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17 - Computational Models of Skill Acquisition

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

By
Stellan Ohlsson
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
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Summary

Computer models of the acquisition of cognitive skills build on a long and progressive tradition of research. Since 1979, a wide range of psychologically plausible mechanisms for learning during skill practice have been implemented in computational models. This repertoire of mechanisms goes a long way towards answering the questions implied by Fitts’ (1964) division of practice into three phases: How does skill practice get started? How is a partially learned skill improved during practice? How does a skill change as practice is extended beyond mastery? Nine distinct modes of learning are identified. Each can be implemented in several different ways. The majority of models explain the speed-up of task completion that occurs during practice. There are fewer attempts to model the origin, consequences, and ultimate elimination of errors.

Keywords

cognitive architecturecognitive skillcomputational modelinformation typemodes of learningskill practicesymbolic computation
Type
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Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 527 - 566
Publisher: Cambridge University Press
Print publication year: 2023

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References

Ackerman, P. L. (1990). A correlational analysis of skill specificity: learning, abilities, and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 883–901.Google Scholar
Altmann, E. M., & Trafton, J. G. (2002). Memory for goals: an activation-based model. Cognitive Science, 26, 39–83.Google Scholar
Amir, E., & Maynard-Zhang, P. (2004). Logic-based subsumption architecture. Artificial Intelligence, 153, 167–237.Google Scholar
Anderson, J. R. (1976). Language, Memory, and Thought. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.Google Scholar
Anderson, J. R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Anderson, J. R. (1986). Knowledge compilation: the general learning mechanism. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 289–310). Los Altos, CA: Kaufmann.Google Scholar
Anderson, J. R. (1987). Skill acquisition: compilation of weak-method problem solutions. Psychological Review, 94, 192–210.Google Scholar
Anderson, J. (1989). The analogical origins of errors in problem solving. In Klahr, D. & Kotovsky, K. (Eds.), Complex Information Processing: The Impact of Herbert A. Simon. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in the Physical Universe? New York, NY: Oxford University Press.CrossRefGoogle Scholar
Anderson, J. R., Betts, S., Bothell, D., Hope, R., & Lebiere, C. (2019). Learning rapid and precise skills. Psychological Review, 126, 727–760.Google Scholar
Anderson, J. R., Kline, P., & Beasley, C. (1978). A Theory of the Acquisition of Cognitive Skills. New Haven, CT: Yale University Press.Google Scholar
Anderson, J. R., Kline, P. J., & Beasley, C. M., Jr. (1979). A general learning theory and its application to schema abstraction. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory (vol. 13, pp. 277–318). New York, NY: Academic Press.Google Scholar
Anderson, J. R., & Thompson, R. (1989). Use of analogy in a production system architecture. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and Analogical Reasoning (pp. 267–297). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Anzai, Y., & Simon, H. A. (1979). The theory of learning by doing. Psychological Review, 86, 124–140.CrossRefGoogle ScholarPubMed
Bharadwaj, K. K., & Jain, N. K. (1992). Hierarchical censored production rule (HCPRs) system. Data & Knowledge Engineering, 8, 19–34.Google Scholar
Bhatnagar, N., & Mostow, J. (1994). On-line learning from search failure. Machine Learning, 15, 69–117.Google Scholar
Blessing, S. B., & Anderson, J. R. (1996). How people learn to skip steps. Journal of Experimental Psychology: Learning, Memory, & Cognition, 22, 576–598. [Reprinted in Polk & Seifert, 2002, pp. 577–620.]Google Scholar
Brown, J. S., & VanLehn, K. (1980). Repair theory: a generative theory of bugs in procedural skills. Cognitive Science, 4, 379–426.Google Scholar
Buchanan, B. & Mitchell, T. (1978). Model-directed learning of production rules. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 297–312). New York, NY: Academic Press.Google Scholar
Bush, R. R., & Mosteller, F. (1951). A model for stimulus generalization and discrimination. Psychological Review, 58, 413–423.Google Scholar
Carbonell, J. G. (1983). Learning by analogy: formulating and generalizing plans from past experience. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 137–161). Palo Alto, CA: Tioga.Google Scholar
Carbonell, J. G. (1986). Derivational analogy: a theory of reconstructive problem solving and expertise acquisition. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 371–392). Los Altos, CA: Morgan Kauffmann.Google Scholar
Carroll, J. B. (1993). Human Cognitive Abilities. Cambridge: Cambridge University Press.Google Scholar
Choi, D., & Ohlsson, S. (2011). Effects of multiple learning mechanisms in a cognitive architecture. In Carlson, L., Hölscher, C., & Shipley, T. (Eds.), Proceedings of the 33rd Annual Meeting of the Cognitive Science Society (pp. 3003–3008). Austin, TX: Cognitive Science Society Boston.Google Scholar
Christiansen, M. H. (2019). Implicit statistical learning. Topics in Cognitive Science, 11, 468–481.CrossRefGoogle ScholarPubMed
Conway, F., & Siegelman, J. (2005). Dark Hero of the Information Age: In Search of Norbert Wiener the Father of Cybernetics. New York, NY: Basic Books.Google Scholar
Cooper, R. P., Ruh, N., & Mareschal, D. (2014). The goal circuit model: a hierarchical, multi-route model of the acquisition and control of routine sequential action in humans. Cognitive Science, 3, 244–274.Google Scholar
Corrigan-Halpern, A., & Ohlsson, S. (2002). Feedback effects in the acquisition of a hierarchical skill. In Gray, W. D. & Schunn, C. D. (Eds.), Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 226–231). Mahwah, NJ: Erlbaum.Google Scholar
Crevier, D. (1993). AI: The Tumultuous History of the Search for Artificial Intelligence. New York, NY: Basic Books.Google Scholar
Crossman, E. (1959). A theory of the acquisition of speed-skill. Ergonomics, 2, 152–166.Google Scholar
Davis, R., & King, J. (1977) An overview of production systems. In Elcock, E. & Michie, D. (Eds.), Machine Intelligence 8 (pp. 300–332). Chichester: Horwood.Google Scholar
De Jong, G. (Ed.). (2012). Investigating Explanation-Based Learning (vol. 120). London: Springer Science & Business Media.Google Scholar
Doane, S. M., Sohn, Y. W., McNamara, D. S., & Adams, D. (2000). Comprehension-based skill acquisition. Cognitive Science, 24, 1–52.Google Scholar
Donald, M. (1991). Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Douglass, S. A., & Anderson, J. R. (2008). A model of language processing and spatial reasoning using skill acquisition to situate action. In Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 2218–2286).Google Scholar
Ebbinghaus, H. (1964/1885). Memory: A Contribution to Experimental Psychology. New York, NY: Dover.Google Scholar
Elio, R., & Scharf, P. B. (1990). Modeling novice-to-expert shifts in problem-solving strategy and knowledge organization. Cognitive Science, 14, 579–639.Google Scholar
Ericsson, K. A., Charness, N., Feltovich, P. J., & Hoffman, R. R. (2006). The Cambridge Handbook of Expertise and Expert Performance. Cambridge: Cambridge University Press.Google Scholar
Ericsson, K. A., Krampe, R. Th., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363–406.Google Scholar
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41, 1–63.Google Scholar
Fischer, K. W. (1980). A theory of cognitive development: the control and construction of hierarchies of skills. Psychological Review, 87, 477–531.Google Scholar
Fitts, P. (1964). Perceptual-motor skill learning. In Melton, A. (Ed.), Categories of Human Learning (pp. 243–285). New York, NY: Academic Press.CrossRefGoogle Scholar
Forgy, C. L. (1982). Rete: a fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence, 19 , 17–37.Google Scholar
Fu, W.-T., & Gray, W. D. (2004). Resolving the paradox of the active user: stable suboptimal performance in interactive tasks. Cognitive Science, 28, 901–935.Google Scholar
Gagne, R. M. (1970). The Conditions of Learning (2nd ed.). London: Holt, Rinehart & Winston.Google Scholar
Gardner, H. (1985). The Mind’s New Science: A History of the Cognitive Revolution. New York, NY: Basic Books.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7, 155–170.Google Scholar
Giunchiglia, E., Lee, J., Lifschitz, V., McCain, N. & Tuner, H. (2004) Nonmonotonic causal theories. Artificial Intelligence, 153, 49–104.Google Scholar
Graesser, A. C., Millis, K., & Graesser, A. (2011). Discourse and cognition. In T. A. Van Dijk (Ed.), Discourse Studies: A Multidisciplinary Introduction (pp. 126–142). London: SAGE Publications.Google Scholar
Gray, W. D., & Boehm-Davis, D. A. (2000). Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. Journal of Experimental Psychology: Applied, 6, 322–335.Google Scholar
Gray, W. D., Schoelles, M. J., & Sims, C. R. (2005). Adapting to the task environment: explorations in expected value. Cognitive Systems Research, 6, 27–40.CrossRefGoogle Scholar
Grefenstette, J. J. (1988). Credit assignment in rule discovery systems based on genetic algorithms. Machine Learning, 3, 225–245.Google Scholar
Hagert, G., Waern, Y., & Tärnlund, S.-Å. (1982). Open and closed models of understanding in conditional reasoning. Acta Psychologica, 52, 41–59.Google Scholar
Hayes-Roth, F., Klahr, P., & Mostow, D. (1981). Advice taking and knowledge refinement: an iterative view of skill acquisition. In Anderson, J. (Ed.), Cognitive Skills and Their Acquisition (pp. 231–253). Hillsdale, NJ: Erlbaum.Google Scholar
Hilgard, E. R., & Bower, G. H. (1966). Theories of Learning (3rd ed.). New York, NY: Appleton-Century-Crofts.Google Scholar
Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: The Processes of Inference, Learning, and Discovery. Cambridge, MA: MIT Press.Google Scholar
Holyoak, K. J. (1985). The pragmatics of analogical transfer. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation (vol. 19, pp. 59–87). New York, NY: Academic Press.Google Scholar
Holyoak, K. J., & Thagard, P. R. (1989a). A computational model of analogical problem solving. In Vosniadou, S. & Ortony, A. (Eds.), Similarity and Analogical Reasoning (pp. 242–266). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Holyoak, K. J., & Thagard, P. (1989b). Analogical mapping by constraint satisfaction. Cognitive Science, 13, 295–355.Google Scholar
Holyoak, K. J., & Thagard, P. (1994). Mental Leaps: Analogy in Creative Thought. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Huffman, S. B., & Laird, J. E. (1995). Flexibly instructable agents. Journal of Artificial Intelligence Research, 3, 271–324.Google Scholar
Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: a theory of analogical access and mapping. Psychological Review, 104, 427–466.CrossRefGoogle Scholar
Hummel, J. E., & Holyoak, K. J. (2003). A symbolic-connectionist theory of relational inference and generalization. Psychological Review, 110, 220–264.Google Scholar
Jain, N. K., & Bharadwaj, K. K. (1998). Some learning techniques in hierarchical censored production rules (HCPRs) system. International Journal of Intelligent Systems, 13, 319–344.Google Scholar
James, W. (1890). Principles of Psychology (vols. 1 and 2). London: Macmillan.Google Scholar
Jones, G., Ritter, F. E., & Wood, D. J. (2000). Using a cognitive architecture to examine what develops. Psychological Science, 11(2), 93–100.CrossRefGoogle ScholarPubMed
Jones, R. M., & Langley, P. A. (2005). A constrained architecture for learning and problem solving. Computational Intelligence, 21, 480–502.Google Scholar
Jones, R. M., & VanLehn, K. (1994). Acquisition of children’s addition strategies: a model of impasse-free, knowledge-level learning. Machine Learning, 16, 11–36. [Reprinted in Polk & Seifert, 2002, pp. 623–646.]Google Scholar
Keane, M. T., Ledgeway, T., & Duff, S. (1994). Constraints on analogical mapping: a comparison of three models. Cognitive Science, 18, 338–387.Google Scholar
Kieras, D., & Bovair, S. (1986). The acquisition of procedures from text: a production-system analysis of transfer of training. Journal of Memory and Language, 25, 507–524.Google Scholar
Kim, J. W., Ritter, F. E., & Koubek, R. .J. (2013). An integrated theory for improved skill acquisition retention in the three stages of learning. Theoretical Issues in Ergonomic Science, 14(1), 32–37.Google Scholar
Kintsch, W. (1998). Comprehension: A Paradigm for Cognition. Cambridge: Cambridge University Press.Google Scholar
Koedinger, K. R., & Anderson, J. R. (1990). Abstract planning and perceptual chunks: elements of expertise in geometry. Cognitive Science, 14, 511–550.Google Scholar
Kokinov, B. N., & Petrov, A. A. (2001). Integrating memory and reasoning in analogy-making: the AMBR model. In Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.), The Analogical Mind: Perspectives from Cognitive Science (pp. 59–124). Cambridge, MA: MIT Press.Google Scholar
Laird, J. E. (2012). The Soar Cognitive Architecture. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Lane, N. (1987). Skill Acquisition Rates and Patterns: Issues and Training Implications. New York, NY: Springer-Verlag.Google Scholar
Langley, P. (1983). Learning search strategies through discrimination. International Journal of Man-Machine Studies, 18, 513–541.Google Scholar
Langley, P. (1985). Learning to search: from weak methods to domain-specific heuristics. Cognitive Science, 9, 217–260.Google Scholar
Langley, P. (1987). A general theory of discrimination learning. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 99–161). Cambridge, MA: MIT Press.Google Scholar
Langley, P., & Choi, D. (2006). Learning recursive control programs from problem solving. Journal of Machine Learning Research, 7, 493–518.Google Scholar
Larkin, J. H. (1981). Enriching formal knowledge: a model for learning to solve textbook physics problems. In Anderson, J. R. (Ed.), Cognitive Skills and Their Acquisition (pp. 311–334). Hillsdale, NJ: Erlbaum.Google Scholar
Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Models of competence in solving physics problems. Cognitive Science, 4, 317–345.Google Scholar
Lenat, D. B. (1983). Toward a theory of heuristics. In Groner, R., Groner, M., & Bischof, W. F. (Eds.), Methods of Heuristics (pp. 351–404). Hillsdale, NJ: Erlbaum.Google Scholar
Lewis, C. (1987). Composition of productions. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 329–358). Cambridge, MA: MIT Press.Google Scholar
Lewis, C. (1988). Why and how to learn why: analysis-based generalization of procedures. Cognitive Science, 12, 211–356.Google Scholar
Lifschitz, V. (Ed.). (1990). Formalizing Common Sense: Papers by John McCarthy. Norwoord, NJ: Ablex.Google Scholar
Logan, G. D. (1998). Toward an instance theory of automatization. Psychological Review, 95, 492–527.Google Scholar
Luchins, A. S., & Luchins, E. H. (1959). Rigidity of Behavior. Eugene, OR: University of Oregon Press.Google Scholar
McCarthy, J. (1959). Programs with common sense. Proceedings of the Teddington Conference on the Mechanization of Thought Processes (pp. 75–91). London: Her Majesty’s Stationery Office. [Reprinted as section 7.1 of J. McCarthy, “Programs with common sense,” in Minsky (Ed.), 1968.]Google Scholar
McCarthy, J. (1963). Situations, Actions and Causal Laws. Stanford Artificial Intelligence Project Memo No. 2. Stanford, CA: Stanford University. [Reprinted as section 7.2 of J. McCarthy (Ed.), “Programs with common sense,” in Minsky (Ed.), 1968.]Google Scholar
McDermott, J., & Forgy, C. (1978). Production system conflict resolution strategies. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 177–199). New York, NY: Academic Press.Google Scholar
Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the Structure of Behavior. New York, NY: Holt, Rinehart & Winston.Google Scholar
Minsky, M. (Ed.). (1968). Semantic Information Processing. Cambridge, MA: MIT Press.Google Scholar
Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, 60, 264–272.Google Scholar
Mostow, D. J. (1983). Machine transformation of advice into a heuristic search procedure. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 367–404). Palo Alto, CA: Tioga.Google Scholar
Nason, S., & Laird, J. E. (2005). Soar-RL: integrating reinforcement learning with Soar. Cognitive Systems Research, 6, 51–59.Google Scholar
Neches, R. (1987). Learning through incremental refinement of procedures. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 163–219). Cambridge, MA: MIT Press.Google Scholar
Neches, R., Langley, P., & Klahr, D. (1987). Learning, development, and production systems. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 1–53). Cambridge, MA: MIT Press.Google Scholar
Neimark, E. D., & Estes, W. K. (Eds.). (1967). Stimulus Sampling Theory. San Francisco, CA: Holden-Day.Google Scholar
Nerb, J., Ritter, F. E., & Krems, J. F. (1999). Knowledge level learning and the power law: a Soar model of skill acquisition in scheduling. Kognitionswissenschaft, 8, 20–29.Google Scholar
Neves, D. M., & Anderson, J. R. (1981). Knowledge compilation: mechanisms for the automatization of cognitive skills. In Anderson, J. R (Ed.), Cognitive Skills and Their Acquisition (pp. 57–84). Hillsdale, NJ: Erlbaum.Google Scholar
Newell, A. (1972). A theoretical exploration of mechanisms for coding the stimulus. In Melton, A. W. & Martin, E. (Eds.), Coding Processes in Human Memory (pp. 373–434). New York, NY: Wiley.Google Scholar
Newell, A. (1973). Production systems: models of control structures. In Chase, W. G. (Ed.), Visual Information Processing (pp. 463–526). New York, NY: Academic Press.Google Scholar
Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press.Google Scholar
Newell, A., & Rosenbloom, P. (1981). Mechanisms of skill acquisition and the law of practice. In Anderson, J. (Ed.), Cognitive Skills and Their Acquisition (pp. 1–55). Hillsdale, NJ: Erlbaum.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65, 151–166.Google Scholar
Newell, A., & Simon, H. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Ohlsson, S. (1987a). Transfer of training in procedural learning: a matter of conjectures and refutations? In Bolc, L. (Ed.), Computational Models of Learning (pp. 55–88). Berlin: Springer-Verlag.Google Scholar
Ohlsson, S. (1987b). Truth versus appropriateness: relating declarative to procedural knowledge. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 287–327). Cambridge, MA: MIT Press.Google Scholar
Ohlsson, S. (1992). Artificial instruction: a method for relating learning theory to instructional design. In Winne, P. & Jones, M. (Eds.), Foundations and Frontiers in Instructional Computing Systems. New York, NY: Springer-Verlag.Google Scholar
Ohlsson, S. (1993). The interaction between knowledge and practice in the acquisition of cognitive skills. In Chipman, S. & Meyrowitz, A. L. (Eds.), Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (pp. 147–208). Boston, MA: Kluwer.Google Scholar
Ohlsson, S. (1996). Learning from performance errors. Psychological Review, 103, 241–262.Google Scholar
Ohlsson, S. (2006). Order effects in constraint-based skill acquisition. In Ritter, F. E., Nerb, J., O’Shea, T., & Lehtinen, E. (Eds.), In Order to Learn: How Ordering Effects in Machine Learning Illuminates Human Learning and Vice Versa (pp. 151–165). New York, NY: Oxford University Press.Google Scholar
Ohlsson, S. (2011). Deep Learning: How The Mind Overrides Experience. Cambridge: Cambridge University Press.Google Scholar
Ohlsson, S., Ernst, A. M., & Rees, E. (1992). The cognitive complexity of doing and learning arithmetic. Journal of Research in Mathematics Education, 23(5), 441–467.Google Scholar
Ohlsson, S., & Jewett, J. J. (1997). Ideal adaptive agents and the learning curve. In Brzezinski, J., Krause, B., & Maruszewski, T. (Eds.), Idealization VIII: Modelling in Psychology (pp. 139–176). Amsterdam: Rodopi.Google Scholar
Ohlsson, S., & Rees, E. (1991a). The function of conceptual understanding in the learning of arithmetic procedures. Cognition and Instruction, 8, 103–179.Google Scholar
Ohlsson, S., & Rees, E. (1991b). Adaptive search through constraint violation. Journal of Experimental and Theoretical Artificial Intelligence, 3, 33–42.Google Scholar
Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology, 48, 128–138.Google Scholar
Paik, J., Kim, J. W., Ritter, F. E., & Reitter, D. (2005). Predicting user performance and learning in human-computer interaction with the Herbal compiler. Transactions on Computer-Human Interaction, 22, Article 25.Google Scholar
Pirolli, P. (1986). A cognitive model and computer tutor for programming recursion. Human-Computer Interaction, 2, 319–355.Google Scholar
Pirolli, P. (1991). Effects of examples and their explanations in a lesson on recursion: a production system analysis. Cognition and Instruction, 8, 207–259.Google Scholar
Pirolli, P., & Recker, M. (1994). Learning strategies and transfer in the domain of programming. Cognition and Instruction, 12, 235–275.Google Scholar
Polk, T. A., & Seifert, C. M. (Eds.). (2002). Cognitive Modeling. Cambridge, MA: MIT Press.Google Scholar
Reason, J. (1990). Human Error. Cambridge: Cambridge University Press.Google Scholar
Reimann, P., Schult, T. J., & Wichmann, S. (1993). Understanding and using worked-out examples: a computational model. In Strube, G. & Wender, K. (Eds.), The Cognitive Psychology of Knowledge (pp. 177–201). Amsterdam: North-Holland.Google Scholar
Restle, R. (1955). A theory of discrimination learning. Psychological Review, 62, 11–19.Google Scholar
Ritter, F. E., & Bibby, P. (2001). Modeling how and when learning happens in a simple fault-finding task. In Proceedings of the Fourth International Conference on Cognitive Modeling (pp. 187–192). Mahwah, NJ: Erlbaum.Google Scholar
Ritter, F. E., & Bibby, P. A. (2008). Modeling how, when, and what is learned in a simple fault‐finding task. Cognitive Science, 32, 862–892.Google Scholar
Ritter, F. E., Jones, R. M., & Baxter, G. D. (1998). Reusable models and graphical interfaces: realizing the potential of a unified theory of cognition. In Schmid, U., Krems, J. K., & Wysotzki, F. W. (Eds.), Mind Modeling: A Cognitive Science Approach to Reasoning, Learning and Discovery (pp. 83–109). Lengerich: Pabst Scientific Publishing.Google Scholar
Rosenbloom, P., & Newell, A. (1986). The chunking of goal hierarchies: a generalized model of practice. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 2, pp. 247–288). Los Altos, CA: Kaufmann.Google Scholar
Rosenbloom, P., & Newell, A. (1987). Learning by chunking: a production system model of practice. In Klahr, D., Langley, P., & Neches, R. (Eds.), Production System Models of Learning and Development (pp. 221–286). Cambridge, MA: MIT Press.Google Scholar
Rosenbloom, P. S., Laird, J. E., & Newell, A. (Eds.). (1993). The Soar Papers: Research on Integrated Intelligence (Volumes 1 and 2). Cambridge, MA: MIT Press.Google Scholar
Ruiz, D., & Newell, A. (1993). Tower-noticing triggers strategy-change in the Tower of Hanoi: a Soar model. In Rosenbloom, P. S., Laird, J. E., & Newell, A. (Eds.), The Soar Papers: Research on Integrated Intelligence (vol. 2, pp. 934–941). Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (Eds.). (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Volumes 1 and 2). Cambridge, MA: MIT Press.Google Scholar
Rychener, M. D. (1983). The instructible production system: a retrospective approach. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (pp. 429–459). Palo Alto, CA: Tioga.Google Scholar
Rychener, M. D., & Newell, A. (1978). An instructable production system: basic design issues. In Waterman, D. A. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 135–153). New York, NY: Academic Press.Google Scholar
Ryle, G. (1968/1949). The Concept of Mind. London: Penguin.Google Scholar
Salomon, G., & Perkins, D. N. (1989). Rocky roads to transfer: rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113–142.CrossRefGoogle Scholar
Salvucci, D. D. (2013). Integration and reuse in cognitive skill acquisition. Cognitive Science, 37, 829–860.CrossRefGoogle ScholarPubMed
Salvucci, D. D., & Anderson, J. R. (1998). Analogy. In Anderson, J. R. & Lebiere, C. (Eds.), The Atomic Components of Thought (pp. 343–383). Mahwah, NJ: Erlbaum.Google Scholar
Salvucci, D. D., & Anderson, J. R. (2001). Integrating analogical mapping and general problem solving: the path-mapping theory. Cognitive Science, 25, 67–110.Google Scholar
Schneider, W., & Chein, J. M. (2003). Controlled & automatic processing: behavior, theory, and biological mechanisms. Cognitive Science, 27, 525–559.Google Scholar
Schneider, W., & Oliver, W. L. (1991). An instructable connectionist/control architecture: using rule-based instructions to accomplish connectionist learning in a human time scale. In VanLehn, K. (Ed.), Architectures for Intelligence (pp. 113–145). Hillsdale, NJ: Erlbaum.Google Scholar
Shrager, J., Hogg, T., & Huberman, B. A. (1988). A graph-dynamic model of the power law of practice and the problem-solving fan effect. Science, 242, 414–416.Google Scholar
Shrager, J., & Siegler, R. S. (1998). A model of children’s strategy choices and strategy discoveries. Psychological Science, 9, 405–410.Google Scholar
Siegler, R., & Araya, R. (2005). A computational model of conscious and unconscious strategy discovery. In Kail, R. V. (Ed.), Advances in Child Development and Behavior (vol. 33, pp. 1–42). Oxford: Elsevier.Google Scholar
Siegler, R. S., & Shipley, C. (1995). Variation, selection, and cognitive change. In Simon, T. J. & Halford, G. S. (Eds.), Developing Cognitive Competencies: New Approaches to Process Modeling (pp. 31–76). Hillsdale, NJ: Erlbaum.Google Scholar
Siegler, R. S., & Shrager, J. (1984). Strategy choices in addition and subtraction: how do children know what to do? In Sophian, C. (Ed.), Origins of Cognitive Skills (pp. 229–293). Hillsdale, NJ: Erlbaum.Google Scholar
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Revew, 63, 129–138.Google Scholar
Simon, H. A. (1972). On reasoning about actions. In Simon, H. A. & Siklossy, L. (Eds.), Representation and Meaning (pp. 414–430). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Singley, M. K., & Anderson, J. R. (1989). The Transfer of Cognitive Skill. Cambridge, MA: Harvard University Press.Google Scholar
Spellman, B. A., & Holyoak, K. J. (1996). Pragmatics in analogical mapping. Cognitive Psychology, 31, 307–346.Google Scholar
Stearns, B., & Laird, J. E. (2018). Modeling instruction fetch in procedural learning. In 16th International Conference on Cognitive Modelling (ICCM), Madison, WI.Google Scholar
Stevens, J. C., & Savin, H. B. (1962). On the form of learning curves. Journal of the Experimental Analysis of Behavior, 5 , 15–18.Google Scholar
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25, 203–244.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112, 159–192.Google Scholar
Taatgen, N. A. (2005). Modeling parallelization and flexibility improvements in skill acquisition: from dual tasks to complex dynamic skills. Cognitive Science, 29, 421–455.Google Scholar
Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120, 439–471.Google Scholar
Taatgen, N. A., & Anderson, J. R. (2002). Why do children learn to say “Broke”? A model of learning the past tense without feedback. Cognition, 86, 123–155.Google Scholar
Taatgen, N. A., & Lee, F. J. (2003). Production compilation: a simple mechanism to model complex skill acquisition. Human Factors, 45, 61–76.Google Scholar
Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: a survey. Journal of Machine Learning Research, 10, 1633–1685.Google Scholar
Tenison, C., Fincham, J. M., & Anderson, J. A. (2016). Phases of learning: how skill acquisition impacts cognitive processing. Cognitive Psychology, 87, 1–28.Google Scholar
Thorndike, E. L. (1898). Animal intelligence: an experimental study of the associative processes in animals. Dissertation, Ph.D., Columbia University.Google Scholar
Thorndike, E. L. (1911). The Principles of Teaching Based on Psychology. New York, NY: A. G. Seiler.Google Scholar
Thorndike, E. L. (1927). The law of effect. American Journal of Psychology, 39, 212–222.Google Scholar
VanLehn, K. (1983 ). Felicity Conditions for Human Skill Acquisition: Validating an AI Based Theory (Technical Report CIS 21). Palo Alto, CA: Xerox Palo Alto Research Centers.Google Scholar
VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence, 31, 1–40.Google Scholar
VanLehn, K. (1988). Toward a theory of impasse-driven learning. In Mandl, H. & Lesgold, A. (Eds.), Learning Issues for Intelligent Tutoring Systems (pp. 19–41). New York, NY: Springer Verlag.Google Scholar
VanLehn, K. (1990). Mind Bugs: The Origins of Procedural Misconceptions. Cambridge, MA: MIT Press.Google Scholar
VanLehn, K. (1998). Analogy events: how examples are used during problem solving. Cognitive Science, 22, 347–388.Google Scholar
VanLehn, K. (1999). Rule-learning events in the acquisition of a complex skill: an evaluation of Cascade. The Journal of the Learning Sciences, 8, 71–125.Google Scholar
VanLehn, K., & Jones, R. (1993). Learning by explaining examples to oneself: a computational model. In Chipman, S. & Meyrowitz, A. L. (Eds.), Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning (pp. 25–82). Boston, MA: Kluwer.Google Scholar
VanLehn, K., Jones, R. M., & Chi, M. T. H. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2, 1–59.Google Scholar
VanLehn, K., Ohlsson, S., & Nason, R. (1994) Applications of simulated students: an exploration. Journal of Artificial Intelligence and Education, 5, 135–175.Google Scholar
Veloso, M. M., & Carbonell, J. G. (1993). Derivational analogy in Prodigy: automating case acquisition, storage and utilization. Machine Learning, 10, 249–278.Google Scholar
Waterman, D., & Hayes-Roth, F. (1978). An overview of pattern-directed inference systems. In Waterman, D. & Hayes-Roth, F. (Eds.), Pattern-Directed Inference Systems (pp. 3–22). New York, NY: Academic Press.Google Scholar
Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20, 158–177.Google Scholar
Weiner, N. (1948). Cybernetics. Wiley, NY: Technology Press.Google Scholar
Welford, A. T. (1968). Fundamentals of Skill. London: Methuen.Google Scholar
Wilson, W. H., Halford, G. S., Gray, B., & Phillips, S. (2001). The STAR-2 model for mapping hierarchically structured analogs. In Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.), The Analogical Mind: Perspectives from Cognitive Science (pp. 125–159). Cambridge, MA: MIT Press.Google Scholar
Winograd, T. (1975). Frame representations and the declarative/procedural controversy. In Bobrow, D. & Collins, A. (Eds.), Representation and Understanding: Studies in Cognitive Science (pp. 185–210). New York, NY: Academic Press.Google Scholar
Winston, P. H. (1986). Learning by augmenting rules and accumulating censors. In Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.), Machine Learning: An Artificial Intelligence Approach (vol. 3, pp. 45–61). Los Altos, CA: Kaufmann.Google Scholar
Woltz, D. J., Gardner, M. K., & Bell, B. G. (2000). Negative transfer errors in sequential skills: strong-but-wrong sequence application. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(3), 601–625.Google Scholar
Woodworth, R. S. (1938). Experimental Psychology. New York, NY: Henry Holt.Google Scholar

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