the MTL,” Annual Summer Interdisciplinary Conference (ASIC) 2004, Dolomiti,
Italy.
3/2004 “A Clustering Account of
Human Learning,” AFOSR Perception & Cognition Program Workshop, Phoenix, AZ.
2/2004 “Human Learning, Memory,
and the Categories in and Imposed on Our World,” UT Odyssey lecture, Austin,
TX.
1/2004 “A Clustering Account of
Human Category Learning,” Caltech, Computation and Neural Systems, Pasadena,
CA.
11/2003 “Infants,
Amnesiacs, and the MTL,” ARMADILLO, Texas A&M, College Station, TX.
9/2003 “Category Learning in
Infants and Amnesiacs,” J. S. McDonnell Foundation meeting on the cognitive
neuroscience of category learning, New York City, NY.
6/2003 “The influence of culture
on conceptual organization,” talk given at a conference to honour Douglas Medin,
Chicago Botanical Gardens, Chicago, IL.
9/2002 “Two systems or just one,”
J. S. McDonnell Foundation meeting on the cognitive neuroscience of category
learning, New York City, NY.
8/2002 Invited Discussant, AMBR
symposium at the Cognitive Science Society Conference, Washington, D.C.
11/2001 “Aging
effects in category learning,” Mind, Brain, & Behavior Forum Series, Harvard
University, Cambridge, MA.
7/2001 “Inference and classification
learning: Data and models,” ICOM-3: Third International Conference on Memory.
Valencia, Spain.
11/2000 “Modeling
Human Category Learning,” Forum for Artificial Intelligence, Department of
Computer Science, The University of Texas at Austin, Austin, TX.
10/2000 “Inference
and Classification Learning,” Association for Research in Memory, Attention,
Decision-making, Intelligence, Language, Learning& Organization
(ARMADILLO), Texas A&M, College Station, TX.
2/1999 “SUSTAIN: A Clustering
Account of Category Learning,” Psychology Department, Columbia University, New
York City, NY.