In press
Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Mayer, L., & Smyth, P. (in press). Bridging the Gap Between What Large Language Models Know and What People Think They Know. Nature Machine Intelligence. [pdf][supporting information][data and code]
2024
Steyvers, M. & Kumar, A. (2024). Three challenges for AI-Assisted Decision-Making. Perspectives on Psychological Science, 19(5), 722-734. [pdf]
Bower, A.H., Han, N., Eckstein, M.P., & Steyvers, M. (2024). How experts and novices judge other people’s knowledgeability. Psychonomic Bulletin & Review, 31, 1627-1637. [pdf]
Groneau, Q.F., Steyvers, M., & Brown, S. (2024). How Do You Know That You Don’t Know? Cognitive Systems Research, 86, 101232. [pdf]
Wang, X., Zhu, W., Saxon, M., Steyvers, M., Wan, W.Y. (2024). Large Language Models Are Implicitly Topic Models: Explaining and Finding Good Demonstrations for In-Context Learning. Advances in Neural Information Processing Systems (NeurIPS), 36 [pdf][supplementary materials]
Showalter, S., Boyd, A., Smyth, P., & Steyvers, M. (2024). Bayesian Online Learning for Consensus Prediction. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 238. [pdf]
Hu, X., Akash, K., Mehrota, S., Misu, T., & Steyvers, M. (2024). Prosocial Acts Towards AI Shaped By Reciprocation And Awareness. Proceedings of the Annual Meeting of the Cognitive Science Society, 46, 2270-2277. [pdf]
Karny, S., Mayer, L.W., Ayoub, J., Tian, D., Song, M., Moradi-Pari, E., Su, H., & Steyvers, M. (2024). Learning with AI Assistance: A Path to Better Task Performance or Dependence? ACM’s 2024 Collective Intelligence Conference, Boston, MA, pp 10-17. [pdf]
Liu, S., & Steyvers, M. (2024). Combining Human and AI Strengths in Object Counting under Information Asymmetry. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 12(1), 86-94. [pdf]
Belem, C.G., Kelly, M., Steyvers, M., Singh, S., & Smyth, P. (2024). Perceptions of Linguistic Uncertainty by Language Models and Humans. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 8467–8502 [pdf]
2023
Kumar, A., Smyth, P., & Steyvers, M. (2023). Differentiating mental models of self and others: A hierarchical framework for knowledge assessment. Psychological Review, 130(6), 1566–1591 [pdf][data and code]
Kumar, A., Tejeda, H., & Steyvers, M. (2023). How Displaying AI Confidence Affects Reliance and Hybrid Human-AI Performance. Hybrid Human Artificial Intelligence (HHAI), 368, 234-242. [pdf]
Kumar, A., Akash, K., Mehrota, S., Misu, T., & Steyvers, M. (2023). When Do Drivers Intervene In Autonomous Driving? Contrasting Drivers’ Perceived Risk Across Two Mobility Types. ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-305. ACM Digital Library. [pdf]
Kelly, M., Kumar, A. Smyth, M., & Steyvers, M. (2023). Capturing humans’ mental models of AI: an item response theory approach. ACM Conference on Fairness, Accountability, and Transparency. [pdf]
Robinson, M. M., & Steyvers, M. (2023). Linking computational models of two core tasks of cognitive control. Psychological Review, 130(1), 71–101. [pdf][data and code]
Benjamin, D.M., Morstatter, F., Abbas, A.E., Abeliuk, A., Atanasov, P., Bennett, S., Beger, A., Birari, S., Budescu, D.V., Catasta, M., Ferrara, E., Haravitch, L., Himmelstein, M., Hossain, T., Yuzhong, H., Joseph, R., Leskovec, J., Matsui, J., Mirtaheri, M., Satyukov, G., Sethi, R., Singh, A., Sosic, R., Steyvers, M., Szekely, P.A., Ward, M.D., Galstyan, A. (2023). Hybrid Forecasting of Geopolitical Events. AI Magazine, 44, 112-128. [pdf]
Karayanidis, F., Hawkins, G.E., Wong, A.S.W., Aziz, F., Hunter, M., & Steyvers, M. (2023). Jointly modelling behavioural and EEG measures of proactive control in task-switching. Psychophysiology, 60, e14241. [pdf]
Kumar, A., & Steyvers, M. (2023). Help me help you: A computational model for goal inference and action planning. Proceedings of the Annual Meeting of the Cognitive Science Society, 45(45), pp. 486-492. Austin, TX: Cognitive Science Society. [pdf]
Moskvichev, A., Tikhonov, R., & Steyvers, M. (2023). Teaching Categories via Examples and Explanations. Cognition, 238, 105511 [pdf]
Das, P., & Steyvers, M. (2023). Older Adults Catch Up to Younger Adults on Cognitive Tasks After Extended Training. Collabra: Psychology 9(1): 88156. [pdf]
2022
Tejeda, H., Kumar, A., Smyth, S., & Steyvers, M. (2022). AI-Assisted Decision-Making: A Cognitive Modeling Approach to Infer Latent Reliance Strategies. Computational Brain and Behavior, 5, 491–508. [pdf][data and code]
Barsever, D., Steyvers, M., Neftci, E. (2022). Building and Benchmarking the Motivated Deception Corpus: Improving the Quality of Deceptive Text Through Gaming. Behavior Research Methods. [pdf]
Steyvers, M., Tejeda, H., Kerrigan, G., & Smyth, P. (2022). Bayesian Modeling of Human-AI Complementarity. Proceedings of the National Academy of Sciences, 119(11), e2111547119, 1-7. [pdf][supporting information][data and code]
Kumar, A., Benjamin, A.S., Heathcote, A., Steyvers, M. (2022). Comparing models of learning and relearning in large-scale cognitive training data sets. npj Science of Learning, 7:24. [pdf][data and code]
Kumar, A., Tejeda, H., & Steyvers, M. (2022). An Empirical Investigation of Reliance on AI-Assistance in a Noisy-Image Classification Task. Hybrid Human Artificial Intelligence (HHAI), 352, 235-237. [pdf]
Bennett, S.T., Steyvers, M. (2022). Leveraging metacognitive ability to improve crowd accuracy via impossible questions. Decision, 9(1), 60-73. [pdf][data]
Kumar, A.A., Steyvers, M., & Balota, D.A. (2022). A Critical Review of Network-based and Distributional Approaches to Semantic Memory Structures and Processes. topiCS, 14, 54-77. [pdf]
2021
Kerrigan, G., Smyth, P., & Steyvers, M. (2021). Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration. Advances in Neural Information Processing Systems (NeurIPS), 35. [pdf]
Bower, A.H. & Steyvers, M. (2021). Perceptions of AI engaging in human expression. Scientific Reports, 11, 21181. [pdf]
Kumar, A., Patel, T., Benjamin, A. & Steyvers, M. (2021). Explaining Algorithm Aversion with Metacognitive Bandits. Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43), pp. 2780-2786. Austin, TX: Cognitive Science Society. [pdf]
Kumar, A., Patel, T., Benjamin, A. & Steyvers, M. (2021). Metacognitive Bandits: When Do Humans Seek AI Assistance? ICRA Workshop on Social Intelligence in Humans and Robots. [pdf]
Bower, A. & Steyvers, M. (2021). The Funny Thing About Algorithm Aversion: Investigating Bias Toward AI Humor. Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43), pp. 3173. Austin, TX: Cognitive Science Society. [pdf]
Kumar, A.A., Steyvers, M., & Balota, D.A. (2021). Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models. Cognitive Science, 45. [pdf]
2020
Kumar, A. A., Balota, D. A., & Steyvers, M. (2020). Distant connectivity and multiple-step priming in large-scale semantic networks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(12), 2261–2276. [pdf]
Steyvers, M., Schafer, R.J. (2020). Inferring Latent Learning Factors in Large-Scale Cognitive Training Data. Nature Human Behaviour, 4, 1145-1155. [pdf][supporting information][data and code]
Ji, D., Logan, R., Smyth, P., & Steyvers, M. (2021). Active Bayesian Assessment of Black-Box Classifiers? Thirty-Fifth AAAI Conference on Artificial Intelligence, 35. [pdf][supplemental]
Ji, D., Smyth, P., & Steyvers, M. (2020). Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference? Advances in Neural Information Processing Systems, 34. [pdf][supplemental]
Zeigenfuse, M.D., Batchelder, W.H., & Steyvers, M. (2020). An Item Response Theory Model of Matching Test Performance. Journal of Mathematical Psychology, 95, 102327. [pdf]
Bennett, M., Mullard, R., Adam, M., Steyvers, M., Brown, S., and Eidels, A. (2020). Going, Going, Gone: Competitive Decision Making in Dutch Auctions. Cognitive Research: Principles and Implications, 5:62. [pdf]
Bower, A.H., & Steyvers, M. (2020). An Aha! Walks into a Bar: Joke Completion as a Form of Insight Problem Solving. In S. Denison, M. Mack, Y. Xu, and B.C. Armstrong (Eds.), Proceedings of the 42th Annual Conference of the Cognitive Science Society, pp. 3034-3040. Austin, TX: Cognitive Science Society. [pdf]
2019
Steyvers, M., Hawkins, G.E., Karayanidis, F., & Brown, S.D. (2019). A large-scale analysis of task switching practice effects across the lifespan. Proceedings of the National Academy of Sciences, 116(36), 17735-17740. [pdf][supporting information][data and code]
Steyvers, M. and Benjamin, A.S. (2019). The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets. Behavior Research Methods, 51(4), 1531-1543. [pdf][code]
Gaut, G., Li, X., Lu, Z.L., & Steyvers, M. (2019). Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model. Human Brain Mapping, 40, 3918-3929. [pdf][data][matlab code]
Gaut, G., Li, X., Turner, B., Cunningham, W.A., Lu, Z.L., & Steyvers, M. (2019). Predicting Task and Subject Differences with Functional Connectivity and BOLD Variability. Brain Connectivity, 9(6), 451-463. [pdf][supplementary][data][code]
Patel, T., Benjamin, A.S., & Steyvers, M. (2019). Monitoring the Ebb and Flow of Attention: Does Controlling the Onset of Stimuli During Encoding Enhance Memory? Memory & Cognition, 47(4), 706-718. [pdf]
Turner, B. M., Forstmann, B. U., and Steyvers, M. (2019). Joint models of neural and behavioral data. Springer: New York. [book site]
Sumner, E., Steyvers, M., & Sarnecka, B.W. (2019). It’s not the treasure, it’s the hunt: Children are more explorative on an explore/exploit task than adults. In Goel, A., Seifert, S., and Freksa, C. (Eds.), Proceedings of the 41th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Ashok, A., Balota, D., & Steyvers, M. (2019). Distant Concept Connectivity in Network-Based and Spatial Word Representations. In Goel, A., Seifert, S., and Freksa, C. (Eds.), Proceedings of the 41th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Moskvichev, A., Tikhonov, R. & Steyvers, M. (2019). A Picture is Worth 7.17 Words: Learning Categories from Examples and Definitions. In Goel, A., Seifert, S., and Freksa, C. (Eds.), Proceedings of the 41th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Bower, A.H., Burton, A., Batchelder, W. & Steyvers, M. (2019). An Insight into Language: Investigating Lexical and Morphological Effects in Compound Remote Associate Problem Solving. In Goel, A., Seifert, S., and Freksa, C. (Eds.), Proceedings of the 41th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Ji, D., Logan, R., Smyth, P., & Steyvers, M. (2019). Bayesian Evaluation of Black Box Classifiers. ICML Workshop on Uncertainty & Robustness in Deep Learning. [pdf]
Moskvichev, A., & Steyvers, M. (2019). Word Games as milestones for NLP research. Fourth Games and Natural Language Processing Workshop (GAMNLP-19). [pdf]
Morstatter, F., Galstyan, A., Satyukov, G. Benjamin, D., Abeliuk, A., Mirtaheri, M., Szekely, P., Ferrara, E., Matsui, A., Steyvers, M., Bennet, S., Budescu, D., Himmelstein, M., Ward, M., Beger, A., Catasta, M., Sosic, R., Leskovec, J., Atanasov, P., Joseph, R., Sethi, R., Abbas, A. (2019). SAGE: A Hybrid Geopolitical Event Forecasting System. International Joint Conference on Artificial Intelligence (IJCAJ). [pdf]
2018
Keuken, M., Maanen, L., Boswijk, M., Forstmann, B., & Steyvers, M. (2018). Large scale structure-function mappings of the human subcortex. Scientific Reports, 8:15854. [pdf]
Molloy, M.F., Bahg, G., Li, X., Steyvers, M., Lu, Z.L., Turner, B.M. (2018). Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data. Computational Brain and Behavior, 1(2), 184-213. [pdf]
Bennett, S.T., Benjamin, A.S., Mistry, P.K., and Steyvers, M. (2018). Making a wiser crowd: Benefits of individual metacognitive control on crowd performance. Computational Brain and Behavior, 1, 90-99. [pdf][data]
Evans, N.J., Steyvers, M. and Brown, S.D. (2018). Modelling the covariance structure of complex data sets using cognitive models: An application to individual differences and the heritability of cognitive ability. Cognitive Science, 42(6), 1925-1944. [pdf]
Palestro, J.J., Bahg, G., Sederberg, P.B., Lu, Z.L., Steyvers, M., & Turner, B.M. (2018). A Tutorial on Joint Models of Neural and Behavioral Measures of Cognition. Journal of Mathematical Psychology, 84, 20-48. [pdf]
Weusthoff, S., Gaut, G., Steyvers, M., Atkins, D.C., Hahlweg, K., Hogand, J., Zimmermanne, T., Fischer, M.S., Baucom, D.H., Georgiou, P., Narayanan, S.,& Baucom, B.R. (2018). The Language of Interpersonal Interaction: An Interdisciplinary Approach to Assessing and Processing Vocal and Speech Data. European Journal of Counselling Psychology, 7(1), 69-85. [pdf]
2017
Bennett, S.T., Benjamin, A.S., & Steyvers, M. (2017). A Bayesian model of knowledge and metacognitive control. In Gunzelmann, G., Howes, A., Tenbrink, T. and Davelaar, E. (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 1623-1628. Austin, TX: Cognitive Science Society. [pdf]
Miller, B., & Steyvers, M. (2017). Leveraging Consistency in Responding within Individuals to Improve Group Accuracy for Rank-Ordering Problems. In Gunzelmann, G., Howes, A., Tenbrink, T. and Davelaar, E. (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society, pp. 793-798. Austin, TX: Cognitive Science Society. [pdf]
Merkle, E.C., Steyvers, M., Mellers, B., & Tetlock, P.E. (2017). A neglected dimension of good forecasting judgment: The questions we choose also matter. International Journal of Forecasting, 33(4), 817-832. [pdf]
Tauber, S., Navarro, D.J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: Setting optimality aside and letting data drive psychological theory. Psychological Review, 124(4), 410-441. [pdf]
Gaut, G., Steyvers, M., Imel, Z., Atkins, D.C., & Smyth, P. (2017). Content Coding of Psychotherapy Transcripts Using Labeled Topic Models. IEEE Journal of Biomedical and Health Informatics, 21(2), 476-487. [pdf]
2016
Cassey, P., Gaut. G., Steyvers, M., Brown, S.D. (2016). A generative joint model for spike trains and saccades during perceptual decision making. Psychonomic Bulletin and Review, 23, 1757-1778. [pdf]
Turner, B.M., Rodriguez, C.A., Norcia, T.M., McClure, S.M., & Steyvers, M. (2016). Why More Is Better: Simultaneous Modeling of EEG, fMRI, and Behavioral Data. NeuroImage, 128, 96-115. [pdf]
Merkle, E.C., Steyvers, M., Mellers, B., and Tetlock, P.E. (2016). Item Response Models of Probability Judgments: Application to a Geopolitical Forecasting Tournament. Decision, 3(1), 1-19. [pdf]
Pace, B.T., Tanana, M., Xiao, B., Dembe, A., Soma, C.S., Steyvers, M., Narayanan, S., Atkins, D.C., Imel, Z.E. (2016).What about the words? Natural language processing in psychotherapy. Psychotherapy Bulletin, 50(1), 14-18. [pdf]
2015
Steyvers, M., Miller, B. (2015). Cognition and Collective Intelligence. In T.W. Malone and M.S. Bernstein (Eds.) Handbook of Collective Intelligence. MIT Press, pp. 119-138. [pdf]
Imel, Z. E., Steyvers, M., & Atkins, D. C. (2015). Computational Psychotherapy Research: Scaling up the Evaluation of Patient Provider Interactions. Psychotherapy, 52(1), 19-30. [pdf]
Lord, S. P, Can, D., Yi, M., Marín, R. A., Dunn, C. W., Imel, Z. E., Georgiou, P. G., Narayanan, S. S., Steyvers, M., & Atkins, D. C. (2015). Advancing methods for reliably assessing motivational interviewing fidelity using the Motivational Interviewing Skills Code. Journal of Substance Abuse Treatment, 49, 50-57. [pdf]
Hemmer, P., Tauber, S., and Steyvers, M. (2015). Moving beyond qualitative evaluations of Bayesian models of cognition. Psychonomic Bulletin and Review, 22(3), 614-628. [pdf]
Holsclaw, T., Hallgren, K. A., Steyvers, M., Smyth, P., & Atkins, D. C. (2015). Measurement error and outcome distributions: Methodological issues in regression analysis of behavioral coding data. Psychology of Addictive Behaviors, 29(4), 1031-1040. [pdf]
Lee, M.D., Liu, E.C., & Steyvers, M. (2015). The roles of knowledge and memory in generating top-10 lists. In D.C. Noelle & R. Dale (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society, pp. 1267-1272. Austin, TX: Cognitive Science Society. [pdf]
2014
Kim, W., Pitt, M.A., Lu, Z.L., Steyvers, M., and Myung, J.I. (2014). A Hierarchical Adaptive Approach to Optimal Experimental Design. Neural Computation, 26(11), 2465-2492. [pdf]
Steyvers, M. (2014). The Collective Memory Performance in a Recognition Memory Task. In Raaijmakers, Criss, Goldstone, Nosofsky, and Steyvers (Eds.) Cognitive Modeling in Perception and Memory. Routledge / Taylor & Francis. [pdf]
Kim, W., Pitt, M., Lu, Z.L., Steyvers, M., Gu, H., Myung, J.I. (2014). A Hierarchical Adaptive Approach to the Optimal Design of Experiments. Proceedings of the 36th Annual Conference of the Cognitive Science Society. [pdf]
Atkins, D.C., Steyvers, M., Imel, Z.E., and Smyth, P. (2014). Scaling up the evaluation of psychotherapy: Evaluating motivational interviewing fidelity via statistical text classification. Implementation Science, 9(49), 1-11. [pdf]
Lee, M.D., Steyvers, M., and Miller, B.J. (2014). A cognitive model for aggregating people’s rankings. PLoS ONE, 9(5). [pdf]
Steyvers, M., Wallsten, T.S., Merkle, E.C., and Turner, B.M. (2014). Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models. Risk Analysis, 34(3), 2014. [pdf][jags code for model in section 3.1] [ forecasting data set ]
Turner, B.M., Steyvers, M., Merkle, E.C., Budescu, D.V., Wallsten, T.S. (2014). Forecast Aggregation via Recalibration. Machine Learning, 95(3), 261-289. [pdf] [ forecasting data set ]
2013
Qiang, L., Steyvers, M., & Ihler, A. (2013). Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Advances in Neural Information Processing Systems, 26. [pdf]
Merkle, E.C., and Steyvers, M. (2013). Choosing a strictly proper scoring rule. Decision Analysis, 10(4), 292-304. [pdf]
Pearl, L., & Steyvers, M. (2013). “C’mon – You Should Read This”: Automatic Identification of Tone from Language Text. International Journal of Computational Linguistics (IJCL), 4(1), 1-30. [pdf]
Tauber, S. , Steyvers, M. (2013). Inference of Subjective Prior Knowledge: An Integrative Bayesian Approach. In M. Knauff, M. Pauen, Sebanz, N., and Wachsmuth (Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. Cognitive Science Society. (pp. 3510-3515). [pdf]
Turner, B.M., Sederberg, P.B., Brown, S.D., and Steyvers, M. (2013). A Method for Efficiently Sampling from Distributions with Correlated Dimensions. Psychological Methods, 18(3), 368-384. [pdf]
Ditta, A.S., & Steyvers, M. (2013). Collaborative Memory in a Serial Combination Procedure. Memory, 21(6), 668-674. [pdf]
Turner, B.M., Forstmann, B.U., Wagenmakers, E.J., Brown, S.D., Sederberg, P.B., and Steyvers, M. (2013). A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193-206. [pdf]
2012
Atkins, D. C., Rubin, T. N., Steyvers, M., Doeden, M. A., Baucom, B. R., & Christensen, A. (2012). Topic Models: A Novel Method for Modeling Couple and Family Text Data. Journal of Family Psychology, 6, 816-827. [pdf]
Warnaar, D. B., Merkle, E. C., Steyvers, M., Wallsten, T. S., Stone, E. R., Budescu, D. V., Yates, J. F., Sieck, W. R., Arkes, H. R., Argenta, C. F., Shin, Y., & Carter, J. N. (2012). The aggregative contingent estimation system: Selecting, rewarding, and training experts in a wisdom of crowds approach to forecasting. Proceedings of the 2012 Association for the Advancement of Artificial Intelligence Spring Symposium Series. [pdf]
Pearl, L., & Steyvers, M. (2012). Detecting Authorship Deception: A Supervised Machine Learning Approach Using Author Writeprints. Literary and Linguistic Computing, 27(2), 183-196. [pdf]
Hawkins, G., Brown, S.D., Steyvers, M., & Wagenmakers, E.J. (2012). An optimal adjustment procedure to minimize experiment time in decisions with multiple alternatives. Psychonomic Bulletin and Review, 19(2), 339-348. [pdf]
Shankle, W.R., Pooley, J.P. Steyvers, M., Hara, J. Mangrola, T., Reisberg, B., Lee, M.D. (in press). Relating Memory To Functional Performance In Normal Aging to Dementia Using Hierarchical Bayesian Cognitive Processing Models. Alzheimer Disease & Associated Disorders. [pdf]
Hawkins, G., Brown, S.D., Steyvers, M., & Wagenmakers, E.J. (2012). Decision speed induces context effects in choice. Experimental Psychology, 59(4), 206-215. [pdf]
Rubin, T., Chambers, A., Smyth, P., & Steyvers, M. (2012). Statistical Topic Models for Multi-Label Document Classification. Journal of Machine Learning, 88(1), 157-208. [pdf]
Lee, M.D., Steyvers, M., de Young, M., & Miller. B.J. (2012). Inferring expertise in knowledge and prediction ranking tasks. Topics in Cognitive Science, 4, 151-163. [pdf]
Steyvers, M. & Hemmer, P. (2012). Reconstruction from Memory in Naturalistic Environments. In Brian H. Ross (Ed.) The Psychology of Learning and Motivation, Vol 56. Elsevier Publishing, pp. 126-144. [pdf]
Yi, S.K.M., Steyvers, M., & Lee, M.D. (2012). The Wisdom of Crowds in Combinatorial Problems. Cognitive Science, 36(3), 452-470. [pdf]
Hawkins, G., Brown, S.D., Steyvers, M., & Wagenmakers, E.J. (2012). Context effects in multi-alternative decision making: empirical data and a Bayesian model. Cognitive Science, 36(3), 498-516. [pdf]
2011
Turner, B., & Steyvers, M. (2011). A Wisdom of the Crowd Approach to Forecasting. 2nd NIPS workshop on Computational Social Science and the Wisdom of Crowds. [pdf]
Pearl, L., Goldwater, S., & Steyvers, M. (2011). Online Learning Mechanisms for Bayesian Models of Word Segmentation. Research on Language and Computation, 8, 107-132. [pdf]
Rubin, T.N., Zeigenfuse, M.D., & Steyvers, M. (2011). A model of concept generalization and feature representation in hierarchies. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Tauber, S., & Steyvers, M. (2011). Using inverse planning and theory of mind for social goal inference. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf][appendix]
Miller, B., & Steyvers, M. (2011). The Wisdom of Crowds with Communication. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Lee, M.D., Steyvers, M., de Young, M., & Miller, B.J. (2011). A model-based approach to measuring expertise in ranking tasks.. In L. Carlson, C. Hölscher, & T.F. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [pdf]
Lee, M.D., Zhang, S., Munro, M., & Steyvers, M. (2011). Psychological models of human and optimal performance in bandit problems. Cognitive Systems Research 12, 164-174. [pdf]
Merkle, E.C., & Steyvers, M. (2011). A Psychological Model for Aggregating Judgments of Magnitude. Conference on Social Computing, Behavioral Modeling, and Prediction, 11. [pdf]
2010
Holloway, A., Smyth, P, & Steyvers, M. (2010). Learning concept graphs from text with stick-breaking priors. Advances in Neural Information Processing Systems, 23. [pdf]
Yi, S.K.M., Steyvers, M., Lee, M.D., & Dry, M. (2010). Wisdom of Crowds in Minimum Spanning Tree Problems. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum. [pdf]
Hemmer, P. & Steyvers, M., & Miller, B. (2010). The Wisdom of Crowds with Informative Priors. Proceedings of the 32nd Annual Conference of the Cognitive Science Society. [pdf]
Pearl, L., & Steyvers, M. (2010) Identifying Emotions, Intentions, and Attitudes in Text Using a Game with a Purpose. NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Los Angeles, CA. [pdf]
Pearl, L., Goldwater, S., & Steyvers, M. (2010) How Ideal Are We? Incorporating Human Limitations into Bayesian Models of Word Segmentation, BUCLD 34: Proceedings of the 34th annual Boston University Conference on Child Language Development, Somerville, MA: Cascadilla Press. [pdf]
Steyvers, M. (2010). Combining feature norms and text data with topic models. Acta Psychologica, 133(3), 234-342. [pdf]
Steyvers, M., Chemudugunta, C., & Smyth, P. (2010). Combining Background Knowledge and Learned Topics. Topics in Cognitive Science, 3, 18-47. [pdf]
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning Author-Topic Models from Text Corpora. ACM Transactions on Information Systems, 28(1), 1-38. [pdf]
2009
Steyvers, M., Lee, M.D., Miller, B., & Hemmer, P. (2009). The Wisdom of Crowds in the Recollection of Order Information. In Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta (Eds.) Advances in Neural Information Processing Systems, 22, pp. 1785-1793. MIT Press. [pdf]
Brown, S.D., Steyvers, M., & Wagenmakers, E.J. (2009). Observing Evidence Accumulation During Multi-Alternative Decisions. Journal of Mathematical Psychology, 53(6), 453-462. [pdf]
Yi, S.K.M., Steyvers, M., & Lee, M.D. (2009). Modeling Human Performance on Restless Bandit Problems using Particle Filters. Journal of Problem Solving, 2(2), 33-53. [pdf]
Hemmer, P. & Steyvers, M. (2009). Integrating Episodic and Semantic Information in Memory for Natural Scenes. In N. Taatgen, H. van Rijn, L. Schomaker and J.Nerbonne (Eds.) Proceedings of the 31th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum. [pdf]
Lee, M.D., Zhang, S., Munro, M., & Steyvers, M. (2009). Using Heuristic Models to Understand Human and Optimal Decision-Making on Bandit Problems. In: Proceedings of the Ninth International Conference on Cognitive Modeling. Manchester, UK. [pdf]
Lee, M.D., Grothe, E., & Steyvers, M. (2009). Conjunction and Disjunction Fallacies in Prediction Markets. In N. Taatgen, H. van Rijn, L. Schomaker and J.Nerbonne (Eds.) Proceedings of the 31th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum. [pdf]
Miller, B., Hemmer, P. Steyvers, M. & Lee, M.D. (2009). The Wisdom of Crowds in Ordering Problems. In: Proceedings of the Ninth International Conference on Cognitive Modeling. Manchester, UK. [pdf]
Rubin, T., & Steyvers, M. (2009). A Topic Model For Movie Choices and Ratings. In: Proceedings of the Ninth International Conference on Cognitive Modeling. Manchester, UK. [pdf]
Hemmer, P. & Steyvers, M. (2009). A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science, 1, 189-202. [pdf]
Steyvers, M., Lee, M.D., & Wagenmakers, E.J. (2009). A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53, 168-179. [pdf]
Brown, S.D., & Steyvers, M. (2009). Detecting and Predicting Changes. Cognitive Psychology, 58, 49-67. [pdf]
Hemmer, P., Steyvers, M. (2009). Integrating Episodic Memories and Prior Knowledge at Multiple Levels of Abstraction. Psychonomic Bulletin & Review, 16(1), 80-87. [pdf]
2008
Chemudugunta, Smyth, P., & Steyvers, M. (2008). Combining Concept Hierarchies and Statistical Topic Models. In: ACM 17th Conference on Information and Knowledge Management. [pdf]
Chemudugunta, C., Smyth, P., & Steyvers, M. (2008). Text Modeling using Unsupervised Topic Models and Concept Hierarchies. Technical Report. [pdf]
Chemudugunta, C., Holloway, A., Smyth, P., & Steyvers, M. (2008). Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning. In: 7th International Semantic Web Conference. [pdf]
Hemmer, P. & Steyvers, M. (2008). A Bayesian Account of Reconstructive Memory. In V. Sloutsky, B. Love, and K. McRae (Eds.) Proceedings of the 30th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Lawrence Erlbaum. [pdf]
Steyvers, M. & Griffiths, T.L. (2008). Rational Analysis as a Link between Human Memory and Information Retrieval. In N. Chater and M Oaksford (Eds.) The Probabilistic Mind: Prospects from Rational Models of Cognition. Oxford University Press, pp. 327-347. [pdf]
2007
Griffiths, T.L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18(12), pp. 1069-1076. [pdf]
Chemudugunta, C., Smyth, P., & Steyvers, M. (2007). Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model. In: Advances in Neural Information Processing Systems, 19. [pdf]
Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B.T. (2007). Topics in Semantic Representation. Psychological Review, 114(2), 211-244. [pdf]
Brown, S.D., Steyvers, M., & Hemmer, P. (2007). Modeling Experimentally Induced Strategy Shifts. Psychological Science, 18, 40-45. [pdf]
2006
Newman, D., Smyth, P., & Steyvers, M. (2006). Scalable Parallel Topic Models. Journal of Intelligence Community Research and Development.
Newman, D., Chemudugunta, C., Smyth, P., & Steyvers, M. (2006). Statistical entity-topic models. The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia. [pdf]
Steyvers, M., Griffiths, T.L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Sciences, 10(7), 327-334. [pdf]
Newman, D., Chemudugunta, C., Smyth, P., & Steyvers, M. (2006). Analyzing entities and topics in news articles using statistical topic models. In: Springer Lecture Notes in Computer Science (LNCS) series — IEEE International Conference on Intelligence and Security Informatics. [pdf]
Wagenmakers, E.J., Grunwald, P., & Steyvers, M. (2006). Accumulative prediction error and the selection of time series models. Journal of Mathematical Psychology, 50, 149-166. [pdf]
Navarro, D.J., Griffiths, T.L., Steyvers, S., & Lee, M.D. (2006). Modeling individual differences using Dirichlet processes. Journal of Mathematical Psychology, 50, 101-122. [pdf]
Steyvers, M., & Brown, S. (2006). Prediction and Change Detection. In Y. Weiss, B. Scholkopf, and J. Platt (eds), Advances in Neural Information Processing Systems, 18, pp. 1281-1288. MIT Press. [pdf]
Steyvers, M. & Griffiths, T. (2006). Probabilistic topic models. In T. Landauer, D McNamara, S. Dennis, and W. Kintsch (eds), Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum. [pdf]
2005
Brown, S.D., & Steyvers, M. (2005). The Dynamics of Experimentally Induced Criterion Shifts. Journal of Experimental Psychology: Learning, Memory & Cognition, 31(4), 587-599. [pdf]
Steyvers, M., & Tenenbaum, J. (2005). The Large Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive Science, 29(1), 41-78. [pdf]
Navarro, D. J., Griffiths, T. L., Steyvers, M. & Lee, M. D. (2005). Modeling individual differences with Dirichlet processes. In B. G. Bara, L. W. Barsalou & M. Bucciarelli (Eds.) Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 1594-1599). Mahwah, NJ: Lawrence Erlbaum. [pdf]
2004
Griffiths, T., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences, 101 (suppl. 1), 5228-5235. [pdf]
Griffiths, T.L., & Steyvers, M., Blei, D.M., & Tenenbaum, J.B. (2005). Integrating Topics and Syntax. In: Advances in Neural Information Processing Systems, 17 (Saul, L.K et al., eds), 537-544. MIT Press. [pdf]
Rosen-Zvi, M., Griffiths T., Steyvers, M., & Smyth, P. (2004). The Author-Topic Model for Authors and Documents. In 20th Conference on Uncertainty in Artificial Intelligence. Banff, Canada. [pdf]
Steyvers, M., Shiffrin, R.M., & Nelson, D.L. (2004). Word Association Spaces for Predicting Semantic Similarity Effects in Episodic Memory. In A. Healy (Ed.), Experimental Cognitive Psychology and its Applications. [pdf]
Wagenmakers, E-J., Zeelenberg, R., Steyvers, M., Shiffrin, R. M., & Raaijmakers, J. G. W. (2004). Nonword repetition in lexical decision: Evidence for two opposing processes. The Quarterly Journal of Experimental Psychology A, 57(7), 1191-1210. [pdf]
Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004). Probabilistic Author-Topic Models for Information Discovery. The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, Washington. [pdf]
Wagenmakers, E.J.M., Steyvers, M., Raaijmakers, J.G.W., Shiffrin, R.M., van Rijn, H., & Zeelenberg, R. (2004). A Model for Evidence Accumulation in the Lexical Decision Task. Cognitive Psychology, 48, 332-367. [pdf]
2003
Goldstone, R., Steyvers, M., & Rogosky, B.J. (2003). Conceptual Interrelatedness and Caricatures. Memory and Cognition, 31(2), 169-180. [pdf]
Steyvers, M., & Malmberg, K. (2003). The effect of normative context variability on recognition memory. Journal of Experimental Psychology: Learning, Memory, & Cognition, 29(5), 760-766. [pdf][Excel file with word and sample frequency counts for 26414 selected words from TASA corpus]
Steyvers, M., Tenenbaum, J., Wagenmakers, E.J., Blum, B. (2003). Inferring Causal Networks from Observations and Interventions. Cognitive Science, 27, 453-489. [pdf]
2002
Griffiths, T.L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. In: Proceedings of the Twenty-Fourth Annual Conference of Cognitive Science Society. George Mason University, Fairfax, VA. [pdf]
Griffiths, T.L., & Steyvers, M. (2002). Prediction and semantic association. In: Advances in Neural Information Processing Systems 15, pp. 11-18. MIT Press. [pdf]
Malmberg, K. J., Steyvers, M., Stephens, J. D., & Shiffrin, R.M. (2002). Feature-frequency effects in recognition memory. Memory & Cognition, 30(4), 607-613. [pdf]
Steyvers, M. (2002). Multidimensional Scaling. In: Encyclopedia of Cognitive Science. Nature Publishing Group, London, UK. [pdf]
2001
Goldstone, R.L., & Steyvers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology, General, 130, 116-139. [pdf][software to create images as in Figure 1][BMP images of the 4 x 4 grid in Figure 1][original BMP files with line files, including C program to create 10 x 10 grid of Figure 1. Also includes all the resulting BMP files of the 10 x 10 grid]
Steyvers, M., Wagenmakers, E.J.M., Shiffrin, R.M., Zeelenberg, R., & Raaijmakers, J.G.W. (2001). A Bayesian model for the time-course of lexical processing. In: Proceedings of the Fourth International Conference on Cognitive Modeling. George Mason University, Fairfax, VA. [pdf]
2000
Goldstone, R., Steyvers, M., Kersten, A., & Spencer-Smith, J. (2000). Interactions between perceptual and conceptual learning. In Dietrich, E., & A. Markman (eds.), Cognitive Dynamics: conceptual and representational change in humans and machines. Cambridge, MA: MIT Press. [pdf]
Steyvers, M. (2000). Modeling semantic and orthographic similarity effects on memory for individual words. Dissertation, Psychology Department, Indiana University. [pdf]
Steyvers, M., & Busey, T. (2000). Predicting Similarity Ratings to Faces using Physical Descriptions. In M. Wenger, & J. Townsend (Eds.), Computational, geometric, and process perspectives on facial cognition: Contexts and challenges. Lawrence Erlbaum Associates. [pdf] [set of all 60 faces (BMP format) plus geometric distances][Matrix of dissimilarity ratings to all 60 faces. Higher numbers –> more dissimilar. All ratings were z-transformed for each individual subject before averaging]
1995-1999
Goldstone, R.L., Steyvers, M. & Larimer, K. (1996). Categorical Perception of Novel Dimensions. In Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 243-248). La Jolla, CA. Lawrence Erlbaum Associates. [pdf]
Shiffrin, R.M. & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving Effectively from Memory. Psychonomic Bulletin & Review, 4 (2), 145-166. [pdf]
Shiffrin, R. M., & Steyvers, M. (1998). The effectiveness of retrieval from memory. In M. Oaksford & N. Chater (Eds.). Rational models of cognition (pp. 73-95), Oxford, England: Oxford University Press. [pdf]
Steyvers, M. (1999). Morphing techniques for generating and manipulating face images. Behavior Research Methods, Instruments, & Computers, 31, 359-369. [pdf]
Steyvers, M. & Grunwald, P. (1996). A recurrent network that performs a context-sensitive task. In: Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 335-339). La Jolla, CA. Lawrence Erlbaum Associates. [pdf]
Van Leeuwen, C., Steyvers, M., & Nooter, M. (1997). Stability and intermittency in large-scale coupled oscillator models for perceptual segmentation. Journal of Mathematical Psychology, 41, 319-344. [pdf]