quals: SciOfSci quals: Causal quals: Rational

Qualifying exam lists version 4.1


  • number of articles follow list titles and section titles

Causal inference and explanation, 34

Explaining Explanation, 5

  1. Salmon, W. (1989). Four Decades of Scientific Explanation @done
  2. Gopnik, A. (2000). Explanation as orgasm and the drive for causal understanding: The evolution, function and phenomenology of the theory-formation system. In F. Keil & R. Wilson (Eds.) Cognition and explanation. Cambridge, Mass: MIT Press. @done
  3. Thagard, P. (2006). Evaluating explanations in science, law, and everyday life. Current Directions in Psychological Science, 15, 141-145. @done
  4. Trout, J.D. (2007). The Psychology of Scientific Explanation. Philosophy Compass, 2/3, 564-591. @done
  5. Lombrozo, T. (2012). Explanation and abductive inference. In K.J. Holyoak and R.G. Morrison (Eds.), Oxford Handbook of Thinking and Reasoning (pp. 260-276), Oxford, UK: Oxford University Press. @done

Bayesian Learning as a model of of Human Causal Learning, 3

  1. Steyvers, M., Tenenbaum, J., Wagenmakers, E.J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453-489. @done
  2. Sloman, S.A., & Lagnado, D. (2005). Do we “do”? Cognitive Science, 29, 5-39. @done
  3. Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384. @done

Simplicity, unification, and what makes an explanation good, 5

  1. Kuhn, T.S. (1977). Objectivity, value judgment, and theory choice. In The Essential Tension: Selected Studies in Scientific Tradition and Change. Chicago and London: University of Chicago Press. @done
  2. Read, S. J., & Marcus-Newhall, A. R. (1993). Explanatory coherence in social explanations: A parallel distributed processing account. Journal of Personality and Social Psychology, 65, 429-447. @done
  3. Preston, J., & Epley, N. (2005). Explanations versus applications: The explanatory power of valuable beliefs. Psychological Science, 18, 826-832. @done
  4. Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55(3), 232-257. @done
  5. Lombrozo, T. (2011). The instrumental value of explanations. Philosophy Compass, 6, 539-551. @done

Choosing explanations, Diagnosis, and Attribution: Reasoning from observations to their causes, 5

  1. Spellman, B. A. (1997). Crediting causality. Journal of Experimental Psychology: General, 126, 323-348. @done
  2. Nielsen, U., Pellet, J.-P., & Elisseeff, A. (2008). Explanation trees for causal Bayesian networks. (D. McAllester & P. Myllymäki, Eds.) Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence. pp. 427–434. @done
  3. Yuan, C., & Lu, T.-C. (2008). A General Framework for Generating Multivariate Explanations in Bayesian Networks. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 1–6. @done
  4. Malle, B. F. (2011). Time to give up the dogmas of attribution: An alternative theory of behavior explanation.Advanceschapter.pdf) In J. M. Olson and M. P. Zanna, Advances of Experimental Social Psychology (Vol. 44, pp. 297-352). Burlington: Academic Press. @done
  5. McCoy*, J. M., Ullman*, T. D., Stuhlmuller, A., Gerstenberg, T., and Tenenbaum, J.B. (2012). Why blame Bob? Probabilistic generative models, counterfactual reasoning, and blame attribution. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science society. * = co-first author @done

Causal Mechanism and Inferring Hidden Causes, 5

  1. Shultz, T. R. (1982). Rules of Causal Attribution. Monographs of the Society for Research in Child Development, 47(1), 1-51. @done
  2. Ahn, W. K., Kalish, C. W., Medin, D. L., & Gelman, S. A. (1995). The role of covariation vs. mechanism information in causal attribution. Cognition, 54, 299–352. @done.
  3. Kushnir, T., Gopnik, A., Lucas, C., & Schulz, L.E. (2010). Inferring hidden causal structure. Cognitive Science, 34, 148-160. @done
  4. Buchanan, D.W., & Sobel, D.M. (2011). Mechanism-based reasoning in young children. Child Development. 82, 6, 2053-2066. @done
  5. Gweon, H., & Schulz, L. (2011). 16-Month-Olds Rationally Infer Causes of Failed Actions. Science, 332(6037), 1524. @done

Human Causal inference in time, 5

  1. Hagmayer, Y., & Waldmann, M. R. (2002). How temporal assumptions influence causal judgments. Memory & Cognition, 30 (7), 1128-1137. @done
  2. Lagnado, D.A., & Sloman, S.A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory & Cognition, 32, 451-460. @done
  3. Greville, W.J. & Buehner, M.J. (2007). The influence of temporal distributions on causal induction from tabular data. Memory & Cognition, 35, 444–453 @done
  4. Lagnado, D.A., & Speekenbrink, M. (2010). The influence of delays in real-time causal learning..pdf) The Open Psychology Journal, 3(2), 184-195. @done
  5. Greville, W.J., & Buehner, M.J. (2010). Temporal Predictability Facilitates Human Causal Learning. Journal of Experimental Psychology: General, 139(4), 756–771 @done

Additional considerations in causal explanation, 6

  1. Hilton, D. J., & Slugoski, B. R. (1986). Knowledge-based causal attribution: The abnormal conditions focus model. Psychological Review, 93(1), 75. @done
  2. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological review, 104(2), 367. @done
  3. Novick, L.R., Cheng, P.W. (2004). Assessing Interactive Causal Influence Psychological Review, 111(2), pp. 455-485. @done
  4. White, P.A. (2009). Not by contingency: Some arguments about the fundamentals of human causal learning. Thinking & Reasoning 15 (2), pp. 129-166. @done
  5. Glymour, C., Danks, D., Glymour, B., Eberhardt, F., Ramsey, J., Scheines, R., Spirtes, P., Teng, C.M., Zhang, J. , (2010). Actual causation: a stone soup essay. Synthese. 169-192 @done
  6. Keil, F.C. (2010). The Feasibility of Folk Science. Cognitive Science, 34, 826-862. @done

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2013_35_5_1311 18
2013_36_3_1400 7
2013_36_7_2342 2
2013_37_2_1437 0

Rational Probabilistic Models of Cognition Reading List: Investigating the possibility of a Bayesian Mind, 29

Foundations of rational, probabilistic models of cognition, 5

  1. Shepard, R.N. (1987). Toward a universal law of generalization for psychological science. Science, 237, 1317-1323. @done
  2. Chapter 1 from Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum. @done
  3. Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24,629-641. (pdf) @done
  4. Chater, N., & Vitányi, P. M. B. (2003). The generalized universal law of generalization. Journal of Mathematical Psychology, 47(3), 346–369. @done
  5. Chapter 1, Foreward & Afterward from Marr D. (2010). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information., The MIT Press. Foreword by Shimon Ullman, afterword by Tomaso Poggio. @done

Theories of a bayesian mind, 3

  1. Jacobs, R. A. & Kruschke, J. K. (2011). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 8-21. @done
  2. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. @done
  3. Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. @done

Theories in a bayesian mind, 5

  1. Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116(4), 661–716. doi:10.1037/a0017201 @done
  2. Kemp, C. Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition. 114(2), 165-196. @done
  3. Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2010). Learning to Learn Causal Models. Cognitive Science, 34(7), 1185–1243. doi:10.1111/j.1551-6709.2010.01128.x @done
  4. Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118(1), 110–119. doi:10.1037/a0021336 @done
  5. Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory learning as stochastic search in the language of thought. Cognitive Development, 1–53. @done

Different meanings of meaning when modeling meaning, 5

  1. Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211-244. @done
  2. Steyvers, M., & Tenenbaum, J. B. (2005). The large scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29, 41-78. @done
  3. Griffiths, T.L., & Tenenbaum, J.B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226. @done
  4. Kemp, C. (2012). Exploring the conceptual universe. Psychological Review. 119(4), 685-722. @done
  5. Navarro, D. J., Perfors, A., & Vong, W. K. (2013). Learning time-varying categories. Memory & Cognition, 1–11. @done
  6. Bonawitz, E., Ullman, T., Gopnik, A., & Tenenbaum, J.B. (2012) Sticking to the evidence? A computational and behavioral case study of micro-theory change in the domain of magnetism. 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) @done

Formal frameworks for continuous-temporal, causal relations, 6

Continuous time bayesian networks
  1. Nodelman, U., Shelton, C. R., & Koller, D. (2002). Continuous time Bayesian networks. 378–387.@done
  2. Gopalratnam, K., Kautz, H., & Weld, D. S. (2005). Extending continuous time bayesian networks In Proceedings of the National Conference on Artificial Intelligence 20(2) p. 981. @done
Point process models
  1. Simma, A. & Jordan, M. I. (2010). Modeling events with cascades of Poisson processes. In P. Grunwald and P. Spirtes (Eds.), Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press. @done
  2. Blundell, C., Heller, K. A., & Beck, J. M. (2012). Modeling Reciprocating Relationship with Hawkes Processes. In Advances in Neural Information Processing Systems pp. 2609-2617. @done
  3. Cho, Y. S., Galstyan, A., Brantingham, J., & Tita, G. (2013). Latent Point Process Models for Spatial-Temporal Networks.. @done
  4. Gomez-Rodriguez, M. Leskovec, J. and Schoelkopf, B. (2013). Modeling Information Propagation with Survival Theory. International Conference on Machine Learning (ICML). @done

Challenges to Probabilistic, Rational, (or) Bayesian models in Cognitive Science, 5

  1. Godfrey-Smith, P. (2001). Three kinds of adaptationism. Adaptationism and optimality, 335-357. @done
  2. Freedman, D.A. (2006)Statistical models for causation: What inferential leverage do they provide? Evaluation Review vol. 30 pp. 691–713. @done
  3. McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T.T.,Seidenberg, M. S., and Smith, L. B. (2010). Letting Structure Emerge:Connectionist and Dynamical Systems Approaches to Understanding Cognition. Trends in Cognitive Sciences, 14, 348-356. @done
  4. 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. + commentary and responses @done
  5. Perfors, A. (2012). Bayesian models of cognition: What's built in after all? Philosophy Compass 7, 127-138. @done
  6. Danks, D. (2013). Moving from Levels & Reduction to Dimensions & Constraints proceedings of the cognitive science society. @done

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2013_35_5_1313 16
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Science as a Cognitive, Computational Process: the lives of ideas, 32

Evolution and contagion of ideas: the rise, fall and competition of ideas 5

  1. Chapter 1 (pp.8-101) in Lakatos, I. (1978). The methodology of scientific research programmes: Philosophical Papers Volume 1. Cambridge: Cambridge University Press. @done
  2. Hull, D. L. (1988) Science as a Process: An Evolutionary Account of the Social and Conceptual Development of Science Chicago: University of Chicago Press. @done
  3. Shi X. , Leskovec J. , McFarland D. A. (2010).Citing for High Impact Joint Conference on Digital Libraries (JCDL), 2010. @done
  4. Godfrey-Smith, P. (2012). Darwinism and Cultural Change Philosophical Transactions of the Royal Society B 367, 2160-2170 @done
  5. Myers, S. & Leskovec, J. (2012). Clash of the Contagions: Cooperation and Competition in Information Diffusion IEEE International Conference On Data Mining @done

Revolutions and pandemics of ideas: rejected and received views 4

  1. Kuhn, T.S. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press. @done
  2. "Introduction" and "Theoretical and Experimental Cultures" Galison, P. (1987). How experiments end. University of Chicago Press. @done
  3. Salmon, W. (1990). Rationality and objectivity in science or Tom Kuhn meets Tom Bayes. Scientific theories, 14, 175–204. @done
  4. Henderson, L., Goodman, N.D., Tenenbaum, J.B., & Woodward, J.F. (2010). The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective. Philosophy of Science 77 (2):172-200. @done
  5. Rutjens, B. T., van Harreveld, F., & van der Pligt, J. (2013). Step by Step Finding Compensatory Order in Science. Current Directions in Psychological Science, 22(3), 250-255. @done

  6. Rutjens, B. T., van Harreveld, F., van der Pligt, J., Kreemers, L. M., & Noordewier, M. K. (2013). Steps, stages, and structure: Finding compensatory order in scientific theories. Journal of Experimental Psychology: General, 142(2), 313. @done

Styles of scientific reasoning or ideas' ways of becoming, 5

  1. Hacking, I. (1982). Language, Truth and Reason. Reprinted in Historical ontology (2002). (pp. 583-600). Springer Netherlands. @done
  2. Crombie, A. C. (1988).Designed in the Mind: Western Visions of Science, Nature and Humankind History of Science 26, 1-12. @done
  3. Hacking, I. (1992).'Style' for Historians and Philosophers. Studies in History and Philosophy of Science 23, 1-20. Reprinted in Historical Ontology @done
  4. Hacking, I. (2012). 'Language, Truth and Reason' 30 years later. Studies in History and Philosophy of Science Part A. @done
  5. Lassiter D., & Goodman, N. D.(2012).How many kinds of reasoning? Inference, probability, and natural language semantics Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society. @done

Research, cooperation despite competition, and the division of cognitive labor 5

  1. "The Organization of Cognitive Labor" (chapter 8) in Kitcher, P. (1993).The advancement of Science. New York, Oxford University Press. @done
  2. Keil, F.C., Stein, C., Webb, L., Billings, V.D., & Rozenblit, L. (2008). Discerning the Division of Cognitive Labor: An Emerging Understanding of How Knowledge is Clustered in Other Minds. Cognitive Science, 32(2), 259-300. @done
  3. Weisberg, M. and Muldoon, R. (2009). Epistemic Landscapes and the Division of Cognitive Labor Philosophy of Science, 76, 225-252 @done
  4. West, R. & Leskovec J. (2012).Automatic versus Human Navigation in Information Networks by AAAI International Conference on Weblogs and Social Media @done
  5. West, R. & Leskovec J. (2012). Human Wayfinding in Information Networks ACM International Conference on World Wide Web @done
  6. Strevens, M. (2013). Herding and the Quest for Credit Journal of Economic Methodology 20, 19–34. @done

Human Ontogeny as a lens on development and the growth of scientific ideas, 13

  1. "Matters of Ontology" and "Conclusions" (chapter 6 & 7) in Carey, S. (1985) Conceptual Change in Childhood Cambridge, MA: MIT Press @done
  2. Vosniadou, S. & Brewer W.F. (1992). Mental Models of the Earth: A Study of Conceptual Change in Childhood Cognitive Psychology 24, 535-585 @done
  3. Solomon, M. (1992). Scientific rationality and human reasoning. Philosophy of Science, 439-455. @done
  4. "Introduction" (Chapter 1) in Koslowski, B. (1996). Theory and Evidence: The Development of Scientific Reasoning. Cambridge, MA: MIT Press @done
  5. Gopnik A. (1996). The scientist as child. Philosophy of Science, 63, 4, 485-514. @done
  6. Carey, S., & Spelke, E. (1996). Science and core knowledge. Philosophy of science, 515-533. @done
  7. Solomon, M. (1996). Commentary on Alison Gopnik's "The Scientist as Child". Philosophy of science, 63(4), 547-551. @done
  8. Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of educational research, 63(1), 1-49. @done
  9. Scientific Discovery as Problem Solving (Chapter 2) in Klahr, D. (2000). Exploring Science: The cognition and development of discovery processes Cambridge, MA: MIT Press @done
  10. Keil, F. C., & Newman, G. E. (2010). Darwin and development: Why ontogeny does not recapitulate phylogeny for human concepts. The Making of Human Concepts, 317. @done
  11. Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments in preschoolers' exploratory play. Cognition, 120(3), 341–349. @done
  12. Schulz, L. (2012). The origins of inquiry: inductive inference and exploration in early childhood. Trends in cognitive sciences, 16(7), 382–389. @done
  13. Gopnik, A. & Wellman, H.M. (in press). Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory. Psychological Bulletin. @done

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