Cognitive Science Summaries

edited by Jim Davies

Contributing

Here is the template for the summaries, if you care to use it. Don't worry about converting it if you have already written the summary in another format-- just email me the text file and I will take care of the rest.

To help you understand these summaries, it might be helpful to look at the Other online resources.

Assigning the Writing of a Summary as a class project

Citing the Summaries

Furcy, D. (1999). Summary of Amarel 1968 On representations of problems of reasoning about actions. In J. Davies (ed.) Cognitive Science Summaries Internet Web site. URL:http://www.cc.gatech.edu/~jimmyd/summaries/.

Where D. Furcy is the summary author and 1999 is the year the summary is written. This information is on each summary page.

The Summaries:

• Analogy
  • Bichindaritz, & Sullivan, K. M. (1998) Reasoning from Knowledge Supported by More or Less Evidence in a Computerized Decision Support System for Bone-Marrow Post-Transplant Car. AAAI Spring Symposium in Multimodal Reasoning. Technical Report SS-98-04. 85--90 I.
  • Bhatta, S. R. & A. K. Goel (1997). Design patterns: A computational theory of analogical design. In the Proceedings of IJCAI-97 workshop on "Using Abstraction and Reformulation in Analogy."
  • Beveridge, M. & Parkins, E. (1987). Visual representation in analogical problem solving. Memory & Cognition: 15(3), 230--237.
  • Bhatta, S. R. & Goel, A. (1997). Learning generic mechanisms for innovative strategies in adaptive design. The Journal of the Learning Sciences, 6(4), 367--396.
  • Carbonell, J. (1986). Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In Michalski, R., Carbonell, J., & Mitchell, T. (Eds.) Machine Learning: An Artificial Intelligence Approach. Morgan Kaufman Publishers: San Mateo, CA.
  • Casakin, H, & Goldschmidt, G. (1999) Expertise and the use of visual analogy: Implications for design education. Design Studies, 20:153--175.
  • Catrambone, R. & Holyoak, K. J. (1989) Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology 15:6, 1147--1156.
  • Craig, D. L., Catrambone, R., & Nersessian, N. J. (2003). The role of perceptually represented structure in analogical problem solving. Unpublished manuscript.
  • Croft, D. & Thagard, P. (2000). Dynamic imagery: A computational model of motion and visual analogy. Unpublished manuscript.
  • Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology 12, 306--355.
  • Goel, A., Bhatta, S. & Stroulia, E. (1997) Kritik: An Early Case-Based Design System. In Maher, M. and Pu, P. (Eds.)Issues and Applications of Case-Based Reasoning in Design, Mahwah, NJ: Erlbaum, pages 87--132.
  • Falkenhainer, B., K. D. Forbus, D. Gentner (1990). The Structure mapping engine: algorithm and examples. Artificial Intelligence (41) pp1-63.
  • Forbus, K.D, Ferguson, W. R. Gentner, D. (1994). Incremental Structure Mapping, Proceedings of the 14th Annual Conference of the Cognitive Science Society, 313--318.
  • Falkenhainer, B.,(1990) A unified approach to explanation and theory formation. In Shrager, J. & Langley, P. (eds.) Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufman: San Meteo, CA. pp 157--196.
  • Ferguson, R. W. & Forbus, K. D. (1998) Telling juxtapositions: Using repetition and alignable difference in diagram understanding. In Holyoak, K., Gentner, D., & Kokinov, B. (Eds.) Advances in Analogy Research, 109--117. Sofia: New Bulgarian University.
  • Gentner, D. (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science. 7 (2), pp155-170. (see also this shorter summary.)
  • Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology 12, 306--355.
  • Hofstadter, D. R.& Mitchell, M. (1995). The copycat project: A model of mental fluidity and analogy-making. In Hofstadter, D. and the Fluid Analogies Research group, Fluid Concepts and Creative Analogies. Basic Books. Chapter 5: 205--267.
  • Holyoak, K. J., & Thagard, P. (1989). A computational model of analogical problem solving. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 242-266). Cambridge: Cambridge University Press.
  • Holyoak, K. J. & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13: 295--355.
  • Holyoak, K. J. & Thagard, P. (1997). The analogical mind. American Psychologist.52(1)35-44.
  • Jurisica, I. & Glasgow, J. (2000). Extending case-based reasoning by discovering and using image features in in-vitro fertilization. In B. Bryant, J. Carroll, E. Damiani, H. Haddad, & D. Oppenheim (Eds.) Proceedings of the 2000 ACM symposium on Applied computing - Volume 1 March 19-21, Villa Olmo,
  • Jurisica, I. & Glasgow, J. (2004). Applications of case-based reasoning in molecular biology. AI Magazine, 25(1), 85--95.
  • Kadar-Cabelli, S. (1985). Purpose-directed analogy. Proceedings of the seventh annual conference of the Cognitive Science Society. Lawrence Erlbaum publishers.
  • McGraw, Gary & Douglas R. Hofstadter. (1993) Perception and Creation of Alphabetic Style. In Artificial Intelligence and Creativity: Papers from the 1993 Spring Symposium, AAAI Technical Report SS-93-01, AAAI Press.
  • McGraw, Gary, Daniel Drasin. (1993). Recognition of Gridletters: Probing the Behavior of Three Competing Models. In Proceedings of the Fifth Midwest AI and Cognitive Science Conference, pages 63-67, April 1993.
  • McGraw, Gary, John Rehling & Robert Goldstone (1994). Letter Perception: Toward a conceptual approach. In the Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, pages 613-618, Atlanta, GA, August 1994.
  • Matlock, T., Ramscar, M. & Boroditsky, L. (2005). On the Experiential Link Between Spatial and Temporal Language. Cognitive Science, 29(4). 655--664.
  • Novick, L. R. & Holyoak, K. J. (1991). Mathematical problem solving by analogy. Journal of Experimental Psychology: Learning, Memory, and Cognition. Vol 17 (3). 398--415.
  • Perner, P. (2001). Why case-based reasoning is attractive for image interpretation. In D. Aha and I Watson (Eds.) Case-Based Reasoning Research and Development. Springer-Verlag, LNAI 2080, 27--44.
  • Reeves, L. M. & Weisberg, R. W. (1994). The role of content and abstract information in analogical transfer. Psychological Bulletin. Vol. 115 (3). 381--400.
  • Rehling, J., & Hofstadter, D. (1997) The Parallel Terraced Scan: An Optimization for an Agent-Oriented Architecture. 1997 IEEE International Conference on Intelligent Processing Systems.
  • Thagard, P. & Hardy, S. (1992) Visual thinking and the development of Dalton's atomic theory. Proceedings of the Ninth Canadian Conference on Artificial Intelligence. Vancouver. 30--37.
  • P.H. Winston (1980). Learning and Reasoning by Analogy, Communications of the ACM, (23) 12, December 1980
  • Tony Veale & Mark T. Keane: Conceptual Scaffolding: A Spatially Founded Meaning Representation for Metaphor Comprehension. Computational Intelligence 8: 494-519 (1992)
  • Winston, P. H. (1992). Artificial Intelligence. Third Addition. Addison-Wesley.
• Artificial Intelligence in general
  • Doyle, J. (1988) Big problems for artificial intelligence. AI Magazine. 9(1) 19-22.
  • Hayes, P. (1973) The Frame Problem and Related Problems in AI. Artificial and Human Thinking, A. Elithorn and D. Jones (eds.), Jossey-Bass (see also this shorter summary)
  • D. Lenat, The Ubiquity of Discovery. Artificial Intelligence, 9, 1977.
  • Russell, S. (1995). Rationality and Intelligence. Artificial Intelligence, 94, 57–77.
  • Simon H.A. (1995). Explaining the Ineffable: AI on the Topics of Intuition, Insight and Inspiration. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1, 939-948.
• Art Science (Psychology of art, computational aesthetics)
  • Csikszentmihalyi, M. & Robinson R. E. (1990). The Art of Seeing: An Interpretation of the Aesthetic Encounter The J. Paul Getty Museum and The Getty Education Institute for the Arts.
  • Rafael Ramirez and Amaury Hazan, A Learning Scheme for Generating Expressive Music Performances of Jazz Standards, IJCAI, pp. 1628-1629, 2005.
• Arithmetic
  • Ashcraft, M. H. & E. H. Stazyk (1981). Mental addition: A test of three verification models. Memory & Cognition. v9 pp 185-196.
  • Bernoussi, M. (1998) Individual differences in cognitive addition. The Psychological Record. v48, pp325-332.
  • Gray, C. & G. Mulhern (1995). Does children's memory for addition facts predict general mathematical ability? Perceptual and Motor Skills. v81, pp163-167.
  • Lebiere, C. (1998) The dynamics of cognition: An ACT-R model of cognitive arithmetic. Carnegie Mellon University technical report #CMU-CS-98-186.
• Attention
  • Pre-attentive processes
    • Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.
• Automaticity
  • Schneider, W. & R. M. Shiffrin. (1977). Controlled and automatic human information processing: 1. Detection, search, and attention. Psychological Review, 84, pp1-66.
• Behavior-based systems
  • Brooks R. (1986). A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, 2 (1)
  • Brooks, R. A. (1989). "A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network," MIT AI Lab Memo 1091, February 1989.
  • Brooks, R. A. (1991). "Intelligence Without Reason," Proceedings of 12th Int. Joint Conf. on Artificial Intelligence, Sydney, Australia, August 1991, pp. 569--595.
• Cognitive Architectures
  • Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261-295. (See also this shorter summary)
• Connectionism
  • Hampson, P. J. & Morris, P. E. (1996). Chapter 13: Connectionism. In Understanding Cognition, Blackwell, Cambridge MA.
• Design
  • de Kleer J. (1984).How Circuits Work. Artificial Intelligence, 24,pp 205-280. (a shorter summary)
  • Faltings, B. and Sun, K. (1995). "FAMING: Supporting Innovative Mechanism Shape Design," Computer-Aided Design.
  • Gomes, P., Seco, N., Pereira, F. C., Paiva, P., Carreiro, P., Ferreira, J. L., and Bento, C. (2003) The importance of retrieval in creative design analogies. in Creative Systems: Approaches to Creativity in AI and Cognitive Science. Workshop program in the Eighteenth International Joint Conference on Artificial Intelligence. pp. 37--45. Acapulco, Mexico.
  • Schon, D. A. & Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies. 13:2 135--156.
  • Herbert A. Simon (1969).The Sciences of the Artificial (First Edition), MIT Press. Cambridge, MA (a shorter summary)
• Distributed & Multi-Agent Systems
  • Arkin, R.C. (1998), Social Behavior. In Behavior-Based Robotics,, Chapter 9. MIT Press, Cambridge, MA.
  • Maes, P. (1990), Situated Agents Can Have Goals, Robotics and Autonomous Systems, 6:49-70.
  • Parker, L. E. (1998). ALLIANCE: An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automaton. v14, no. 2, April 1998.
  • Reynolds, C. W. (1987), Flocks, herds, and schools: A distributed behavioral model, Computer Graphics, 21(4):25-34.
• Echoic Memory
• Expertise
  • Bosman, E. A., & Charness, N. (1996) Age-related differences in skilled performance and skill acquisition. In F. Blanchard-Fields & T. M. Hess (Eds.), Perspectives on cognitive change in adulthood and aging (pp. 428-453). New York: McGraw-Hill.
  • Camerer, C. F., & Johnson, E. J. (1991). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In K. A. Ericsson & J. Smith (Eds.), Towards a general theory of expertise: Prospects and limits (pp. 195-217). New York: Cambridge Press.
  • Charness, N. (1997). Can acquired knowledge compensate for age-related declines in cognitive efficiency: Evidence from chess and bridge. Manuscript in preparation.
  • Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 16, pp. 1-58). New York: Academic Press.
  • Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243, 1668-1674.
  • Ericsson, K. A., & Staszewski, J. J. (1989). Skilled memory and expertise: Mechanisms of exceptional performance. In D. Klahr & K. Kotovsky (Eds.), Complex information processing: The impact of Herbert A. Simon (pp. 235-267). Hillsdale, NJ: Lawrence Erlbaum.
  • Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211-245.
  • Ericsson, K. A. & Charness, N. (1997). Cognitive and developmental factors in expert performance. In P. J. Feltovich, K. M. Ford, & R. R. Hoffman (Eds.), Expertise in context: Human and machine (pp 3-41). Cambridge, MA: MIT Press.
  • Ericsson, K. A. (1996). The acquisition of expert performance: An introduction to some of the issues. In K. A. Ericsson (Ed.), The road to excellence: The acquisition of expert performance in the arts and sciences, sports, and games (pp. 1-50). Mahwah, NJ: Lawrence Erlbaum.
  • Ericsson, K. A., Krampe, R. Th., & Tesch-Roemer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363-406.
  • Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal adaptation to task. Annual Review of Psychology, 47, 273-305.
  • Gardner, H. (1995). Expert performance: Its structure and acquisition: Comment. American Psychologist, 50, 802-803.
  • Krampe, R. Th., & Ericsson, K. A. (1996). Maintaining excellence: Deliberate practice and elite performance in younger and older pianists. Journal of Experimental Psychology: General, 125, 331-359.
  • Morrow, D. G., Leirer, V. O., & Altieri, P. A. (1992). Aging, expertise, and narrative processing. Psychology and Aging, 7, 376-388.
  • Salthouse, T. A. (1984). Effects of age and skill in typing. Journal of Experimental Psychology: General, 13, 345-371.
  • Salthouse, T. A. (1989). Aging and skilled performance. In A. M. Colley & J. R. Beech (Eds.), Acquisition and performance of cognitive skills (pp. 247-263). Chichester, UK: John Wiley & Sons.
  • Schutlz, R., Musa, D., Staszewski, J., & Siegler, R. S. (1994). The relationship between age and major league baseball performance: Implications for development. Psychology and Aging, 9, 274-286.
  • Simonton, D. K. (1997). Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104, 66-89.
• Iconic Memory and Imagery
  • Aginsky, V. & Tarr, M. J. (2000). How are different properties of a scene encoded in visual memory? Visual Cognition, 7 (1/2/3), 147--162.
  • Anderson, M. & McCartney, R (2003). Diagram processing: Computing with Diagrams. Artificial Intelligence. 145. pp 181--226.
  • Colheart, M. (1980). Iconic memory and visible persistence. Perception and Psychophysics, 27, 183-228.
  • Do, E. Y. & Gross, M. D. (2001). Thinking with diagrams in architectural design. Artificial Intelligence Review. 15 135--149.
  • Glasgow, J. I. (1993). The imagery debate revisited: A computational perspective. Computational Intelligence 9:4, 309--333.
  • Glasgow, J. & Papadias, D. (1998). Computational imagery. In Thagard, P. Mind Readings. Cambridge, MA: MIT Press.
  • Glasgow, J., Fortier, S., Conklin, D., Allen, F, & Leherte, L. (2004). Knowledge representation for molecular scene analysis. Unpublished manuscript.
  • Glasgow, J., Epstein, S., Meurice, N., & Vercauteren, D. (2004). Spatial motifs in design. In Proceedings of the Third International Conference on Visual and Spatial Reasoning in Design Massachusettes Institute of Technology, Cambridge, MA.
  • Glasgow, J., Narayanan, N. H., Chandrasekaran, B. (1995). Diagrammatic Reasoning: Cognitive and Computational Perspectives. AAAI Press/MIT Press: Cambridge, MA.
  • Haber, R. N. (1983). The impending demise of the icon: A critique of the concept of iconic storage in visual information processing. The Behavioral and Brain Sciences, 6, 1-54.
  • Hegarty, M. (1992). Mental animation: Inferring motion from static displays of mechanical systems. Journal of Experimental Psychology: Learning, Memory, and Cognition. 18(5), 1084--1102.
  • Forbus, K. D. (1995). Qualitative spatial reasoning framework and frontiers. In Diagrammatic Reasoning, Glasgow, J., Narayanan, N. H. A., and Chandrasekaran, B., AAAI Press, 1995. pp 183--202.
  • Larkin, J. & Simon, H. (1987) Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11:65-99.
  • Loftus, G. R., Johnson, C. A., & Shimamura, A. P. (1985). How much is an icon worth? Journal of Experimental Psychology: Human Performance and Perception, 11, 1-13.
  • McNamara, T. P. (1986). Mental representations of spatial relations. Cognitive Psychology 18. 87--121.
  • Narayanan, N. H., Suwa, M. & Motoda, H. (1994). How things appear to work: Predicting behaviors from device diagrams. Proceedings of the 12th National Conference on Artificial Intelligence, AAAI Press, pp. 1161-1167.
  • Palmer, S. E. (1975). Visual perception and world knowledge: Notes on a model of sensory-cognitive interaction. In D. A. Norman & D. E. Rumelhart (Eds.), Explorations in cognition (pp. 279-307). San Francisco: Freeman.
  • Pylyshyn, Z. W. (1978). Imagery and artificial intelligence. from C.W. Savage, ed., Perception and Cognition. Issues in the Foundations of Psychology, Minnesota Studies in the Philosophy of Science, vol. 9, Minneapolis: University of Minnesota Press) pp. 19-55
  • Richardson, D. C., Spivey, M. J., Edelman, S. & Naples, A. J. (submitted). Image schemas of concrete and abstract verbs: Experimental evidence.
  • Schwartz, D. L. & Black, J. B. (1996) Shuttling between depictive models and abstract rules: Induction and fallback. Cognitive Science. 20. 457--497.
  • Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs,
  • Slezak, P. (1991). Can images be rotated and inspected? A test of the pictorial medium theory. In the Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society (pp. 55-60). Hillsdale, NJ: Erlbaum
  • Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs, 74 1-29.
  • Tarr, M. J. (1994). Visual representation: From features to objects. in V. S. Ramachandran (Ed.) Encyclopedia of Human Behavior. San Diego, CA: Academic Press, Vol. 4, 503--512.
  • Tarr, M. J. & Black, M. J. (1994). A computational and evolutionary perspective on the role of representation in vision. CVGIP: Image Understanding 60(1) 65--73.
• Imagery
  • Farah, M.J. (2000). The neural bases of mental imagery. In M,S. Gazzaniga (Ed), The cognitive neurosciences (2^nd ed., 965-974). Cambridge, MA: MIT Press.
  • Funt, B. V. (1980). Problem-solving with diagrammatic representations. Artificial Intelligence 13, No. 3, pp. 201--230.
  • Hinton, G. (1979). Some demonstrations of the effects of structural descriptions in mental imagery. Cognitive Science, 3, 231--250.
  • Langston, W., Kramer, D. C., & Glenberg, A. M. (1998). The representation of space in mental models derived from text. Memory and Cognition, 26, 247-262.
  • Salthouse, T. A., & Mitchell, D. R. D. (1990). Effects of age and naturally occurring experience on spatial visualization performance. Developmental Psychology, 26, 845-854.
  • Thomas, N. J. T. (1999). Are theories of imagery theories of imagination? An active perception approach to conscious mental content. Cognitive Science 23, 207--245.
• Inference
  • Bruner, J.S. (1957). Going beyond the information given. In J.S. Bruner, E, Brunswik, L. Festinger, F. Heider, K.F. Muenzinger, C.E. Osgood, & D. Rapaport, (Eds.), Contemporary approaches to cognition (pp. 41-69). Cambridge, MA: Harvard University Press. [Reprinted in Bruner, J.S. (1973). Beyond the information given (pp. 218-238). New York: Norton.]. [pages 218-222]
• Intelligent Tutoring Systems
  • Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-on-one tutoring. Applied Cognitive Psychology, vol. 9, pp495-522.
• Knowledge Systems
  • Amarel, S. (1968). On representations of problems of reasoning about actions, Machine Intelligence, (3), 131--171 (see this shorter summary)
  • Chandrasekaran, B. (1988) Generic Tasks as Building Blocks for Knowledge-Based Systems: The Diagnosis and Routine Design Examples. Knowledge Engineering Review, 3 (3). (see this shorter summary)
  • Hayes, P. (1973) The Frame Problem and Related Problems in AI. Artificial and Human Thinking, A. Elithorn and D. Jones (eds.), Jossey-Bass (see also this shorter summary)
  • Lenat, D. & R. Guha (1990)., Building Large Knowledge Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley Publishing. (See also this shorter summary)
  • D. McDermott and J. Doyle (1980). Nonmonotonic logic 1, Artificial Intelligence", Vol. 13, p41-72, 1980. (see this shorter summary)
  • M. Minsky, A Framework for Representing Knowledge. The Psychology of Computer Vision, P. H. Winston (ed.), McGraw-Hill 1975.
  • A. Newell, The Knowledge Level. Artificial Intelligence, 18 (1), 1982.
• Language
  • Bock, K. & Garnsey, S. M. (1999). Chapter 14: Language Processing. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
  • Deacon, T. W. (1999). Chapter 13: Language evolution and neuromechanisms. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
  • Donald, M. (1991) Origins of the modern mind: three stages in the evolution of cognition and culture. The President and Fellows of Harvard College. Chapter 1.
  • Graesser, A. & Tipping, P. (1999). Chapter 24: Understanding Texts. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
  • Hunt, E. & Agnoli, F. (1991). The Worfian hypothesis: A cognitive psychology perspective. Psychological Review, 98 (3). pp 377-389.
  • Lakoff, G. & Kovecses, Z. (1987). Chapter 8: The cognitive model of anger inherent in American English. In Cultural Models in Language and Thought, Holland, D. & Quinn, N. (eds.)
  • Lakoff, G. & M. Johnson. (1999) Philosophy In The Flesh: The Embodied Mind and Its Challenge to Western Thought. Basic Books.
  • Landaur, T. K. (1998). Learning and Representing verbal meaning: The latent semantic analysis theory. Current Directions is Psychological Science, 7 (5) pp 161-164
  • Luger, G. F. (1994). Chapter 13: Language representation and processing. In Cognitive Science: The Science Of Intelligent Systems. Academic Press, San Diego, CA
  • Miles, H. L. (1983) Apes and language: The search for communicative competence. In Language in Primates: Perspectives and Implications, J. De Luce & H. T. Wilder (eds.). Springer-Verlag, New York. pp 43-61.
  • Miles, L. (1990) The cognitive foundations for reference in a signing orangutan. In "Language" and intelligence in monkeys and apes by Parker, S. T. and K. R. Gibson (Eds) 1990. p 511-539.
  • Miles, L. W. & S. E. Harper (1994) "Ape language" studies and the study of human language origins. in Hominid Culture in Primate Perspective, by Quiatt, D. D. & J. Itani (eds). University Press of Colorado. pp253-278
  • Partee, B. H. (1999). Semantics. In The MIT Encyclopedia of the Cognitive Sciences, Wilson, R. A. & Keil, F. C. (eds.) MIT Press, Cambridge, MA. pp 739-741.
  • Peterson, J., K. Mahesh & A. Goel (1994). Situating natural language understanding within experience-based design. Int. J. Human-Computer Studies. 41, 881-913
  • Quillian, M. (1968). Semantic Memory, in M. Minsky (ed.), Semantic Information Processing, pp 227-270, MIT Press; reprinted in Collins & Smith (eds.), Readings in Cognitive Science, section 2.1 (see also this shorter summary)
  • Schank, R. C. (1972). Conceptual Dependency: {A} Theory of Natural Language Understanding, Cognitive Psychology, (3)4, 532-631 (see also this shorter summary)
  • Schank, R. C. and Abelson, R. P. (1977) Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures (Chap. 1-3), L. Erlbaum, Hillsdale, NJ (see also this shorter summary)
  • Woods, W. (1975), What's in a Link: Foundations for Semantic Networks, in D.G. Bobrow & A. Collins (eds.), Representation and Understanding, Academic Press; reprinted in, Collins & Smith (eds.), Readings in Cognitive Science, section 2.2. (see also this shorter summary)
  • Worden, R. (1998) The evolution of language from social intelligence. to be published in The Evolution of Phonology and Syntax, Hurford, Studdert-Kennedy & Knight (eds) Cambridge University Press. pp148-166.
  • Zurif, Edgar B. (1990). Language and the brain. In Language: An invitation to Cognitive Science, Vol 1. Osherson, D. N. & Lasnik, H. MIT Press, Cambridge, MA. (pp 177-198)
• Machine Learning
  • Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369-406. (See also this other summary and this one too.)
  • Atkeson, C. G. and S. Schaal (1997). Robot Learning From Demonstration, Machine Learning: Proceedings of the Fourteenth International Conference (ICML '97), Edited by Douglas H. Fisher, Jr. pp. 12-20, Morgan Kaufmann, San Francisco, CA, 1997.
  • Fisher, D.H. (1987), Knowledge Acquisition via Incremental Conceptual Clustering, Machine Learning 2:139-172, reprinted in Shavlik & Dietterich (eds.), Readings in Machine Learning, section 3.2.1.
  • K. Hammond (1989), Case-Based Planning: Viewing Planning as a Memory Task, chapters 1, Academic Press.
  • K. Hammond (1989), Case-Based Planning: Viewing Planning as a Memory Task, chapters 2, Academic Press.
  • K. Hammond (1989), Case-Based Planning: Viewing Planning as a Memory Task, chapter 3, Academic Press.
  • Leherte, L., Glasgow, J., Baxter, K., Steeg, E., and Fortier, S. (1997). Analysis of three-dimensional protein images. Journal of Artificial Intelligence Research, 7, 125--159.
  • Mitchell, T., R. Keller, and S. Kedar-Cabelli, Explanation-Based Generalization: A Unifying View. Machine Learning, 1, 1982. (see also this shorter summary)
  • J.R. Quinlan (1986), Induction of Decision Trees, Machine Learning 1:81-106; reprinted in Shavlik & Dietterich (eds.), Readings in Machine Learning, section 2.2.1.
  • Ram, A. & Leake, D. (1995). Learning, Goals, and Learning Goals. In Goal-Driven Learning. by A. Ram & D. Leake, (eds) Chapter 1. MIT Press/Bradford Books, Cambridge, MA.
  • Stanley, K. Miikkulaine, R. (2004). Competitive Coevolution through Evolutionary Complexification. Journal of Artificial Intelligence Research, 21, 63-100.
  • Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM 1984 pp1134-1142.
• Memory
  • Haberlandt, K. (1999). Chapter 1: Introduction. In Human Memory: Exploration and Applications, Allyn & Bacon. Needham Heights, MA.
  • Haberlandt, K. (1999). Chapter 5: Memory for skills. In Human Memory: Exploration and Applications, Allyn & Bacon. Needham Heights, MA.
  • Roediger, H. L. & Goff, L. M. (1999). Chapter 17: Memory. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
  • Allen, R. & Reber, A. S. (1999). Chapter 23: Unconscious intelligence. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
• Numerical Machine Learning
  • D.H. Ballard (1997) An Introduction to Natural Computation, MIT Press.
• Neural Networks (artificial) and Connectionism
  • de Garis, H. (1990). Building Artificial Nervous Systems Using Genetically Programmed Neural Network Modules. Machine Learning: Proceedings of the Seventh International Converence, 132-139.
• Neuroscience
  • Frith, C. D. (1999). Chapter 29: Deficits and pathologies. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
  • Buckner, R. L. & Petersen, S. E. (1999). Chapter 32: Neuroimaging. In A Companion to Cognitive Science, Bechtel, W. & Graham, G. (eds) Blackwell, Malden MA.
• Perception
  • computer perception
    • Brunelli, R. & T. Poggio (1993), Face Recognition: Features versus Templates, IEEE Transactions on PAMI, 15(10):1042-1052.
    • Cedras, C. & M. Shah (1995), Motion-Based Recognition: A Survey, IVC, 13(2):129-155.
    • Fermuller, C.& Y. Aloimonos (1995). Vision and Action, IVC, 13(10):725-744
    • Gupta, A.[Amarnath], Jain, R.[Ramesh], Visual Information Retrieval, CACM(40), No. 5, May 1997, pp. 70-79.
    • Jain, R. (1997), Visual Information Management, CACM, 40(12):30-32.
    • Liang, Z. (1993). Tissue classification and segmentation of MR images. IEEE Engineering in Medicine and Biology. March, pp 81--85.
    • Rowley, H.A., S. Baluja, & T. Kanade (1998), Neural Network-Based Face Detection, IEEE Transactions on PAMI, 20(1):23-38.
  • human perception (see also mental imagery)
    • Ellis, R. & Humphreys, G. W. (1999). Chapter 4: Perception. In Connectionist Psychology: A Text with Readings. Psychology Press.
    • Eysenck, M. W. & Keane, M. T. (1995). Chapter 4: Theories of perception, movement, and action. In Cognitive Psychology: A Student's Handbook, Lawrence Erlbaum, Hillsdale, USA.
    • Barsalou, L.W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577-609.
    • Ferguson, R. W., & Forbus, K. D. (2000). GeoRep: A flexible tool for spatial representation of line drawings, Proceedings of the 18th National Conference on Artificial Intelligence. Austin, Texas: AAAI Press.
    • Gibson, E.J. (1977). How perception really develops: A view from outside the network. In Laberge & Samuels, (Eds.). Basic processes in reading: Perception and comprehension (pp. 155-173). Mahwah, NJ: Erlbaum.
    • Kellman, P. & Arterberry, M.E. (1998). Chapter 5: Object Perception. The cradle of knowledge: Development of perception in infancy. Cambridge: M.I.T. Press.
    • Kosslyn, S. M. & Shwartz, S. P. (1977). A simulation of visual imagery. Cognitive Science 1, 265--295.
    • Kosslyn, S. M. (1994) Image and Brain: The Resolution of the Imagery Debate. MIT Press, Cambridge, MA.
    • D. Marr, Vision. Freeman Publishers, 1982.
    • Warren, R.M. (1970). Restoration of missing speech sounds. Science, 167, 392--393.
    • P. Winston, Learning Structural Descriptions from Examples. The Psychology of Computer Vision, P. Winston (ed.), 1975.
• Philosophy of AI
  • McCarthy, J. (1995). What has AI in Common with Philosophy?. Proceedings of the 14th International Joint Conference on AI, Montreal, 2041-2042.
  • Sloman, A. (1995) A Philosophical Encounter. Proceedings of 14th International Joint Conference on Artificial Intelligence. 2037--2040.
• Planning
  • Blum, Avrim L. and Furst, Merrick L. (1997) Fast Planning Through Planning Graph Analysis. Artificial Intelligence. 90. 1997. p281-300.
  • B. Bonet, G. Loerincs and H. Geffner (1997). A Robust and Fast Action Selection Mechanism for Planning, Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference (AAAI-97/IAAI-97)
  • T. Dean & S. Kambhampati (1996), Planning and Scheduling. The CRC Handbook of Computer Science and Engineering, A. B. Tucker (Ed.), CRC press, 1997. pp614-636.
  • Gutman, J., Fukuchi, M., & Fujita, M. (2005) Real-time path planning for humanoid robot navigation. IJCAI-05, 1232-1238.
  • Haddawy, P. (1996). Focussing Attention in Anytime Decision-Theoretic Planning, SIGART Bulletin, Vol. 7, No. 2.
  • Hampson, P. J. & Morris, P. E. (1996). Chapter 8: Planning and actions: Successes and failures. In Understanding Cognition, Blackwell, Cambridge MA.
  • H. Kautz and B. Selman (1996). Pushing the Envelope: Planning, Propositional Logic and Stochastic Search, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, OR.
  • C.A. Knoblock (1994). Automatically Generating Abstractions for Planning, Artificial Intelligence, 68(2).
  • D. McDermott, Planning and Acting. Cognitive Science, 2, 1978. See also this longer outline
  • M. Stefik, Planning and Meta-Planning (MOLGEN: Part 2). Artificial Intelligence 16 (2), 1981. (see also this structured outline and this other other structured outline)
  • D.S. Weld (1994). An Introduction to Least-Commitment Planning, AI Magazine, 15(4):27-61.
  • Wilensky, Robert (1980). Meta-Planning: Representing and Using Knowledge About Planning in Problem Solving and Natural Language Understanding.
• Preattentive Processes
  • Loftus, G. R., & Duncan, J., & Gehrig, P. (1992). On the time course of perceptual information that results from a brief visual presentation. Journal of Experimental Psychology: Human Performance and Perception, 18, 530-549.
  • Logan, G. D. (1992). Attention and preattention in theories of automaticity. American Journal of Psychology, 105, 317-339.
  • Treisman, A., Vieira, A., & Hayes, A. (1992). Automatic and preattentive processing. American Journal of Psychology, 105, 341-362.
• Problem Solving
  • Charness, N. (1981). Aging and skilled problem solving. Journal of Experimental Psychology: General, 110, 21-38.
  • Hayes-Roth, B. and F. Hayes-Roth (1979) A Cognitive Model of Planning. International Joint Conference on Artificial Intelligence (See also this shorter summary.
  • J. Kolodner and D. Leake, A Tutorial Introduction to Case-Based Reasoning. To appear in Case-Based Reasoning: Experiences, Lessons, and Future Directions, D. Leake (ed.), AAAI Press, 1996. (See also this longer summary.)
  • Monaghan, J. M. & Clement, J. (1999). Use of computer simulation to develop mental simulations for understanding relative motion concepts. International Journal of Science Education 21(9), 921--944.
  • Laird J., Newell A., and Rosenbloom P. (1987) SOAR: An Architecture for General Intelligence. Artificial Intelligence, 33.
  • A. Newell and H. Simon, GPS, A Program that Simulates Human Thought. Computers and Thought, E. A. Feigenbaum and J. Feldman (eds.), R. Oldenbourg KG., 1963.
  • Newell, A. & H.A. Simon (19??), The Theory of Human Problem Solving. Reprinted in Readings in Cognitive Science, Collins & Smith (eds.), section 1.3, pp. 33. (see also this shorter summary)
  • M. M. Veloso. (1994) PRODIGY/ANALOGY: Analogical Reasoning in General Problem Solving. In: S. Wess, K. D. Altho, M. M. Richter (eds). Topics on Case-Based Reasoning, Selected Papers from the First European Workshop on Case-Based Reasoning|EWCBR'93, Vol. 837 of Lecture Notes in Articial Intelligence, Springer-Verlag, pp. 33-50.
  • Veloso, M.M. & Carbonell, J.G.(1993). Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization. Machine Learning, 10(3):249-278.
• Reasoning under Uncertainty
  • R. Dechter (1996), Bucket Elimination: A Unifying Framework for Probabilistic Inference, in Proceedings of Uncertainty in AI, Portland, Oregon.
  • Russell, S. & P. Norvig (1994) Probabilisitc Reasoning Systems, Chapter 15: Artificial Intelligence: A Modern Approach.
  • S. Russell and E. Wefald (1991). Do the right thing : studies in limited rationality (Chapter 1: Limited Rationality), MIT Press, Cambridge, MA.
  • S. Russell and E. Wefald (1991). Do the right thing : studies in limited rationality (Chapter 2: Metareasoning Architectures), MIT Press
  • S. Russell and E. Wefald (1991). Rational Metareasoning (Chapter 3: Do the Right Thing), MIT Press
  • S. Russell and E. Wefald (1991). Do the right thing : studies in limited rationality (Chapter 4: Application to Game-Playing), MIT Press
  • S. Russell and E. Wefald (1991). Do the right thing : studies in limited rationality (Chapter 5: Application to Problem-Solving Search), MIT Press
• Science Studies
  • Bowker, G. C. & Star, S. L. (1999) Sorting Things Out: Classification and Its Consequences. Parts II and III. MIT Press: Cambridge, MA.
  • Dunbar, K. & Blanchette, I. (2001). The invivo/invitro approach to cognition: the case of analogy. Trends in Cognitive Sciences, 5, 334--339.
  • Giere, R. N. (2000). Scientific cognition as distributed cognition. Manuscript draft.
  • Kuhn, T. H. The Structure of Scientific Revolutions
  • Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In Giere, R. N. (ed.) Cognitive Models of Science. 5--22. University of Minnesota Press. Minneapolis, MN.
  • Nersessian, N. J. (1984). Maxwell's `Newtonian aether-field'. In Nersessian, N. J. Faraday to Einstein: Constructing Meaning in Seientific Theories. Kluwer, Dordrecht. pp 68-93.
  • Nersessian, N. J. (1994). Opening the black box: Cognitive science and the history of science. In Thackray, A. (ed.) Constructing Knowledge in the History of Science. Osiris 10, 1995.
  • Nersessian, N. J. (1997) Abstraction via generic modeling in concept formation in science. In Idealization and Abstraction in Science. M. R. Jones and N. Cartwright (eds.). Amsterdam: Editions Rodopi, 1997.
  • Nersessian, N. J. (1998). Model-based reasoning in conceptual change. in Model-Based Reasoning in Scientific Discovery, L. Magnani, N. Nersessian & P. Thagard (eds). Plenum.
  • Schrager, J. (1990). Commonsense perception and the psychology of theory formation. In Shrager, J. & Langley, P. (Eds.) Computational Models of Scientific Discovery and Theory Formation. Morgan Kaufman, San Mateo, CA. 437--470.
• Similarity
  • Resnik, P. (1995) Using Information Content to Evaluate Semantic Similarity in a Taxonomy. International Joint Conference for Artificial Intelligence (IJCAI-95). 448-453.
• Skill Aquisition and Expert Performance
  • Ackerman, P. L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 117, 288-318.
  • Ackerman, P. L. (1992). Predicting individual differences in complex skill acquisition: Dynamics of ability determinants.
• Theorem proving
  • C. Hewitt, PLANNER: A Language for Proving Theorems in Robots. International Joint Conference on Artificial Intelligence, 1969.
• Theory of Mind
  • Pynadath D. V. & Marsella, S. C. (2005) PsychSim: Modeling Theory of Mind with Decision-Theoretic Agents. Proceedings of the International Joint Conference on Artificial Intelligence. 1181--1186.
• Visual Languages and reasoning
  • Freksa C. & Zimmermann K. (1993). On the Utilization of Spatial Structures for Cognitively Plausible and Efficient Reasoning. Proceedings of the Workshop on Spatial and Temporal Reasoning, 13th International Joint Conference on Artificial Intelligence, Chamberg, France, 1993, pp. 61-66.
  • Guesgen, H. W. (1989). Spatial reasoning based on Allen's temporal logic. International Computer Science Institute technical report. TR-89-049. 1947 Center St, Suite 600, Berkeley, CA.
• Working Memory
  • Salthouse, T. A. (1990). Working memory as a processing resource in cognitive aging. Developmental Review, 10, 101-124.