Individual differences in learning and transfer: Stable tendencies for learning exemplars versus abstracting rules

Journal of Experimental Psychology: General

Volumn 143, Issue 2, 2014, Pages 668-693

  McDaniel, Mark A ^a   Cahill, Michael J ^a   Robbins, Mathew ^a   Wiener, Chelsea ^a  

^a WASHINGTON UNIVERSITY   (United States)

Author keywords

Concept learning and transfer; Exemplar learning; Individual differences in concept learning; Rule abstraction

Indexed keywords

COGNITION; CONCEPT FORMATION; HUMAN; INDIVIDUALITY; LEARNING;

COGNITION; CONCEPT FORMATION; HUMANS; INDIVIDUALITY; LEARNING; TRANSFER (PSYCHOLOGY);

EID: 84897549906     PISSN: 00963445     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0032963     Document Type: Article

References (63)

1. Anderson, J. R.,&Betz, J. (2001). A hybrid model of categorization. Psychonomic Bulletin&Review, 8, 629-647.
2. Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U.,&Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105, 442-481.
3. Ashby, F. G., Ell, S. W.,&Waldron, E. M. (2003). Procedural learning in perceptual categorization. Memory&Cognition, 31, 1114-1125.
4. Ashby, F. G.,&Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149-178.
5. Ashby, F. G.,&O 'Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83-89.
6. Baddeley, A. D.,&Hitch, G. (1974). Working memory. Psychology of Learning and Motivation, 8, 47-89.
7. Balota, D. A., Yap, M. J., Cortese, M. J., Hutchison, K. A., Kessler, B., Loftis, B., . . . Treiman, R. (2007). The English Lexicon Project. Behavior Research Methods, 39, 445-459.
8. Bors, D. A.,&Stokes, T. L. (1998). Ravens Advanced Progressive Matrices: Norms for first-year university students and the development of a short form. Educational and Psychological Measurement, 58, 382-398.
9. Bott, L.,&Heit, E. (2004). Nonmonotonic extrapolation in functionlearning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 38-50.
10. Bourne, L. E., Jr. (1974). An inference model of conceptual rule learning. In R. L. Solso (Ed), Theories in cognitive psychology: The Loyola symposium (pp. 231-256). Potomac, MD: Erlbaum.
11. Bower, G.,&Trabasso, T. (1964). Concept identification. In R. C. Atkinson (Ed.), Studies in mathematical psychology (pp. 32-94). Palo Alto, CA: Stanford University Press.
12. Busemeyer, J. R., Byun, E., DeLosh, E.,&McDaniel, M. A. (1997). Learning functional relations based on experience with input-output pairs by humans and artificial neural networks. In K. Lamberts & D. R. Shanks (Eds.), Knowledge, concepts, and categories (pp. 405-435). Hove, England: Psychology Press.
13. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42, 116-131.
14. Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404-431.
15. Choi, S., McDaniel, M. A., & Busemeyer, J. R. (1993). Incorporating prior biases in network models of conceptual rule learning. Memory & Cognition, 21, 413-423.
16. Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O.,&Engle, R. W. (2005). Working memory span tasks: A methodological review and user 's guide. Psychonomic Bulletin & Review, 12, 769-786.
17. Craig, S., & Lewandowsky, S. (2012). Whichever way you choose to categorize, working memory helps you learn. The Quarterly Journal of Experimental Psychology, 65, 439-464.
18. DeLosh, E. L., Busemeyer, J. R., & McDaniel, M. A. (1997). Extrapolation: The sine qua non for abstraction in function-learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 968-986.
19. Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23.
20. Erickson, J. E., Chin-Parker, S., & Ross, B. H. (2005). Inference and classification learning of abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 86-99.
21. Erickson, M. A. (2008). Executive function and task switching in category learning: Evidence for stimulus-dependent representation. Memory & Cognition, 36, 749-761.
22. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140.
23. Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: The relationship between fluid intelligence and working memory capacity. Psychonomic Bulletin&Review, 17, 673-679.
24. Hayes, T., Petrov, A., & Sederberg, P. (2011). A novel method for analyzing sequential eye movements reveals strategic influence on Raven 's Advanced Progressive Matrices. Journal of Vision, 11, 10-11.
25. Johansen, M. K., & Palmeri, T. J. (2002). Are there representational shifts during category learning? Cognitive Psychology, 45, 482-553.
26. Juslin, P., Olsson, H., & Olsson, A.-C. (2003). Exemplar effects in categorization and multiple-cue judgment. Journal of Experimental Psychology: General, 132, 133-156.
27. Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: Knowledge partitioning and function-learning. Psychological Review, 111, 1072-1099.
28. Katona, G. (1940). Organizing and memorizing. New York, NY: Columbia University Press.
29. Klayman, J. (1988). On the how and why (not) of learning from outcomes. In B. Brehmer & C. R. B. Joyce (Eds.), Human judgment: The SJT view (pp. 115-162). Amsterdam, the Netherlands: Elsevier.
30. Klein, K., & Fiss, W. H. (1999). The reliability and stability of the Turner and Engle working memory task. Behavior Research Methods, Instruments, & Computers, 31, 429-432.
31. Koh, K., & Meyer, D. E. (1991). function-learning: Induction of continuous stimulus-response relations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 811-836.
32. Kolb, D. A. (2007). Kolb Learning Style Inventory (Version 3.1). Boston, MA: Hay Group.
33. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44.
34. Kucera, H., & Francis, W. N. (1967). Computational analysis of presentday English. Providence, RI: Brown University Press.
35. Lewandowsky, S. (2011). Working memory capacity and categorization: Individual differences and modeling. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 720-738.
36. Lewandowsky, S., Kalish, M., & Ngang, S. K. (2002). Simplified learning in complex situations: Knowledge partitioning in function-learning. Journal of Experimental Psychology: General, 131, 163-193.
37. Little, D. R., Nosofsky, R. M., & Denton, S. E. (2011). Response-time tests of logical-rule models of categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 1-27.
38. Little, J. L., McDaniel, M. A., & Cahill, M. J. (2012, November). Individual differences in category learning: Rule-versus exemplar-based strategies. Poster presented at the 53rd meeting of the Psychonomic Society, Minneapolis, MN.
39. McCabe, D. P., Roediger, H. L., III, McDaniel, M. A., Balota, D., & Hambrick, J. (2010). The relationship between working memory capacity and executive functioning: Evidence for a common executive attention construct. Neuropsychology, 24, 222-243.
40. McDaniel, M. A.,&Busemeyer, J. R. (2005). The conceptual basis of function-learning and extrapolation: Comparison of rule-based and associative-based models. Psychonomic Bulletin&Review, 12, 24-42.
41. McDaniel, M. A., Dimperio, E., Griego, J. A.,&Busemeyer, J. R. (2009). Predicting transfer performance: A comparison of competing functionlearning models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 173-195.
42. McDaniel, M. A., Frey, R., Kudelka, C., & Shields, S. P. (2012, June). Individual differences in concept learning tendencies: Spanning the laboratory and the classroom. Paper presented at the POGIL South Central Regional Meeting, St. Louis, MO.
43. Medin, D. L., Altom, M. W.,&Murphy, T. D. (1984). Given versus induced category representations: Use of prototype and exemplar information in classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 333-352.
44. Medin, D. L.,&Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238.
45. Minda, J. P., Desroches, A. S.,&Church, B. A. (2008). Learning ruledescribed and non-rule-described categories: A comparison of children and adults. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 1518-1533.
46. Nelson, D. L., McEvoy, C. L.,&Schreiber, T. A. (1998). The University of South Florida word association, rhyme, and word fragment norms. Retrieved from http://www.usf.edu/FreeAssociation/
47. Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104-114.
48. Nosofsky, R. M. (1986). Attention, similarity, and the identificationcategorization relationship. Journal of Experimental Psychology: General, 115, 39-57.
49. Nosofsky, R. M., & Kruschke, J. K. (1992). Investigations of an exemplarbased connectionist model of category learning. In D. L. Medin (Ed.), The psychology of learning and motivation (Vol. 28, pp. 207-250). San Diego, CA: Academic Press.
50. Nosofsky, R. M., Palmeri, T. J., & McKinley, S. C. (1994). Rule-plusexception model of classification learning. Psychological Review, 101, 53-79.
51. Posner, M. I., & Keele, S. W. (1968). On the genesis of abstract ideas. Journal of Experimental Psychology, 77, 353-363.
52. Raven, J. C., Raven, J. E., & Court, J. H. (1998). Progressive matrices. Oxford, England: Oxford Psychologists Press.
53. Regehr, G., & Brooks, L. R. (1993). Perceptual manifestations of an analytic structure: The priority of holistic individuation. Journal of Experimental Psychology: General, 122, 92-114.
54. Rehder, B., & Ross, B. H. (2001). Abstract coherent categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1261-1275.
55. Sewell, D. K., & Lewandowsky, S. (2012). >Attention and working memory capacity: Insights from blocking, highlighting, and knowledge restructuring. Journal of Experimental Psychology: General, 141, 444-469.
56. Smith, J. D., & Minda, J. P. (1998). Prototypes in the mist: The early epochs of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24, 1411-1436.
57. Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28, 127-154.
58. Unsworth, N., Heitz, R. P., Schrock, J. C.,&Engle, R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37, 498-505.
59. Wenke, D., Frensch, P. A., & Funke, J. (2005). Complex problem solving and intelligence: Empirical relation and causal direction. In R. J. Sternberg & J. E. Pretz (Eds.), Cognition&intelligence: Identifying the mechanisms of the mind (pp. 160-187). New York, NY: Cambridge University Press.
60. Wiley, J., Jarosz, A. F., Cushen, P. J., & Colflesh, G. J. H. (2011). New rule use drives the relation between working memory capacity and Raven 's Advanced Progressive Matrices. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 256-263.
61. Wisniewski, E. J. (1995). Prior knowledge and functionally relevant features in concept learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 449-468.
62. Yang, L.-X., & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 663-679.
63. Yang, L.-X., & Lewandowsky, S. (2004). Knowledge partitioning in categorization: Constraints on exemplar models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 1045-1064.