Qualifying exam lists

Final: version 4.1

ᴍᴅ Pᴀᴄᴇʀ

A Note on Interpretation: number of articles follow list headings and section titles

Causal inference and explanation, 34

Explaining Explanation, 5

  1. Salmon, W. (1989). Four Decades of Scientific Explanation, University of Pittsburgh Press, Pittsburgh.
  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.
  3. Thagard, P. (2006). Evaluating explanations in science, law, and everyday life. Current Directions in Psychological Science, 15, 141-145.
  4. Trout, J.D. (2007). The Psychology of Scientific Explanation. Philosophy Compass, 2/3, 564-591.
  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.

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.
  2. Sloman, S.A., & Lagnado, D. (2005). Do we “do”? Cognitive Science, 29, 5-39.
  3. Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51, 354-384.

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.
  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.
  3. Preston, J., & Epley, N. (2005). Explanations versus applications: The explanatory power of valuable beliefs. Psychological Science, 18, 826-832.
  4. Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55(3), 232-257.
  5. Lombrozo, T. (2011). The instrumental value of explanations. Philosophy Compass, 6, 539-551.

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.
  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.
  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.
  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.
  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

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.
  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. .
  3. Kushnir, T., Gopnik, A., Lucas, C., & Schulz, L.E. (2010). Inferring hidden causal structure. Cognitive Science, 34, 148-160.
  4. Buchanan, D.W., & Sobel, D.M. (2011). Mechanism-based reasoning in young children. Child Development. 82, 6, 2053-2066.
  5. Gweon, H., & Schulz, L. (2011). 16-Month-Olds Rationally Infer Causes of Failed Actions. Science, 332(6037), 1524.

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.
  2. Lagnado, D.A., & Sloman, S.A. (2006). Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory & Cognition, 32, 451-460.
  3. Greville, W.J. & Buehner, M.J. (2007). The influence of temporal distributions on causal induction from tabular data. Memory & Cognition, 35, 444–453
  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.
  5. Greville, W.J., & Buehner, M.J. (2010). Temporal Predictability Facilitates Human Causal Learning. Journal of Experimental Psychology: General, 139(4), 756–771

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.
  2. Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological review, 104(2), 367.
  3. Novick, L.R., Cheng, P.W. (2004). Assessing Interactive Causal Influence Psychological Review, 111(2), pp. 455-485.
  4. White, P.A. (2009). Not by contingency: Some arguments about the fundamentals of human causal learning. Thinking & Reasoning 15 (2), pp. 129-166.
  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
  6. Keil, F.C. (2010). The Feasibility of Folk Science. Cognitive Science, 34, 826-862.

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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.
  2. Chapter 1 from Anderson, J. R. (1990). The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.
  3. Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24,629-641. (pdf)
  4. Chater, N., & Vitányi, P. M. B. (2003). The generalized universal law of generalization. Journal of Mathematical Psychology, 47(3), 346–369.
  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.

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.
  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.
  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.

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
  2. Kemp, C. Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition. 114(2), 165-196.
  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
  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
  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.

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.
  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.
  3. Griffiths, T.L., & Tenenbaum, J.B. (2007). From mere coincidences to meaningful discoveries. Cognition, 103, 180-226.
  4. Kemp, C. (2012). Exploring the conceptual universe. Psychological Review. 119(4), 685-722.
  5. Navarro, D. J., Perfors, A., & Vong, W. K. (2013). Learning time-varying categories. Memory & Cognition, 1–11.
  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)

Formal frameworks for continuous-temporal, causal relations, 6

Continuous time bayesian networks (2 of 6)
  1. Nodelman, U., Shelton, C. R., & Koller, D. (2002). Continuous time Bayesian networks. 378–387.
  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.
Point process models (4 of 6)
  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.
  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.
  3. Cho, Y. S., Galstyan, A., Brantingham, J., & Tita, G. (2013). Latent Point Process Models for Spatial-Temporal Networks..
  4. Gomez-Rodriguez, M. Leskovec, J. and Schoelkopf, B. (2013). Modeling Information Propagation with Survival Theory. International Conference on Machine Learning (ICML).

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.
  2. Freedman, D.A. (2006)Statistical models for causation: What inferential leverage do they provide? Evaluation Review vol. 30 pp. 691–713.
  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.
  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
  5. Perfors, A. (2012). Bayesian models of cognition: What's built in after all? Philosophy Compass 7, 127-138.
  6. Danks, D. (2013). Moving from Levels & Reduction to Dimensions & Constraints proceedings of the cognitive science society.

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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.
  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.
  3. Shi X. , Leskovec J. , McFarland D. A. (2010).Citing for High Impact Joint Conference on Digital Libraries (JCDL), 2010.
  4. Godfrey-Smith, P. (2012). Darwinism and Cultural Change Philosophical Transactions of the Royal Society B 367, 2160-2170
  5. Myers, S. & Leskovec, J. (2012). Clash of the Contagions: Cooperation and Competition in Information Diffusion IEEE International Conference On Data Mining

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.
  2. "Introduction" and "Theoretical and Experimental Cultures" Galison, P. (1987). How experiments end. University of Chicago Press.
  3. Salmon, W. (1990). Rationality and objectivity in science or Tom Kuhn meets Tom Bayes. Scientific theories, 14, 175–204.
  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.

  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.

  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.

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.
  2. Crombie, A. C. (1988).Designed in the Mind: Western Visions of Science, Nature and Humankind History of Science 26, 1-12.
  3. Hacking, I. (1992).'Style' for Historians and Philosophers. Studies in History and Philosophy of Science 23, 1-20. Reprinted in Historical Ontology
  4. Hacking, I. (2012). 'Language, Truth and Reason' 30 years later. Studies in History and Philosophy of Science Part A.
  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.

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.
  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.
  3. Weisberg, M. and Muldoon, R. (2009). Epistemic Landscapes and the Division of Cognitive Labor Philosophy of Science, 76, 225-252
  4. West, R. & Leskovec J. (2012).Automatic versus Human Navigation in Information Networks by AAAI International Conference on Weblogs and Social Media
  5. West, R. & Leskovec J. (2012). Human Wayfinding in Information Networks ACM International Conference on World Wide Web
  6. Strevens, M. (2013). Herding and the Quest for Credit Journal of Economic Methodology 20, 19–34.

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
  2. Vosniadou, S. & Brewer W.F. (1992). Mental Models of the Earth: A Study of Conceptual Change in Childhood Cognitive Psychology 24, 535-585
  3. Solomon, M. (1992). Scientific rationality and human reasoning. Philosophy of Science, 439-455.
  4. "Introduction" (Chapter 1) in Koslowski, B. (1996). Theory and Evidence: The Development of Scientific Reasoning. Cambridge, MA: MIT Press
  5. Gopnik A. (1996). The scientist as child. Philosophy of Science, 63, 4, 485-514.
  6. Carey, S., & Spelke, E. (1996). Science and core knowledge. Philosophy of science, 515-533.
  7. Solomon, M. (1996). Commentary on Alison Gopnik's "The Scientist as Child". Philosophy of science, 63(4), 547-551.
  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.
  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
  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.
  11. Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments in preschoolers' exploratory play. Cognition, 120(3), 341–349.
  12. Schulz, L. (2012). The origins of inquiry: inductive inference and exploration in early childhood. Trends in cognitive sciences, 16(7), 382–389.
  13. Gopnik, A. & Wellman, H.M. (in press). Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory. Psychological Bulletin.

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