Hostname: page-component-77c78cf97d-sp94z Total loading time: 0 Render date: 2026-04-25T01:06:49.674Z Has data issue: false hasContentIssue false
  • English
  • Français

Transcending inductive category formation in learning

Published online by Cambridge University Press:  04 February 2010

Roger C. Schank ,
Gregg C. Collins  and
Lawrence E. Hunter
Roger C. Schank
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
Gregg C. Collins
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
Lawrence E. Hunter
Affiliation:
Computer Science Department, Yale University, New Haven, Conn. 06520
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial roles of other cognitive processes (such as explanation and credit assignment) are ignored or oversimplified. Learning necessarily involves pragmatic considerations that can only be handled by complex cognitive processes.

We provide an alternative framework for learning according to which category definitions must be based on category function. The learning system invokes other cognitive processes to accomplish difficult tasks, makes inferences, analyses and decides among potential features, and specifies how and when categories are to be generated and modified. We also examine the methodological underpinnings of the two approaches and compare their motivations.

Keywords

categorizationcausal inferencecomputer modelsconcept formationexpectationsfeaturesgoalsinductioni learningmemoryplanningproblem-solvingrepresentation

Information

Type
Target article
Information
Behavioral and Brain Sciences , Volume 9 , Issue 4 , December 1986 , pp. 639 - 651
Copyright
Copyright © Cambridge University Press 1986

References

Abelson, R. (1981) Constraint, construal and cognitive science. In: The proceedings of Third Annual Conference of the Cognitive Science Society,Berkeley CA [rRCS]Google Scholar
Abbot, V., Black, J. & Smith, E. E. (1985) The representation of scripts in memory. Journal of Memory and Language 24:179–99. [EES]CrossRefGoogle Scholar
Anderson, J. R. (1983) The architecture of cognition. Harvard University Press. [JRA]Google Scholar
Anderson, J. R., Farrell, R. & Sauers, R. (1984) Learning to program in LISP. Cognitive Science 8:87–129. [JRA]Google Scholar
Anderson, J. R. & Kline, P. J. (1979) A learning system and its psychological implications. Proceedings of the Sixth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [PL]Google Scholar
Anderson, J. R., Kline, P. J. & Beasley, C. M. (1979) A general learning theory and its application to schema abstraction. In: The psychology of learning and motivation, vol. 13, ed. Bower, G. H.. Academic Press. [arRCS, JRA]Google Scholar
Anderson, J. R. & Thompson, R. (1986) Use of analogy in a production system architecture. Presented at the University of Illinois Workshop on Similarity and Analogy. [JRA]Google Scholar
Angluin, D. (1981) A note on the number of queries needed to identify regular languages. Information and Control 51:76–87. [KTK]CrossRefGoogle Scholar
Angluin, D. & Smith, C. H. (1982) A survey of inductive inference: Theory and methods. Technical Report 250. Yale University. [KTK]Google Scholar
Armstrong, S., Gleitman, L. R. & Gleitman, H. (1981) What some concepts might not be. Cognition 13:263–308. [BL, GLM]CrossRefGoogle Scholar
Barsalou, L. W. (1982) Context-independent and context-dependent information in concepts. Memory and Cognition 10:82–93. [aRCS, LWB, JEC]CrossRefGoogle ScholarPubMed
Barsalou, L. W. (1983) Ad hoc categories. Memory and Cognition 11:211–27. [aRCS, LWB]CrossRefGoogle ScholarPubMed
Barsalou, L. W. (1985) Ideals, central tendency, and frequency of instantiation as determinants of graded structure in categories. Journal of Experimental Psychology: Learning, Memory, and Cognition 11:629–54. [rRCS, LWB, JEC, GLM, EES]Google ScholarPubMed
Barsalou, L. W. (1986) Intra-concept similarity and its implications for inter-concept similarity. To be presented at a conference on Similarity and Analogy at the University of Illinois in June (1986). Chapter in an edited volume to follow. [LWB]Google Scholar
Barsalou, L. W. (in press) The instability of graded structure: Implications for the nature of concepts. In: Concepts and conceptual development: Ecological and intellectual factors in categorization, ed. Neisser, U.. Cambridge University Press. [LWB]Google Scholar
Barsalou, L. W. & Medin, D. L. (1986) Concepts: Static definitions or context-dependent representations? Cahiers de Psychologie Cognitive. 6:187–202. [LWB]Google Scholar
Barsalou, L. W. & Ross, B. H. (1986) The roles of automatic and strategic processing in sensitivity to superordinate and property frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition 12:116–34. [LWB]Google Scholar
Barsalou, L. W. & Sewell, D. R. (1984) Constructing representations of categories from different points of view. Emory Cognition Project Report no. 2. Emory University. [LWB]Google Scholar
Barsalou, L. W., Usher, J. A. & Sewell, D. R. (1985) Schema-based planning of events. Paper¯ presented at the Meeting of the Psychonomic Society, Boston. [LWB]Google Scholar
Bickhard, M. H. & Richie, D. M. (1983) On the nature of representation: A case study of James J. Gibson's theory of perception. Praeger. [RLC]Google Scholar
Birnbaum, L. (1985) A short note on opportunistic planning and memory for arguments. Proceedings of the Ninth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Bower, G. H., Black, J. B. & Turner, T. J. (1979) Scripts in memory for text. Cognitive Psychology 11:177–220. [aRCS]CrossRefGoogle Scholar
Brooks, L. R. (1978) Nonanalytic concept formation and memory for instances. In: Cognitive and categorization, ed. Rosch, E. & Lloyd, B. B.. Erlbaum. [aRCS, GLM]Google Scholar
Bruner, J. S., Goodnow, J. J. & Austin, G. A. (1956) A study of thinking. Wiley. [RLC]Google Scholar
Bruner, J. S., Goodnow, J. J. & Austin, G. A. (1956) A study of thinking. Science Editions. [arRCS]Google Scholar
Buchanan, B. G. & Mitchell, T. M. (1978) Model-based learning of production rules In: Pattern-directed inference systems, ed. Waterman, D. A. & Hayes-Roth, F.. Academic Press. [rRCS, TGD]Google Scholar
Bullock, M. (1985) Causal reasoning and developmental change in the preschool years. Human Development 28:169–91. [RLC]CrossRefGoogle Scholar
Bullock, M., Gelman, R. & Baillargeon, R. (1982) The development of causal reasoning. In: The developmental psychology of time, ed. Friedman, W. J.. Academic Press. [RLC]Google Scholar
Callanan, M. A. & Markman, E. M. (1982) Principles of organization in young children's natural language hierarchies. Child Development 53:1093–1101. [REP]CrossRefGoogle Scholar
Campbell, R. L. & Bickhard, M. H. (1986a) Knowing levels and developmental stages. Karger. [RLC]Google Scholar
Campbell, R. L. & Bickhard, M. H. (1986b) Knowing levels and the development of natural kind categories. In: Interactivism and developmental psychology. Presented at Jean Piaget Society Symposium, Philadelphia. [RLC]Google Scholar
Campbell, R. L. & Bickhard, M. H. (in press) A deconstruction of Fodor's anticonstructivism. Human Development. [RLC]Google Scholar
Carbonell, J. G. (1985) Derivational analogy: A theory of reconstructive problem solving and expertise acquisition (Technical Report CMU-CS-85–115). Carnegie-Mellon University, Computer Science Department. [JRA]Google Scholar
Carbonell, J. G. (1986) Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In: Machine learning: An artificial intelligence approach, vol. 2, ed. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M.. Morgan Kaufmann. [DK, mL]Google Scholar
Carey, S. (1985) Conceptual change in childhood. MIT Press. [GLM]Google Scholar
Chi, M. T. H., Feltovich, P. J. & Glaser, R. (1981) Categorization and representation of physics knowledge by experts and novices. Cognitive Science 5:121–52. [GLM]CrossRefGoogle Scholar
Chomsky, N. (1957) Syntactic structures. Mouton. [REP]CrossRefGoogle Scholar
Chomsky, N. (1965) Aspects of the theory of syntax. MIT Press. [rRCS, EW]Google Scholar
Chomsky, N. (1975) Reflections on language. Pantheon. [EW]Google Scholar
Clark, E. V. (1983) Meanings and concepts. In: Manual of child psychology: Cognitive development, vol. 3, ed. Flavell, J. H. & Markman, E. M.. Wiley. [GLM]Google Scholar
Cohen, L. B. & Younger, B. A. (1983) Perceptual categorization in the infant. In: New trends in conceptual representation: Challenges to Piaget's theory? ed. Scholnick, E. K.. Erlbaum. [RLC]Google Scholar
Cohen, N. J. & Corkin, S. (1982) Learning to solve the Tower of Hanoi puzzle in amnesia. Presented at the Psychonomic Society, Minneapolis. [RLC]Google Scholar
Cullingford, R. (1978) “Script application: Computer understanding of newspaper stories.” Ph.D. thesis, Yale University. Research Report no. 116. [aRCS]Google Scholar
Dehn, N. (1981) Story generation after tale-spin. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
DeJong, G. (1981) Generalizations based on explanations. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [rRCS, DK]Google Scholar
DeJong, G. (1985) A brief overview of explanatory schema acquisition. Proceedings of the Third International Machine Learning Workshop, Skytop, Pa., 06. [rRCS]CrossRefGoogle Scholar
DeJong, G. F. (1986) An approach to learning from observation. In: Machine learning: An artificial intelligence approach, vol. 2, ed. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M.. Morgan-Kaufmann. [mL]Google Scholar
DeJong, G. & Mooney, R. (in press) Explanation based learning: An alternative view. Machine Learning. [PL]Google Scholar
Dennett, D. C. (1978) Brainstorms. MIT Press. [rRCS]Google Scholar
Dietterich, T. G. (1980) Applying general induction methods to the card game Elusis. Proceedings of the National Conference on Artificial Intelligence. [aRCS]Google Scholar
Dietterich, T. G. & Michalski, R. S. (1981) Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methodologies. Artificial Intelligence 16:257–94. [aRCS]CrossRefGoogle Scholar
Dietterich, T. & Michalski, R. (1983) A comparative review of selected methods of learning from examples. In: Machine learning, ed. Michalski, R., Carbonell, J. & Mitchell, T.. Morgan-Kaufmann. [YW]Google Scholar
Dunlaing, C. O. & Yap, C. (1982) The Voronoi diagram method of motion-planning I. Courant Institute of Mathematical Sciences. [DK]Google Scholar
Ellman, T. (1985) Generalizing logic circuit designs by analyzing proofs of correctness. Proceedings of the Ninth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [ML]Google Scholar
Estes, W. K. (1986) Array models for category learning. Unpublished manuscript, Harvard University. [JEC]CrossRefGoogle Scholar
Estes, W. K. (in press) Array models for category learning. Cognitive Psychology. [EES]Google Scholar
Estes, W. K., Burke, C. J., Atkinson, R. C. & Frankmann, J. P. (1957) Probabilistic discrimination learning. Journal of Experimental Psychology 54:233–39. [CPS]CrossRefGoogle ScholarPubMed
Feldman, J. (1972) Some decidability results in grammatical inference. Information and Control 20:244–62. [KTK]CrossRefGoogle Scholar
Fodor, J. A. (1975) The language of thought Thomas Y. Crowell. [PT]Google Scholar
Fodor, J. A. (1981) Representations. MIT Press. [rRCS, RLC, BL]Google Scholar
Fodor, J. A. (1983) The modularity of mind. MIT Press. [aRCS, GLM, EES]CrossRefGoogle Scholar
Fodor, J. A., Fodor, J. D. & Garrett, M. (1975) The psychological unreality of semantic representations. Linguistic Inquiry 6:515–31. [DS]Google Scholar
Fried, L. & Holyoak, K. (1984) Induction of category distributions: A framework for classification learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:234–57. [aRCS, JEC]Google ScholarPubMed
Garey, M. R. and Johnson, D. S. (1979) Computers and intractability: A guide to the theory of NP-Completeness, W. H. Freeman. [DK]Google Scholar
Gelman, S. A., Collman, P. & Maccoby, E. E. (1986) Inferring properties from categories versus inferring categories from properties: The case of gender. Child Development 57:396–404. [RLC]CrossRefGoogle Scholar
Gentner, D. (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7/2:150–70. [DK]Google Scholar
Gleitman, L. & Wanner, E. (1982) Language acquisition: The state of the state of the art. In: Language acquisition: The state of the art, ed. Wanner, E. & Gleitman, L.. Cambridge University Press. [EW]Google Scholar
Gold, E. M. (1967) Language identification in the limit. Information and Control 10:447–74. [arRCS, KTK]CrossRefGoogle Scholar
Gold, E. M. (1978) Complexity of automaton identification from given data. Information and Control 10:447–74. [KTK]CrossRefGoogle Scholar
Goodman, N. (1979) Fact, fiction, and forecast. Hachett. [BL]Google Scholar
Hanson, N. R. (1958). Patterns of discovery. Cambridge University Press. [CPS]Google Scholar
Hanson, S. & Bauer, M. (1986) Machine learning clustering and polymorphy. In: Approaches to uncertainty in AI, ed. Kanal, L. & Lemmer, J.. North Holland. [SJH]Google Scholar
Harman, G. (1973) Thought. Princeton University Press. [JTT]Google Scholar
Harnad, S. (Ed.) (1987) Categorical perception. Cambridge University Press. [REP]Google Scholar
Harré, R. (1970) The principles of scientific thinking. University of Chicago Press. [RLC]CrossRefGoogle Scholar
Harré, R. & Madden, E. H. (1975) Causal powers. Rowman and Littlefield. [RLC]Google Scholar
Hasher, L. & Zacks, R. T. (1979) Automatic and effortful processes in memory. Journal of Experimental Psychology: General 108:356–88. [RLC]CrossRefGoogle Scholar
Hayes-Roth, F. & McDermott, J. (1977) Knowledge acquisition from structure descriptions. Proceedings of the Fifth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Holland, J., Holyoak, K., Nisbett, R. & Thagard, P. (1986) Induction: Processes of inference, learning, and discovery. MIT Press. [rRCS, PT]Google Scholar
Horning, J. J. (1969) A study of grammatical inference. Ph.D. thesis, Stanford University, Computer Science Dept. [KTK]Google Scholar
Hunt, E. B. (1962) Concept learning: An information processing problem. Wiley. [aRCS]CrossRefGoogle Scholar
Hunt, E. B., Marin, J. & Stone, P. (1966) Experiments in induction. Academic Press. [CG]Google Scholar
Jacoby, L. L. & Brooks, L. R. (1984) Nonanalytic cognition: Memory, perception, and concept learning. In: The psychology of learning and motivation: Advances in research and theory, vol. 18, ed. Bower, G. H.. Academic Press. [LWB]Google Scholar
Jusczyk, P. W. (1981) Infant speech perception: A critical appraisal. In: Perspectives in the study of speech, ed. Eimas, P. D. & Miller, J. L.. Erlbaum. [REP]Google Scholar
Kahneman, D. & Miller, D. T. (1986) Norm theory: Comparing reality to its alternatives. Psychological Review 93:136–53. [LWB]CrossRefGoogle Scholar
Kass, A. (1986) Modifying explanations to understand stories. Proceedings of the Eighth Annual Conference of the Cognitive Science Society.Cognitive Science Society. [rRCS]Google Scholar
Katz, J. J. (1981) Language and other abstract objects. Rowman and Littlefield. [DS]Google Scholar
Keil, F. C. (1981) Constraints on knowledge and cognitive development. Psychological Review 88:197–227. [aRCS]CrossRefGoogle Scholar
Keil, F. C. (1986) The acquisition of natural kind and artifact terms. In: Language learning and concept acquisition: foundation issues, ed. Demopoulos, W. & Marras, A.. Ablex. [RLC]Google Scholar
Kyburg, H. E. Jr. (1983) Rational belief. Brain and Behavioral Sciences 6:231–75. [HEK]CrossRefGoogle Scholar
LaBerge, D. (1976) Perceptual learning and attention. In: Handbook of learning and cognitive processes, vol. 4, ed. Estes, W. K.. Erlbaum. [aRCS]Google Scholar
Landau, B. & Gleitman, L. (1985) Language and experience. Harvard University Press. [EW]Google Scholar
Langley, P. (1982) Language acquisition through error recovery. Cognition and Brain Theory 5:211–55. [PL]Google Scholar
Larkin, J. H. (1985) Understanding problem representations, and skill in physics. In: Thinking and learning skills, vol. 2: Research and open questions, ed. Chipman, S. F., Segal, J. W. & Glaser, R.. Erlbaum. [GLM]Google Scholar
Leake, D. & Owens, C. (1986) Organizing memory for explanation. Proceedings of the Eighth Annual Conference of the Cognitive Science Society.Cognitive Science Society. [rRCS]Google Scholar
Lebowitz, M. (1980) Generalization and memory in an integrated understanding system. (Tech. Rep. No. 186). Yale University Department of Computer Science. [ML]Google Scholar
Lebowitz, M. (1982) Correcting erroneous generalizations. Cognition and Brain Theory 5:367–81. [ML, PL]Google Scholar
Lebowitz, M. (1983) Generalization from natural language text. Cognitive Science 7:1–40. [ML]Google Scholar
Lebowitz, M. (1986a) Integrated learning: Controlling explanation. Cognitive Science 10:219–40. [ML]CrossRefGoogle Scholar
Lebowitz, M. (1986b) Not the path to perdition: The utility of similarity-based learning. Columbia University Department of Computer Science. [ML]Google Scholar
Lebowitz, M. (1986c) Concept learning in a rich input domain: Generalization-based Memory. In: Machine learning: An artificial intelligence approach, vol. 2. ed. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M.. Morgan Kaufmann. [ML]Google Scholar
Lindsay, R., Buchanan, B., Feigenbaum, E. & Lederberg, J. (1980) Applications of artificial intelligence for organic chemistry. McGraw Hill. [CG]Google Scholar
Lycan, W. G.Conservatism and the data base. Manuscript. [JTT]Google Scholar
McClelland, J. L. & Rumelhart, D. E., eds. (1985) Distributed memory and the representation of general and specific information. Journal of Experimental Psychology 114:159–88. [rRCS, JRA]CrossRefGoogle ScholarPubMed
McDermott, D. V. (1981) Artificial intelligence meets natural stupidity. In: Pattern directed inference systems, ed. Haugeland, J.. MIT Press. [rRCS]Google Scholar
McFadden, R. (1986) Two bottles of poisoned tylenol were shipped by the same distributor. New fork Times 135:1. [ML]Google Scholar
McGuire, R., Birnbaum, L. & Flowers, M. (1981) Opportunistic processing in arguments. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Medin, D. L. (1982) Structural principles of categorization. In: Perception, development, and cognition: An interactional approach, ed. Shepp, B. & Tighe, T.. Erlbaum. [JEC]Google Scholar
Medin, D. L. & Schaeffer, M. M. (1978) A context theory of classification learning. Psychological Review 85:207–38. [rRCS, JRA, GLm]CrossRefGoogle Scholar
Menzel, E. M. (1978) Cognitive mapping in chimpanzees. In: Cognitive processes in animal behavior, ed. Hulse, S. H., Fowler, H. & Honig, W. K.. Erlbaum. [CPS]Google Scholar
Michalski, R. S. (1980) Pattern recognition as rule-guided inductive inference. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:349–61. [aRCS]CrossRefGoogle Scholar
Michalski, R. S. (1983) Theories and methodology of inductive learning. Artificial Intelligence 20/2:111–61. [arRCS]CrossRefGoogle Scholar
Michalski, R. S., Carbonell, J., Anderson, J. & Mitchell, T. (1983) Machine learning: An artificial intelligence approach. Tioga Press. [CG]CrossRefGoogle Scholar
Michalski, R. S. & Larson, J. B. (1978) Selection of most representative training examples and incremental addition of VL1 hypothesis: The underlying methodology and the description of programs ESEL and AQ11. Technical Report no. 867, Computer Science Department, University of Illinois. [aRCS]Google Scholar
Michalski, R. S. & Stepp, R. (1983) Learning from observation: Conceptual clustering. In: Machine learning: An artificial intelligence approach, ed. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M.. Tioga Press. [rRCS, PL]CrossRefGoogle Scholar
Millikan, R. G. (1984) Language, thought, and other biological categories, Chapters 16 & 17. MIT Press. [RGM]CrossRefGoogle Scholar
Minsky, M. (1961) Steps toward artificial intelligence. Proceedings of the Institute of Radio Engineers 49:8–30. Reprinted in E. Feigenbaum & J. Feldman (1963) Computers and thought. McGraw-Hill. [arRCS]Google Scholar
Minsky, M. (June (1979)) K-lines: A theory of memory. MIT AI memo, no. 516. [DK] (forthcoming) Society of mind. Simon and Schuster. [DK]Google Scholar
Mitchell, T. M. (1978) “Version spaces: An approach to concept learning.” Ph.D. Thesis. Stanford University. [aRCS]Google Scholar
Mitchell, T. M. (1979) An analysis of generalization as a search problem. Proceedings of the Sixth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Mitchell, T. M. (1983) Learning and problem solving. Proceedings of the Eighth International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS, ML, YW]Google Scholar
Mitchell, T. M., Keller, R. M. & Kedar-Cabelli, S. T. (1986) Explanation-based generalization: A unifying view. Machine Learning 1:47–80. [rRCS, JRA, DK, PL]CrossRefGoogle Scholar
Mitchell, T. M., Utgoff, P. E., Nudel, B. & Banerji, R. B. (1981) Learning problem-solving heuristics through practice. Proceedings of the Seventh International Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Mostow, J. (1983) Operationalizing advice: A problem-solving model. Proceedings of the (1983) International Machine Learning Workshop, Champaign-Urbana, Illinois. [ML]Google Scholar
Murphy, G. L. & Medin, D. L. (1985) The role of theories in conceptual coherence. Psychological Review 92:289–316. [arRCS, LWB, JEC, GLM, CPS]CrossRefGoogle ScholarPubMed
Murphy, G. L. & Wright, J. C. (1984) Changes in conceptual structure with expertise: Differences between real-world experts and novices. Journal of Experimental Psychology: Learning, Memory, and Cognition 10:144–55. [GLM]Google Scholar
Nelson, D. G. K. (1984) The effect of intention on what concepts are acquired. Journal of Verbal Learning and Verbal Behavior 23:734–59. [EES]CrossRefGoogle Scholar
Newell, A. (1969) Heuristic programming: Ill-structured problems. In: Progress in operations research, ed. Amofsky, J.. Wiley. [TGD]Google Scholar
Nicod, J. (1970) Geometry and induction: Containing geometry in the sensible world and the logical problem of induction, trans. Bell, J. & Wood, M.. University of California Press. [HEK]Google Scholar
Osherson, D. N. & Smith, E. E. (1981) On the adequacy of prototype theory as a theory of concepts. Cognition 9:35–58. [GLM]CrossRefGoogle ScholarPubMed
Pao, T. W. & Carr, J. W. III (1978) A solution of the syntactical induction-inference problem for regular languages. Computer Languages 3:53–64. [KTK]CrossRefGoogle Scholar
Pazzani, M. J. (1985) Explanation and generalization based memory. (Technical Report no. UCLA-AI-85–13). UCLA Artificial Intelligence Laboratory. [ML]Google Scholar
Piaget, J. (1977) Essai sur la nécessité. Archives de Psychologie 45:235–51. (English trans., in press, Human Development). [RLC]Google Scholar
Pitt, L. (1984) A characterization of probabilistic inference. Ph. D. thesis, Yale University, Computer Science Department. [KTK]Google Scholar
Pomerantz, J. R., Sager, L. C. & Stoever, R. G. (1977) Perception of wholes and their component parts: Some configurai superiority effects. Journal of Experimental Psychology: Human Perception and Performance 3:422–35. [REP]Google Scholar
Pople, H. E. (1975) On the mechanization of abductive logic. Proceedings of the Fourth international Joint Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS]Google Scholar
Popper, K. (1965) Conjectures and refutations. Harper. [RLC]Google Scholar
Posner, M. I. (1980) Orienting of attention. Quarterly Journal of Experimental Psychology 32:3–25. [DK]CrossRefGoogle ScholarPubMed
Posner, M. I. & Kelle, S. W. (1968) On the genesis of abstract ideas. Journal of Experimental Psychology 77:353–63. [RLC, GLM]CrossRefGoogle ScholarPubMed
Putnam, H. (1975) The meaning of “meaning.” In: Mind language and reality: Philosophical papers, vol. 2. Cambridge University Press. [RLC]CrossRefGoogle Scholar
Quine, W. V. (1960) Word and object. MIT Press. [rRCS, BL]Google Scholar
Quine, W. V. & Ullian, J. S. (1973) The web of belief. Random House. [JTT]Google Scholar
Reed, S. K. (1972) Pattern recognition and categorization. Cognitive Psychology 3:382–407. [GLM]CrossRefGoogle Scholar
Repp, B. H. (1983) Trading relations among acoustic cues in speech perception are largely a result of phonetic categorization. Speech Communication 2:341–62. [REP]CrossRefGoogle Scholar
Rosch, E. & Mervis, C. B. (1975) Family resemblances: Studies in the internal structure of categories. Cognitive Psychology 7:573–605. [aRCS, JEC, GLM]CrossRefGoogle Scholar
Roth, E. M. & Shoben, E. J. (1984). The effect of context on the structure of categories. Cognitive Psychology 15:346–78. [LWB]CrossRefGoogle Scholar
Schank, R. C. (1982) Dynamic memory: A theory of learning in computers and people. Cambridge University Press [arRCS, CG, KTK, ML]Google Scholar
Schank, R. C. (1984) Explanation: A first pass. Technical Report no. 330. Yale University. [arRCS]Google Scholar
Schank, R. C. (in press) Explanation patterns: Understanding mechanically and creatively. Erlbaum. [rRCS]CrossRefGoogle Scholar
Schank, R. C. & Abelson, R. (1977) Scripts, plans, goals and understanding. Erlbaum. [aRCS]Google Scholar
Schank, R. C. & Riesbeck, C. (1985) Explanation: A second pass. Technical Report no. 384. Department of Computer Science, Yale University. [arRCS]Google Scholar
Schneider, W. & Sniffrin, R. M. (1977) Controlled and automatic human information processing: 1. Detecting, search, and attention. Psychological Review 84:1–66. [EES]CrossRefGoogle Scholar
Shapiro, E. (1980) Inductive inference of theories from facts. Ph.D. thesis, Yale University, Computer Science Department. [KTK]Google Scholar
Shimp, C. P. (1973) Probabilistic discrimination learning in the pigeon. Journal of Experimental Psychology 97:292–304. [CPS]CrossRefGoogle Scholar
Shimp, C. P. (1984a) Timing, learning, and forgetting. In: Timing and perception, ed. Gibbon, J. & Allan, L.. New York Academy of Sciences. [CPS]Google Scholar
Shimp, C. P. (1984b) Relations between memory and operant behavior, according to an associative learner (AL). Canadian Journal of Psychology 38:269–84. [CPS]CrossRefGoogle Scholar
Shimp, C. P. (1984c) Cognition, behavior, and the experimental analysis of behavior. Journal of the Experimental Analysis of Behavior 42:407–20. [CPS]CrossRefGoogle ScholarPubMed
Shimp, C. P. (1984d) The question: Not shall it be, but which shall it be? Commentary on “Are theories of learning necessary?” by B. F. Skinner. Behavioral and Brain Sciences 7:536–37. [CPS]CrossRefGoogle Scholar
Shultz, T. R. (1982) Rules of causal attribution. Monographs of the Society for Research in Child Development 47:(1, ser. no. 194). [RLC]CrossRefGoogle Scholar
Silver, B. (1986) Precondition analysis: Learning control information. In: Machine learning: An artificial intelligence approach, vol. 2, ed. Michalski, R. S., Carbonell, J. G. & Mitchell, T. M.. Morgan-Kaufmann. [ML]Google Scholar
Simon, C. & Fourcin, A. J. (1978) Cross language study of speech pattern learning. Journal of the Acoustical Society of America 63:925–35. [REP]CrossRefGoogle Scholar
Skinner, B. F. (1984) Theoretical contingencies. Behavioral and Brain Sciences 7:544. [CPS]Google Scholar
Smith, A. H. (1963) The mushroom hunter's field guide. University of Michigan Press. [HER]Google Scholar
Smith, E. E. (1978) Theories of semantic memory. In: Handbook of learning and cognitive processes, vol. 6, ed. Estes, W. K.. Erlbaum. [GLM]Google Scholar
Smith, E. E. & Medin, D. L. (1981) Categories and concepts. Harvard University Press. [aRCS, GLM, EES]CrossRefGoogle Scholar
Soames, S. (1985) Semantics and psychology. In: The philosophy of linguistics, ed. Katz, J. J.. Oxford University Press. [DS]Google Scholar
Sperber, D. & Wilson, D. (1986) Relevance: Communication and cognition. Harvard University Press. [DS]Google Scholar
Spence, K. W. (1942) The basis of solution by chimpanzees of the intermediate size problem. Journal of Experimental Psychology 31:257–71. [CPS]CrossRefGoogle Scholar
Tolman, E. C. & Brunswick, E. (1935) The organism and the causal texture of the environment. Psychological Review 42:43–77. [CPS]CrossRefGoogle Scholar
Treisman, A. & Gelade, G. (1980) A feature-integration theory of attention. Cognitioe Psychology 12:97–136. [DK, REP]CrossRefGoogle ScholarPubMed
Treisman, A. & Patterson, R. (1984) Emergent features, attention, and object perception. Journal of Experimental Psychology: Human Perception and Performance 10:12–31. [REP]Google ScholarPubMed
Tversky, A. (1977) Features of similarity. Psychological Review 84:327–52. [JEC]CrossRefGoogle Scholar
Tversky, A. & Kahneman, D. (1974) Judgement under uncertainty: Heuristics and biases. Science 185:1124–31. [SJH]CrossRefGoogle ScholarPubMed
Ullman, S. (1983) Visual routines. MIT AI memo, no. 723. [DK]Google Scholar
VanLehn, K. (1983) Human procedural skill acquisition: Theory, model, and psychological validation. Proceedings of the national conference on artificial intelligence.Morgan-Kaufmann. [TGD]Google Scholar
Vere, S. A. (1978) Inductive learning of relational productions. In: Pattern directed inference systems, ed. Waterman, D. A. & Hayes-Roth, F.. Academic Press. [aRCS]Google Scholar
Von Wright, G. H. (1960) A treatise on induction and probability. Littlefield, Adams. [HEK]Google Scholar
Wagner, A. R. (1976) Priming in STM: An information-processing mechanism for self-generated or retrieval-generated depression in performance. In: Habituation, ed. Tighe, T. J. & Leaton, R. N.. Erlbaum. [CPS]Google Scholar
Waterman, D. A. (1970) Generalization learning techniques for automating the learning of heuristics. Artificial Intelligence 1:121–70. [aRCS]CrossRefGoogle Scholar
Wexler, K. & Culicover, P. (1980) Formal principles of language acquisition. MIT Press. [KTK]Google Scholar
Winston, P. H. (1970) Learning structural descriptions from examples. AI Laboratory, Massachusetts Institute of Technology, Technical Report no. 231. Reprinted (1975) in: The psychology of computer vision, ed. P. H. Winston. McGraw-Hill. [aRCS, PL]Google Scholar
Winston, P. & Horn, B. (1975) The psychology of computer vision. McGraw-Hill. [CG]Google Scholar
Winston, P. & Horn, B. (1977) Artificial intelligence. Addison-Wesley. [aRCS]Google Scholar
Winston, P. H. (1980) Learning and reasoning by analogy. In: Communications of Association for Computing Machinery vol. 23, no. 12. [DK]CrossRefGoogle Scholar
Winston, P. H., Binford, T. O., Katz, B. & Lowry, M. (1983) Learning physical descriptions from functional definitions, examples, and precedents. Massachusetts Institute of Technology AI memo, no. 679. [DK]Google Scholar
Winston, P. H., Binford, T. O., Katz, B. & Lowry, M. (1983) Learning physical descriptions from function definitions, examples, and precedents. Proceedings of the National Conference on Artificial Intelligence.Morgan-Kaufmann. [aRCS, JRA, ML]Google Scholar
Witkin, A. (1983) Scale-space filtering. Proceedings of the Eighth International Joint Conference for Artificial Intelligence. [DK]Google Scholar
Wittgenstein, L. (1953) Philosophical investigations, 3rd edition, trans. Anscombe, G. E. M.. MacMillan. [aRCS, CPS]Google Scholar