| Human and Machine Intelligence Learning Metacognition Machine Learning Wisdom of Crowds |
Large-scale Data Forecasting Episodic and Semantic Memory Bayesian Models of Cognition |
Human and Machine Intelligence
Steyvers, M., Tejeda, H., Kumar, A., Belem, C., Karny, S., Hu, X., Mayer, L., & Smyth, P. (in press). What Large Language Models Know and What People Think They Know. Nature Machine Intelligence. [pdf][supporting information][data and code]
Steyvers, M. & Kumar, A. (2024). Three challenges for AI-Assisted Decision-Making. Perspectives on Psychological Science, 19(5), 722-734. [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]
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]
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]
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]
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]
Groneau, Q.F., Steyvers, M., & Brown, S. (2024). How Do You Know That You Don’t Know? Cognitive Systems Research, 86, 101232. [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]
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]
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]
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]
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]
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]
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]
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]
Learning
Das, P., & Steyvers, M. (2023). Older adults catch up to younger younger adults on cognitive tasks after extended training. Collabra: Psychology, 9(1). [pdf]
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]
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]
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]
Metacognition
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]
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., & 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. XX-XX. Austin, TX: Cognitive Science Society. [pdf]
Bennett, S.T., Steyvers, M. (2022). Leveraging metacognitive ability to improve crowd accuracy via impossible questions. Decision, 9(1), 60-73. [pdf][data]
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]
Steyvers, M. and Benjamin, A.S. (2018). The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets. Behavior Research Methods. [pdf][code]
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]
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]
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]
Machine Learning
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][supplemenatry materials]
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]
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]
Ji, D., Logan, R., Smyth, P., & Steyvers, M. (2019). Bayesian Evaluation of Black Box Classifiers. ICML Workshop on Uncertainty & Robustness in Deep Learning. [pdf]
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]
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]
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]
Holloway, A., Smyth, P, & Steyvers, M. (2010). Learning concept graphs from text with stick-breaking priors. Advances in Neural Information Processing Systems, 23. [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]
Steyvers, M., Chemudugunta, C., & Smyth, P. (2010). Combining Background Knowledge and Learned Topics. Topics in Cognitive Science, 3, 18-47. [pdf]
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]
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]
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]
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]
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., 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]
Wisdom of Crowds
Bennett, S.T., Steyvers, M. (2022). Leveraging metacognitive ability to improve crowd accuracy via impossible questions. Decision, 9(1), 60-73. [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]
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]
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]
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]
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]
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]
Lee, M.D., Steyvers, M., and Miller, B.J. (2014). A cognitive model for aggregating people’s rankings. PLoS ONE, 9(5). [pdf]
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]
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]
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]
Yi, S.K.M., Steyvers, M., & Lee, M.D. (2012). The Wisdom of Crowds in Combinatorial Problems. Cognitive Science, 36(3), 452-470. [pdf]
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]
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]
Merkle, E.C., & Steyvers, M. (2011). A Psychological Model for Aggregating Judgments of Magnitude. Conference on Social Computing, Behavioral Modeling, and Prediction, 11. [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]
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]
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]
Large-Scale Data
Robinson, M. & Steyvers, M. (2023). Linking computational models of two core tasks of cognitive control. Psychological Review, 130(1), 71–101 [pdf][data and code]
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]
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. (2018). The joint contribution of participation and performance to learning functions: Exploring the effects of age in large-scale data sets. Behavior Research Methods. [pdf][code]
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]
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]
Forecasting
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]
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]
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]
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]
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 ]
Merkle, E.C., and Steyvers, M. (2013). Choosing a strictly proper scoring rule. Decision Analysis, 10(4), 292-304. [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]
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]
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]
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]
Episodic and Semantic Memory
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]
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]
Ditta, A.S., & Steyvers, M. (2013). Collaborative Memory in a Serial Combination Procedure. Memory, 21(6), 668-674. [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]
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]
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]
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]
Hemmer, P. & Steyvers, M. (2009). A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science, 1, 189-202. [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]
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]
Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B.T. (2007). Topics in Semantic Representation. Psychological Review, 114(2), 211-244. [pdf]
Steyvers, M., Griffiths, T.L., & Dennis, S. (2006). Probabilistic inference in human semantic memory. Trends in Cognitive Sciences, 10(7), 327-334. [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]
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]
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. (2000). Modeling semantic and orthographic similarity effects on memory for individual words. Dissertation, Psychology Department, Indiana University. [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]
Bayesian Models of Cognition
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]
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]
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]
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]
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]
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]
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]
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]
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