Ph.D. HCC — Cognitive Science Reading List
Background Textbook
1. Thagard, P. (2005). MIND: Introduction to cognitive science. MIT press.
Computational Theory of Mind
1. Turing, A.M. (1950). Computing Machinery and Intelligence. Mind, Volume LIX, Issue 236, Pages 433–460, https://doi.org/10.1093/mind/LIX.236.433
2. Newell, A. and Simon, H (1976). Computer science as empirical inquiry: symbols and search. Commun. ACM 19, 3, 113–126. https://doi.org/10.1145/360018.360022
Levels of Cognitive Theory
1. Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. San Francisco: W. H. Freeman. pp. xvi + 397.” Journal of Mathematical Psychology 27, 107-110. DOI:10.1016/0022-2496(83)90030-5
Philosophical Debates
1. Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417-424. DOI:10.1017/S0140525X00005756
2. Fodor, J. A. (1983). From pg 44 “Well then, what precisely…” – 101. Modularity of mind. Cambridge, MA: MIT Press. http://cognet.mit.edu/book/modularity-of-mind
Cognitive Architecture: Symbolic
1. Laird, J. E., Lebiere, C., & Rosenbloom, P. S. (2017). A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics. AI Magazine, 38(4), 13-26. https://doi.org/10.1609/aimag.v38i4.2744
2. Varma, S. (2017). “'The CAPS Family of Cognitive Architectures” in Susan E. F. Chipman (ed.), The Oxford Handbook of Cognitive Science, Oxford Handbooks https://doi.org/10.1093/oxfordhb/9780199842193.013.002.
Cognitive Architecture: Neural Networks
1. Rumelhart, D. E. (1998). The architecture of mind: A connectionist approach. In P. Thagard (Ed.), Mind readings (pp. 207-238). Cambridge, MA: MIT Press. https://doi.org/10.7551/mitpress/4626.003.0008
2. Hassabis D., Kumaran D., Summerfield C., Botvinick M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron. 95(2):245-258. doi: 10.1016/j.neuron.2017.06.011 PMID: 28728020
Embodied, Distributed, and Situated Cognition
1. Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review 9, 625–636. https://doi.org/10.3758/BF03196322
2. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139-159. Doi: 10.1016/0004-3702(91)90053-m
Social, Cultural, and Evolutionary Approaches
1. Tomasello, M. (2000). Chapters 1-3 from The Cultural Origins of Human Cognition. Psychology. DOI:10.2307/j.ctvjsf4jc
Problem solving and expertise
1. Robertson, S. I. (2017). “Well-structured Problem Solving” (pg 27-53), “Insight Problem Solving” (pg 176-204) and “Analogical Problem Solving” (pg 66-88) from Problem solving: Perspectives from cognition and neuroscience (2nd ed.). London: Routledge. https://learning.oreilly.com/library/view/problem-solving-2nd/9781317496007/?ar=
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2. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). National Research Council. “Chapter 2: Expertise”. How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press. doi: 10.17226/9853
Similarity and Analogy
1. Goldstone, R. L., & Son, J. Y. (2012). “Similarity” (pg 155-176). In K. J. Holyoak & R. G. Morrison (Eds.). The Oxford handbook of thinking and reasoning. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199734689.001.0001
2. Gentner, D., & Smith, L. A. (2013). “Analogical learning and reasoning” (pg 668-681). In D. Reisberg (Ed.), The Oxford handbook of cognitive psychology. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195376746.001.0001
3. Kunda, M., McGreggor, K., & Goel, A. K. (2013). A computational model for solving problems from the Raven’s Progressive Matrices intelligence test using iconic visual representations. Cognitive Systems Research, 22-23, 47–66. https://doi.org/10.1016/j.cogsys.2012.08.001
Concepts and Knowledge Representation
1. Murphy, G. L. (2002). Chapter 2-3 from The big book of concepts. MIT Press.
2. Carey, S. (2014). On learning new primitives in the language of thought: Reply to Rey. Mind & Language, 29(2), 133–166. https://doi.org/10.1111/mila.12045
3. Markman, A. B. (2002). “Knowledge representation” (pg 165-208). In H. Pashler & D. Medin (Eds.), Steven's handbook of experimental psychology: Memory and cognitive processes. John Wiley & Sons Inc.https://ebookcentral.proquest.com/lib/gatech/detail.action?docID=5294990
Metaphor and Blending
1. Fauconnier, G. (2001). “Conceptual blending and analogy” (pp. 255-285). In D. Gentner, K. J. Holyoak, & B. N. Kokinov (Eds.), The analogical mind: Perspectives from cognitive science. Cambridge, MA: MIT Press.
2. Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago, IL. The University ofChicagoPress. file:///Users/inventure1/Downloads/Metaphors%20We%20Live%20By.pdf
Creativity
1. Davis, N., Hsiao, C., Singh, K.Y., Lin, B. and Magerko, B. (2017). Creative Sense-Making: Quantifying Interaction Dynamics in Co-Creation. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (C&C '17). Association for Computing Machinery, New York, NY, USA, 356–366. https://doi.org/10.1145/3059454.3059478
2. Kaufman, J. C., & Glăveanu, V. P. (2019). A review of creativity theories: What questions are we trying to answer? In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (pp. 27–43). Cambridge University Press. https://doi.org/10.1017/9781316979839.004