Love Lab

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

Funded by

Tweets

View on Twitter

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 Love Lab consists of a lab manager, 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.

Lab Manager

Amna Ali
Amna Ali

My research interests focus on face perception, visual attention and decision making. More specifically, I am interested in how humans form first impressions, visual attentional patterns and rank-based decision making with an emphasis on brain imaging analysis techniques. My most recent project studied the effects of choice judgments on the brain, using multivariate representational similarity models to see patterns of face-selection when forming first impressions. In the past, I have also worked on drug testing and how different hormones affect the brain. My interests in cognitive neuroscience expand over fMRI, MEG and eye-tracking experiments.

Postdocs

Christiane Ahlheim
Christiane Ahlheim

I aim to understand how we flexibly combine information to organise our experiences and which brain areas are involved in this process. To this end, I use behavioural as well as functional neuroimaging data and combine multivariate with explorative data analysis techniques to derive an estimate of the dimensionality of the neural code.

Kurt Braunlich
Kurt Braunlich

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

Johan Carlin
Johan Carlin

I am interested in the cortical representation of high-level visual percepts, and in how attention, learning and memory modulate perceptual representations to support flexible behaviour. To this end, I use multivariate representational similarity approaches to compare the predictions of computational models to data from functional MRI, eye-tracking and psychophysics experiments.

Olivia Guest
Olivia Guest

My research involves computational modelling of categorisation and semantic memory. I am interested in the representation of categories and concepts in healthy participants, patient groups, infants and children, and animal models. I make use of shallow and deep artificial neural networks in order to compare and contrast such models with empirical results.

Robert Mok
Robert Mok

I am interested in how the brain constructs our perception of the world and how we can flexibly adapt to perform our goals. My current research focusses on category learning in the brain using fMRI (including multivariate pattern analysis) 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.

Takao Noguchi
Takao Noguchi

My research is focused on knowledge representation and psychological processes of judgement and decision making. As a behavioural scientist by training, I take quantitative approaches to understand human behaviour.

PhD Students

Adam Hornsby
Adam Hornsby

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

Lukasz Kopec
Lukasz Kopec

I am a current PhD student in the lab. My general academic interests span many areas in cognitive science and more specifically decision making. I study decision making in various contexts. An example may be a psychiatrist predicting whether their patient will become violent. I take a data-driven approach, in which I look at models of memory and retrieval, thus linking previous training and experience to different decisions. I analyse models of human category learning and optimise the training given to humans to achieve the best possible outcome (whether highest accuracy, or easiest training). I am currently researching human limitations in memory, through building exemplar-based models of category learning. I’ve graduated from Cognitive Science at Edinburgh University, where I studied a model of how infants segment fluent speech into discrete words. In the past I’ve also worked in data analytics using large-scale text analysis.

Katie Parker
Katie Parker

My work is focused on understanding the cognitive biases and limitations involved in human probabilistic judgment and choice. I am interested in the effects of different informational frames and formats on decision making where numerical and statistical data is involved. Applications of my work include systems and interface recommendations to support professional forecasting and monitoring judgments, and consumer decision making in health, well being and financial contexts.

Paula Parpart
Paula Parpart

My PhD is about the relationship between simple decision making heuristics (e.g., fast and frugal heuristics) and rational models of cognition, i.e., Bayesian inference models. I am interested in showing that these two opposing approaches to cognition are in fact compatible and can be integrated in one overarching approach. I make use of several common machine learning techniques like ridge regression to characterize the formal mathematical relationship between simple heuristics and more traditional regression approaches.

Peter Sebastian Riefer
Peter Sebastian Riefer

In my projects, I’m trying to understand human decision-making, particularly in economic contexts. On the one hand, I’m working on a project about choice exploration and exploitation. The focus here is on how people sample available options in non-stationary environments. On the other hand, I concern myself with cooperation and coordination of people in large groups.

Sebastian Bobadilla Suarez
Sebastian Bobadilla Suarez

My work focuses on the cognitive mechanisms that are necessary for the implementation of “fast and frugal” decision making heuristics. I am specifically interested in the role of attentional control when applying two popular heuristics known as Tallying and Take-the-Best. The end goal is to use neuroscientific techniques to characterize these mechanisms and explain them in terms of a computational model.

Press

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

See the Publications section for more details.

Publications

[ link to pdf ] Parpart, P., Jones, M. & Love, B.C. (2017). Heuristics as Bayesian inference under extreme priors. PsyArXiv (preprint), DOI: https://dx.doi.org/10.17605/OSF.IO/QKBT5

[ pdf ] Mack, M.L., Preston, A.R. & Love, B.C. (2017). Medial prefrontal cortex compresses concept representations through learning. Pattern Recognition in Neuroimaging, DOI: https://doi.org/10.1109/PRNI.2017.7981500 (full paper forthcoming, preprint here)

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

[ pdf ] 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, 3880-16; DOI: https://doi.org/10.1523/JNEUROSCI.3880-16.2017

[ pdf ] Bobadilla-Suarez, S., & Love, B.C. (in press). Fast or Frugal, but not both: Decision Heuristics under Time Pressure. Journal of Experimental Psychology: Learning, Memory, and Cognition.

[ pdf ] Palmeri, T.J., Love, B.C., & Turner, B.M. (2017). Model-based cognitive neuroscience. Journal of Mathematical Psychology, 76, 59-64.

[ pdf ] 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.

[ link to pdf ] Guest, O., Love, B.C. (2017). What the Success of Brain Imaging Implies about the Neural Code. eLife,6:e21397.

[ pdf ] Riefer, P.S., Prior, R., Blair, N., Pavey, G., & Love, B.C. (2017). Coherency Maximizing Exploration in the Supermarket. Nature Human Behaviour. doi: 10.1038/s41562-016-0017.

[ pdf ] Spiers, H.J., Love, B.C., Le Pelley, M.E., Gibb, C.E., & Murphy, R.A. (2016). Anterior Temporal Lobe Tracks the Formation of Prejudice. Journal of Cognitive Neuroscience, doi: 10.1162/jocn_a_01056.

[ pdf ] 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), 113(46), 13203–13208.

[ pdf ] Love, B.C. (2016). Cognitive Models as Bridge between Brain and Behavior. Trends in Cognitive Science (TiCS). 20, 4, 247-248. http://dx.doi.org/10.1016/j.tics.2016.02.006

[ pdf ] 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, 3, 284-297. http://dx.doi.org/10.1037/xge0000133

[ link ] Love, B.C. (2016). Will AI spell the end of humanity? The tech industry wants you to think so. The Register (via The Conversation).

[ link to pdf ] Love, B.C., Kopec, L., & Guest, O. (2015). Optimism Bias in Fans and Sports Reporters. PLOS ONE.

[ pdf ] Riefer, P.S., & Love, B.C. (2015). Unfazed by Both the Bull and Bear: Strategic Exploration in Dynamic Environments. Games, 6, 251-261.

[ pdf ] Newall, P., & Love, B.C. (2015). Nudging Investors Big and Small Toward Better Decisions. Decision,2(4), 319-326.

[ pdf ] Blanco, N.J., Love, B.C., Cooper, J.A., McGeary, J.E., Knopik, V.S., & Maddox, W.T. (in press). A Frontal Dopamine System for Reflective Exploratory Behavior. Neurobiology of Learning and Memory.

[ link, IFL repost ] Love, B.C. (7-May 2015). Gaming improves your brain power - reality or hype? The Conversation.

[ pdf ] Parpart, P., Schulz, E., Speekenbrink, M., & Love, B.C. (2015). Active learning as a means to distinguish among prominent decision strategies. Proceedings of the Cognitive Science Society.

[ pdf ] Love, B.C. (2015). Concepts, Meaning, and Conceptual Relationships. In The Oxford Handbook of Cognitive Science. Ed. S. Chipman. DOI: 10.1093/oxfordhb/9780199842193.013.12

[ pdf ] Love, B.C. (2015). The Algorithmic Level is the Bridge Between Computation and Brain. Topics in Cognitive Science, 7, 230-242.

[ pdf ] Patil, K., Zhu, X., Kopec, L., & Love, B.C. (2014). Optimal Teaching for Limited-Capacity Human Learners. In Advances in Neural Information Processing Systems (NIPS). Spotlight presentation. (raw data and teaching sets)

[ pdf ] Gureckis, T.M., & Love, B.C. (in press). Computational Reinforcement Learning. Oxford Handbook of Computational and Mathematical Psychology (Eds. J.R. Busemeyer, J.T. Townsend, Z.J. Wang, A Eidels). Oxford University Press.

[ pdf ] Hornsby, A.N. & Love, B.C. (2014). Improved Classification of Mammograms Following Idealized Training. Journal of Applied Research in Memory and Cognition, 3, 72-76. DOI: 10.1016/j.jarmac.2014.04.009

[ pdf ] 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.

[ pdf ] Anderson, R.O., Love, B.C. & Tsai, M.J. (2014). Neuroscience Perspectives For Science And Mathematics Learning In Technology-Enhanced Learning Environments (intro to special issue). International Journal of Science and Mathematics Education,12,467-474.

[ pdf ] 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, 14,1167-1183.

[ pdf ] Ramscar, M., Hendrix, P., Love, B. C. & Baayen, H. (2013). Learning is not decline: The mental lexicon as a window into cognition across the lifespan. The Mental Lexicon, 8(3), 450-481.

[ pdf ] Mack, M.L., Preston, A.R. & Love, B.C. (2013). Decoding the Brain's Algorithm for Categorization from its Neural Implementation. Current Biology, 23, 2023-2027. supplemental

[ pdf ] 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, 563-568.

[ link to pdf ] 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.

[ link to pdf ] Giguère, G. & Love, B.C. (2013). Limits in decision making arise from limits in memory retrieval. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 110 (19), 7613-7618.

[ pdf ] Love, B.C. (2013). Category Learning, Computational Perspectives. In Hal Pashler (Ed.), Encyclopedia of the Mind, Sage.

[ pdf ] Love, B. C. (2013). Grounding quantum probability in psychological mechanism. Behavioral and Brain Sciences, 36, 296.

[ pdf ] Love, B.C. (2013). Categorization. In K.N. Ochsner and S.M. Kosslyn (Eds.) Oxford Handbook of Cognitive Neuroscience, 342-358. Oxford Press.

[ pdf ] Sanders, M., Davis, T., & Love, B.C. (2013). Are Better Examples Beautiful or Are Beautiful Examples Better? Exploring the Relationship Between Beauty and Category Structure. Psychonomic Bulletin & Review, 20, 566-573.

[ pdf ] Love, B.C., & Jones, M. (2012). Bayesian Learning. In B. Seel (Ed.), Encyclopedia of the Sciences of Learning. Springer.

[ ... ] Dixit, V.V., Alsup, R.M., Waller, S.T., Love, B.C., & Tomlinson, M.T. (2012). A Static Model for Predicting Disrupted Network Behaviour. In Proceedings of the 17th International Conference of Hong Kong Society for Transportation Studies (HKSTS), 17, 3-10.

[ pdf ] 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.

[ pdf ] Davis, T., Love, B.C., & Maddox, W.T. (2012). Age-related Declines in the Fidelity of Newly Acquired Category Representations. Learning & Memory, 19, 325-329. (supplement).

[ pdf ] 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, 38,821-839. (special issue on Theory and data in categorization: Integrating computational, behavioral, and cognitive neuroscience approaches).

[ link to pdf ] 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,2,398. doi: 10.3389/fpsyg.2011.00398

[ pdf ] 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.

[ pdf ] Davis, T., Love, B.C., & Preston, A.R. (2012). Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members. Cerebral Cortex, 22, 260-273. (supplement).

[ pdf ] 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, 34, 169-231. (target article, commentaries, response).

[ pdf ] 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 (target article).

[ pdf ] Jones, M. & Love, B.C. (2011). Pinning Down the Theoretical Commitments of Bayesian Cognitive Models. Behavioral and Brain Sciences (response to commentaries).

[ pdf ] 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, 156-170.

[ pdf ] Tomlinson, M.T., & Love, B.C. (2010). When Learning to Classify by Relations Is Easier Than by Features. Thinking & Reasoning,16, 372-401.

[ pdf ] Love, B. C., & Tomlinson, M. (2010). Mechanistic Models of Associative and Rule-based Category Learning. In Denis Mareschal, Paul Quinn, Stephen Lea (Eds.), The Making of Human Concepts. Oxford, UK: Oxford University Press.

[ pdf ] 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. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Sakamoto, Y., & Love, B.C. (2010). Learning and Retention through Predictive Inference and Classification. Journal of Experimental Psychology: Applied, 16, 361-377.

[ pdf ] Otto, A.R., Gureckis, T.M., Markman, A.B., & Love, B.C. (2010). Regulatory Fit and Systematic Exploration in a Dynamic Decision-Making Environment. Journal of Experimental Psychology: Learning, Memory, & Cognition,36(3), 797-804.

[ pdf ] 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, 1-10.

[ pdf ] Davis, T., & Love, B.C. (2010). Memory for Category Information is Idealized through Contrast with Competing Options. Psychological Science, 21, 234-242. (supplemental)

[ pdf ] Gureckis, T. M., & Love, B. C. (2010). Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior. Cognitive Science, 34, 10-50.

[ pdf ] Tomlinson, M.T., Howe, M., Love, B.C. (2009). Seeing the World Through an Expert's Eyes: Context-Aware Display as a Training Companion. Proceedings of HCI International, LNAI 5638, 668-677.

[ pdf ] 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 ACM SIGCHI Conf. on Human Factors in Computing Systems (CHI 2009), 1351-1360.

[ pdf ] Sakamoto, Y,. & Love, B.C. (2009). You Only Had to Ask Me Once:Long-term Retention Requires Direct Queries During Learning. Proceedings of the Annual Meeting of Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Otto, A.R., Gureckis, T.M., Markman, A.B., & Love, B.C. (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. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] 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, 957-963.

[ pdf] Davis, T., Love, B.C., Maddox, T.M. (2009). Anticipatory Emotions in Decision Tasks: Covert Markers of Value or Attentional Processes? Cognition, 112, 195-200.

[ pdf ] Davis, T., Love, B.C., & Maddox, W.T. (2009). Two Pathways to Stimulus Encoding in Category Learning? Memory & Cognition, 37, 394-413.

[ pdf ] Gureckis, T. M., & Love, B. C. (2009). Short-term gains, long-term pains: how cues about state aid learning in dynamic environments. Cognition, 113, 293-313.

[ pdf ] Gureckis, T. M., & Love, B. C. (2009). Learning in Noise: Dynamic Decision-Making in a Variable Environment. Journal of Mathematical Psychology, 150, 180-193.

[ pdf ] Love, B. C., Jones, M., Tomlinson, M.T., & Howe, M. (2008). Predicting Information Needs: Adaptive Display in Dynamic Environments . Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Davis, T., & Love, B. C. (2008). How Goals Shape Category Acquisition: The Role of Contrasting Categories . Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] 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, 578-589.

[ pdf ] Tomlinson, M.T., & Love, B. C. (2008). Monkey see, monkey do: Learning relations through concrete examples. Behavioral and Brain Sciences, 31, 150-151.

[ pdf ] Love, B.C., Tomlinson, M., & Gureckis, T.M. (2008). The concrete substrates of abstract rule use. In B.H. Ross, The Psychology of Learning and Motivation, 49, 167-207.

[ pdf ] Love, B.C. (2008). Prediction Markets are only Human: Subadditivity in Probability Judgments

[ pdf ] Sakamoto, Y., Jones, M., & Love, B. C. (2008). Putting the Psychology Back into Psychological Models: Mechanistic vs. Rational Approaches. Memory & Cognition, 36, 1057-1065.
NOTE: Click here for data from Experiment 2.

[ pdf ] Love, B. C., & Gureckis, T. M. (2007). Models in search of a brain. Cognitive, Affective, & Behavioral Neuroscience.,7, 90-108.

[ pdf ] Gureckis, T. M., & Love, B. C. (2007). Behaviorism Reborn? Statistical Learning as Simple Conditioning . Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Davis, T., Love, B. C., & Maddox, W.T. (2007). Translating From Perceptual to Cognitive Coding. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Tomlinson, M., & Love, B. C. (2007). Relation-Based Categories are Easier to Learn than Feature-Based Categories. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Rein, J. R., Love, B. C., & Markman, A. B. (2007). Feature Relations and Feature Salience in Natural Categories. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Jones, M., & Love, B. C. (2007). Beyond common features: The role of roles in determining similarity. Cognitive Psychology, 55, 196-231.

[ pdf ] Sakamoto, Y., & Love, B. C. (2006). Vancouver, Toronto, Montreal, Austin: Enhanced oddball memory through differentiation, not isolation. Psychonomic Bulletin & Review, 13, 474-479.

[ pdf ] Jones, M., Love, B. C., & Maddox, W. T. (2006). Recency as a window to generalization: Separating decisional and perceptual sequential effects in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 316-332.

[ pdf ] Gureckis, T. M., & Love, B. C. (2006). Bridging levels: Using a cognitive model to connect brain and behavior in category learning. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Love, B. C., & Jones, M. (2006). The emergence of multiple learning systems. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Sakamoto, Y., & Love, B. C. (2006). Sizable sharks swim swiftly: Learning correlations through inference in a classroom setting. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Sakamoto, Y., Love, B. C., & Jones, M. (2006). Tracking variability in learning: Contrasting statistical and similarity-based accounts. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Jones, M., Maddox, W. T., & Love, B. C. (2006). The role of similarity in generalization. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Tomlinson, M., & Love, B. C. (2006). Learning abstract relations through analogy to concrete exemplars. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Tomlinson, M. T., & Love, B. C. (2006). From pigeons to humans: Grounding relational learning in concrete examples. Twenty-First National Conference on Artificial Intelligence (AAAI-2006), USA, 17, 136-141.

[ pdf ] Love, B. C. (2005). Environment and goals jointly direct category acquisition. Current Directions in Psychological Science, 14, 195-199.

[ pdf ] Love, B. C. (2005). In vivo or in vitro: Cognitive architectures and task-specific models. In R. W. Pew and K. A. Gluck, Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation. 351-364. Mahwah, NJ: Lawrence Erlbaum.

[ pdf ] Love, B. C., & Gureckis, T. M. (2005). Modeling learning under the influence of culture. In W. Ahn, R. L., Goldstone, B. C., Love, A. B., Markman, & P. Wolff (Eds.), Categorization inside and outside of the lab: Festschrift in Honor of Douglas L. Medin. 229-248. Washington, DC: American Psychological Association.

[ pdf ] Gureckis, T. M., & Love, B. C. (2005). A critical look at the mechanisms underlying implicit sequence learning. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Jones, M., Maddox, W. T., & Love, B. C. (2005). Stimulus generalization in category learning. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Sakamoto, Y., & Love, B. C. (2005). A novel approach to understanding novelty effects in memory. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ uspto ] Love, B. C. (2005). Love, B. C. (2005). Method and apparatus for incorporating decision making into classifiers. US Patent #6,920,439.

[ pdf ]Ahn, W., Goldstone, R. L., Love, B. C., Markman, A. B., & Wolff, P. (Eds.). (2005). Categorization inside and outside of the lab: Festschrift in Honor of Douglas L. Medin. Washington, DC: American Psychological Association.

[ pdf] Sakamoto, Y., & Love, B. C. (2004). Schematic influences on category learning and recognition memory. Journal of Experimental Psychology: General, 133, 534-553.

[ pdf ] Jones, M. & Love, B. C. (2004). Beyond common features: The role of roles in determining similarity. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Sakamoto, Y., & Love, B. C. (2004). Type/token information in category learning and recognition. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Love, B. C., & Gureckis, T. M. (2004). The hippocampus: Where a cognitive model meets cognitive neuroscience. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf] Sakamoto, Y., Matuska, T., & Love, B. C. (2004). Dimension-wide vs. exemplar-specific attention in category learning and recognition. In M. Lovett, C. Schunn, C. Lebiere, and P. Munro (Eds.), Proceedings of the International Conference of Cognitive Modeling (pp. 261-266). Mahwah, New Jersey: Lawrence Erlbaum.

[ pdf ] Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332.

[ pdf ] Gureckis, T. M., & Love, B. C. (2004). Common mechanisms in infant and adult category learning. Infancy, 5, 173-198.

[ pdf ] Larkey, L. B., & Love, B. C. (2003). CAB: Connectionist analogy builder. Cognitive Science, 27, 781-794.

[ pdf ] Sakamoto, Y., & Love, B. C. (2003). Category structure and recognition memory. Proceedings of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum Associates.

[ pdf ] Gureckis, T. M., & Love, B. C. (2003). Human unsupervised and supervised learning as a quantitative distinction. International Journal of Pattern Recognition and Artificial Intelligence, 17, 885-901.

[ pdf ] Love, B. C., & Markman, A. B. (2003). The nonindependence of stimulus properties in human category learning. Memory & Cognition, 31, 790-799.
NOTE: Learning criterion was 10 trials correct in a row. In Table 2, the column heading to the left of "Type IV" should read "Shape Relevant"

[ pdf ] Love, B. C. (2003). The multifaceted nature of unsupervised category learning. Psychonomic Bulletin & Review, 10, 190-197.

[ pdf ] Gureckis, T. M., & Love, B. C. (2003). Towards a unified account of supervised and unsupervised learning. Journal of Experimental and Theoretical Artifical Intelligence, 15, 1-24.

[ pdf ] Love, B. C. (2002). Comparing supervised and unsupervised category learning. Psychonomic Bulletin & Review, 9, 829-835.

[ pdf ]Gureckis, T. M., & Love, B. C. (2002). Modeling unsupervised learning with SUSTAIN. In S. Haller & G. Simmons (Eds.), Proceedings of the 15th international Florida artificial intelligence research society conference (p. 163-167). Menlo Park, California: AAAI Press.

[ pdf ] Gureckis, T. M., and Love, B. C. (2002). Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions. Proceedings of the Cognitive Science Society, USA, 24, 399-404.

[ pdf ] Love, B. C. (2003). Concept learning. In L. Nadel (Ed.), The Encyclopedia of Cognitive Science (Vol. 1, p. 646-652), London: Nature Publishing Group.

[ pdf ] Love, B. C. (2002). Similarity and Categorization: A review [Review of the book Similarity and Cognition] AI Magazine, 23, 103-105.

[ pdf ] Love, B. C. (2002). Three deadly sins of category learning modelers. Behavioral and Brain Sciences, 24, 687-688.

[ pdf ] Love, B. C. (2001). Uncovering analogy [Review of the book The Analogical Mind]. Trends in Cognitive Sciences, 5, 454-455.

[ pdf ] Yamauchi, T., Love, B. C., & Markman, A. B. (2002). Learning non-linearly separable categories by inference and classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 585-593.

[ pdf ] Love, B. C., & Markman, A. B., & Yamauchi, T. (2000). Modeling classification and inference learning. Seventeenth National Conference on Artificial Intelligence (AAAI-2000), USA, 17, 136-141.

[ pdf ] Love, B. C. (2000). A computational level theory of similarity. Proceedings of the Cognitive Science Society, USA, 22, 316-321.

[ pdf ] Love, B. C. (2000). Learning at different levels of abstraction. Proceedings of the Cognitive Science Society, USA, 22, 800-805.

[ pdf ] Love, B. C., Rouder, J. N., & Wisniewski, E. J. (1999). A structural account of global and local processing. Cognitive Psychology, 38, 291-316.

[ pdf ] Love, B. C. (1999). Utilizing time: Asynchronous Binding. Advances in Neural Information Processing Systems, 11, 38-44.

[ pdf ] 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), USA, 15, 671-676.

[ pdf ] Love, B. C., & Medin, D. L. (1998). Modeling item and category learning. Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, USA, 20, 639-644.

[ pdf ] Sloman, S. A., Love, B. C., & Ahn, W. K. (1998). Feature centrality and conceptual coherence. Cognitive Science, 22, 189-228.

[ pdf ] Wisniewski, E. J., & Love, B. C. (1998). Relations versus properties in conceptual combination. Journal of Memory and Language, 38, 177-202.

[ pdf ] Love, B. C. (1996). Mutability, conceptual transformation, and context. Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, USA, 18, 459-463.

[ pdf ] 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, USA, 17, 654-659.

Tweets

View on Twitter

Funded by