Correction to "Attention and Working Memory Capacity: Insights From Blocking, Highlighting, and Knowledge Restructuring," [Journal of Experimental Psychology: General 141, 3, (2012) 444-469] DOI: 10.1037/a0026560;Attention and working memory capacity: Insights from blocking, highlighting, and knowledge restructuring

Journal of Experimental Psychology: General

Volumn 141, Issue 3, 2012, Pages 444-469

  Sewell, David K ^a   Lewandowsky, Stephan ^b  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

Associative learning; Attention; Category learning; Knowledge restructuring; Working memory capacity

Indexed keywords

ARTICLE; ASSOCIATION; ATTENTION; FEMALE; HUMAN; KNOWLEDGE; LEARNING; MALE; NEUROPSYCHOLOGICAL TEST; PHYSIOLOGY; PROBLEM SOLVING; SHORT TERM MEMORY;

ASSOCIATION LEARNING; ATTENTION; CUES; FEMALE; HUMANS; KNOWLEDGE; MALE; MEMORY, SHORT-TERM; NEUROPSYCHOLOGICAL TESTS; PROBLEM SOLVING;

EID: 84872139869     PISSN: 00963445     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0035672     Document Type: Erratum

References (93)

1. Aha, D. W., & Goldstone, R. L. (1992). Concept learning and flexible weighting. In J. K. Kruschke (Ed.), Proceedings of the 14th Annual Conference of the Cognitive Science Society (pp. 534-539). Hillsdale, NJ: Erlbaum.
2. Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149-178. doi:10.1146/annurev.psych.56.091103.070217
3. Ashby, F. G., & O'Brien, J. B. (2005). Category learning and multiple memory systems. Trends in Cognitive Sciences, 9, 83-89. doi:10.1016/ j.tics.2004.12.003
4. Beesley, T., & Le Pelley, M. E. (2011). The influence of blocking on overt attention and associability in human learning. Journal of Experimental Psychology: Animal Behavior Processes, 37, 114-120. doi:10.1037/ a0019526
5. Blair, M. R., Watson, M. R., Walshe, R. C., & Maj, F. (2009). Extremely selective attention: Eye-tracking studies of the dynamic allocation of attention to stimulus features in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35, 1196-1206. doi: 10.1037/a0016272
6. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433-436. doi:10.1163/156856897X00357
7. Charles, E. P. (2005). The correction for attenuation due to measurement error: Clarifying concepts and creating confidence sets. Psychological Methods, 10, 206-226. doi:10.1037/1082-989X.10.2.206
8. 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. doi:10.3758/BF03196772
9. Cools, R., Ivry, R. B., & D'Esposito, M. (2006). The human striatum is necessary for responding to changes in stimulus relevance. Journal of Cognitive Neuroscience, 18, 1973-1983. doi:10.1162/jocn.2006.18.12.1973
10. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87-114. doi:10.1017/S0140525X01003922
11. Cowan, N. (2005). Working memory capacity. New York, NY: Psychology Press. doi:10.4324/9780203342398
12. Craig, S., & Lewandowsky, S. (2011). Whichever way you choose to categorize, working memory helps you learn. The Quarterly Journal of Experimental Psychology. Advance online publication. doi:10.1080/ 17470218.2011.608854
13. Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning & Verbal Behavior, 19, 450-466. doi:10.1016/S0022-5371(80)90312-6
14. Denton, S. E., & Kruschke, J. K. (2006). Attention and salience in associative blocking. Learning & Behavior, 34, 285-304. doi:10.3758/ BF03192884
15. Denton, S. E., Kruschke, J. K., & Erickson, M. A. (2008). Rule-based extrapolation: A continuing challenge for exemplar models. Psychonomic Bulletin & Review, 15, 780-786. doi:10.3758/PBR.15.4.780
16. Ecker, U. K. H., Lewandowsky, S., Oberauer, K., & Chee, A. E. H. (2010). The components of working memory updating: An experimental decomposition and individual differences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 170-189. doi:10.1037/a0017891
17. Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23. doi:10.1111/ 1467-8721.00160
18. Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 44, pp. 145-199). Amsterdam, the Netherlands: Elsevier.
19. Engle, R. W., Tuholski, S. W., Laughlin, J. E., & Conway, A. R. A. (1999). Working memory, short-term memory, and general fluid intelligence: A latent-variable approach. Journal of Experimental Psychology: General, 128, 309-331. doi:10.1037/0096-3445.128.3.309
20. Erickson, M. A. (2008). Executive attention and task switching in category learning: Evidence for stimulus-dependent representation. Memory & Cognition, 36, 749-761. doi:10.3758/MC.36.4.749
21. Erickson, M. A., & Kruschke, J. K. (1998). Rules and exemplars in category learning. Journal of Experimental Psychology: General, 127, 107-140. doi:10.1037/0096-3445.127.2.107
22. Erickson, M. A., & Kruschke, J. K. (2002a). Multiple representations in inductive category learning: Evidence of stimulus- and task-dependent representation. Unpublished manuscript.
23. Erickson, M. A., & Kruschke, J. K. (2002b). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9, 160-168. doi:10.3758/BF03196273
24. Fan, X. (2003). Two approaches for correcting correlation attenuation cause by measurement error: Implications for research practice. Educational and Psychological Measurement, 63, 915-930. doi:10.1177/ 0013164403251319
25. Gluck, M. A., & Bower, G. H. (1988). From conditioning to category learning: An adaptive network model. Journal of Experimental Psychology: General, 117, 227-247. doi:10.1037/0096-3445.117.3.227
26. Hoffman, A. B., & Rehder, B. (2010). The costs of supervised classification: The effect of learning task on conceptual flexibility. Journal of Experimental Psychology: General, 139, 319-340. doi:10.1037/ a0019042
27. Johansen, M. K., Fouquet, N., & Shanks, D. R. (2010). Featural selective attention, exemplar representation, and the inverse base-rate effect. Psychonomic Bulletin & Review, 17, 637-643. doi:10.3758/ PBR.17.5.637
28. Kahneman, D. (1973). Attention and effort. Englewood Cliffs, NJ: Prentice Hall.
29. Kalish, M. L. (2001). An inverse base rate effect with continuously valued stimuli. Memory & Cognition, 29, 587-597. doi:10.3758/BF03200460
30. Kalish, M. L., & Kruschke, J. K. (2000). The role of attention shifts in the categorization of continuous dimensioned stimuli. Psychological Research, 64, 105-116. doi:10.1007/s004260000028
31. Kalish, M. L., Lewandowsky, S., & Davies, M. (2005). Error-driven knowledge restructuring in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 846-861. doi:10.1037/ 0278-7393.31.5.846
32. Kamin, L. J. (1968). "Attention-like" processes in classical conditioning. In M. R. Jones (Ed.), Miami Symposium on the Prediction of Behavior: Aversive stimulation (pp. 9-33). Coral Gables, FL: University of Miami Press.
33. Kamin, L. J. (1969). Predictability, surprise, attention, and conditioning. In R. M. Church & B. A. Campbell (Eds.), Punishment and aversive behavior (pp. 279-296). New York, NY: Appleton-Century-Crofts.
34. Kane, M. J., Bleckley, M. K., Conway, A. R. A., & Engle, R. W. (2001). A controlled-attention view of working-memory capacity. Journal of Experimental Psychology: General, 130, 169-183. doi:10.1037/0096-3445.130.2.169
35. Kane, M. J., Conway, A. R. A., Hambrick, D. Z., & Engle, R. W. (2007). Variation in working memory capacity as variation in executive attention and control. In A. R. A. Conway, C. Jarrold, A. Miyake, & J. Towse (Eds.), Variation in working memory (pp. 21-46). Oxford, England: Oxford University Press.
36. Kane, M. J., & Engle, R. W. (2003). Working-memory capacity and the control of attention: The contributions of goal neglect, response competition, and task set to Stroop interference. Journal of Experimental Psychology: General, 132, 47-70. doi:10.1037/0096-3445.132.1.47
37. Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99, 22-44. doi: 10.1037/0033-295X.99.1.22
38. Kruschke, J. K. (1996a). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 3-26. doi:10.1037/0278-7393.22.1.3
39. Kruschke, J. K. (1996b). Dimensional relevance shifts in category learning. Connection Science, 8, 225-247. doi:10.1080/095400996116893
40. Kruschke, J. K. (2001a). The inverse base-rate effect is not explained by eliminative inference. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1385-1400. doi:10.1037/0278-7393.27.6.1385
41. Kruschke, J. K. (2001b). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812-863. doi: 10.1006/jmps.2000.1354
42. Kruschke, J. K. (2003). Attention in learning. Current Directions in Psychological Science, 12, 171-175. doi:10.1111/1467-8721.01254
43. Kruschke, J. K. (2005a). Category learning. In K. Lamberts & R. L. Goldstone (Eds.), The handbook of cognition (pp. 183-201). London, England: Sage.
44. Kruschke, J. K. (2005b). Learning involves attention. In G. Houghton (Ed.), Connectionist models in cognitive psychology (pp. 113-140). Hove, East Sussex, United Kingdom: Psychology Press.
45. Kruschke, J. K. (2008). Models of categorization. In R. Sun (Ed.), The Cambridge handbook of computational psychology (pp. 267-301). Cambridge, England: Cambridge University Press.
46. Kruschke, J. K. (2009). Highlighting: A canonical experiment. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 51, pp. 153-185). doi:10.1016/S0079-7421(09)51005-5
47. Kruschke, J. K., & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636-645. doi:10.3758/BF03213001
48. Kruschke, J. K., & Johansen, M. K. (1999). A model of probabilistic category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 1083-1119. doi:10.1037/0278-7393.25.5.1083
49. Kruschke, J. K., Kappenman, E. S., & Hetrick, W. P. (2005). Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 830-845. doi:10.1037/0278-7393.31.5.830
50. Lamberts, K., & Kent, C. (2007). No evidence for rule-based processing in the inverse base-rate effect. Memory & Cognition, 35, 2097-2105. doi:10.3758/BF03192941
51. Le Pelley, M. E. (2004). The role of associative history in models of associative learning: A selective review and a hybrid model. The Quarterly Journal of Experimental Psychology B: Comparative and Physiological Psychology, 57(B), 193-243.
52. Le Pelley, M. E., Beesley, T., & Suret, M. B. (2007). Blocking of human causal learning involves learned changes in stimulus processing. The Quarterly Journal of Experimental Psychology, 60, 1468-1476. doi: 10.1080/17470210701515645
53. Lewandowsky, S. (2011). Working memory capacity and categorization: Individual differences and modeling. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37, 720-738. doi:10.1037/ a0022639
54. Lewandowsky, S., Kalish, M., & Griffiths, T. L. (2000). Competing strategies in categorization: Expediency and resistance to knowledge restructuring. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1666-1684. doi:10.1037/0278-7393.26.6.1666
55. Lewandowsky, S., Oberauer, K., Yang, L.-X., & Ecker, U. K. H. (2010). A working memory test battery for MATLAB. Behavior Research Methods, 42, 571-585. doi:10.3758/BRM.42.2.571
56. Lewandowsky, S., Roberts, L., & Yang, L.-X. (2006). Knowledge partitioning in categorization: Boundary conditions. Memory & Cognition, 34, 1676-1688. doi:10.3758/BF03195930
57. Lewandowsky, S., Yang, L.-X., Newell, B. R., & Kalish, M. L. (2011). Working memory does not dissociate between different perceptual categorization tasks. Manuscript submitted for publication.
58. Little, D. R., & Lewandowsky, S. (2009). Beyond nonutilization: Irrelevant cues can gate learning in probabilistic categorization. Journal of Experimental Psychology: Human Perception and Performance, 35, 530-550. doi:10.1037/0096-1523.35.2.530
59. Little, D. R., Lewandowsky, S., & Heit, E. (2006). Ad hoc category restructuring. Memory & Cognition, 34, 1398-1413. doi:10.3758/ BF03195905
60. Logan, G. D. (2002). An instance theory of attention and memory. Psychological Review, 109, 376-400. doi:10.1037/0033-295X.109.2.376
61. Logan, G. D. (2004). Cumulative progress in formal theories of attention. Annual Review of Psychology, 55, 207-234. doi:10.1146/annurev.psych.55.090902.141415
62. Love, B. C., Medin, D. L., & Gureckis, T. M. (2004). SUSTAIN: A network model of category learning. Psychological Review, 111, 309-332. doi:10.1037/0033-295X.111.2.309
63. Mackintosh, N. J. (1975). A theory of attention: Variations in the associability of stimuli with reinforcement. Psychological Review, 82, 276-298. doi:10.1037/h0076778
64. Medin, D. L., & Edelson, S. M. (1988). Problem structure and the use of base-rate information from experience. Journal of Experimental Psychology: General, 117, 68-85. doi:10.1037/0096-3445.117.1.68
65. Medin, D. L., & Schaffer, M. M. (1978). Context theory of classification learning. Psychological Review, 85, 207-238. doi:10.1037/0033-295X.85.3.207
66. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, A. (2000). The unity and diversity of executive functions and their contributions to complex "frontal lobe" tasks: A latent variable analysis. Cognitive Psychology, 41, 49-100. doi:10.1006/cogp.1999.0734
67. Neisser, U. (1967). Cognitive psychology. New York, NY: Appleton-Century-Crofts.
68. Newell, B. R., Dunn, J. C., & Kalish, M. (2011). Systems of category learning: Fact or fantasy? In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 54, pp. 167-215). Amsterdam, the Netherlands: Elsevier.
69. Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10, 104-114. doi:10.1037/0278-7393.10.1.104
70. Nosofsky, R. M. (1986). Attention, similarity, and the identificationcategorization relationship. Journal of Experimental Psychology: General, 115, 39-57. doi:10.1037/0096-3445.115.1.39
71. Nosofsky, R. M., Palmeri, T. J., & Mckinley, S. C. (1994). Rule-plusexception model of classification learning. Psychological Review, 101, 53-79. doi:10.1037/0033-295X.101.1.53
72. Oberauer, K. (1993). Die koordination kognitiver operationen: Eine studie über die beziehung zwischen intelligenz und "working memory" [The coordination of cognitive operations: A study on the relation of intelligence and "working memory"]. Zeitschrift für Psychologie mit Zeitschrift für angewandte Psychologie, 201, 57-84.
73. Oberauer, K. (2009). Design for a working memory. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 51, pp. 45-100). doi:10.1016/S0079-7421(09)51002-X
74. Oberauer, K., & Kliegl, R. (2001). Beyond resources: Formal models of complexity effects and age differences in working memory. European Journal of Cognitive Psychology, 13, 187-215. doi:10.1080/ 09541440042000278
75. Oberauer, K., Süß, H.-M., Schulze, R., Wilhelm, O., & Wittman, W. W. (2000). Working memory capacity-Facets of a cognitive ability construct. Personality and Individual Differences, 29, 1017-1045. doi: 10.1016/S0191-8869(99)00251-2
76. Oberauer, K., Süß, H.-M., Wilhelm, O., & Sander, N. (2007). Individual differences in working memory capacity and reasoning ability. In A. R. A. Conway, C. Jarrold, M. J. Kane, A. Miyake, & J. N. Towse (Eds.), Variation in working memory (pp. 49-75). Oxford, England: Oxford University Press.
77. Oberauer, K., Süß, H.-M., Wilhelm, O., & Wittman, W. W. (2003). The multiple faces of working memory: Storage, processing, supervision, and coordination. Intelligence, 31, 167-193. doi:10.1016/S0160-2896(02)00115-0
78. Pelli, D. G. (1997). The videotoolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437-442. doi: 10.1163/156856897X00366
79. Posner, M. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3-25. doi:10.1080/00335558008248231
80. Rehder, B., & Hoffman, A. B. (2005a). Eyetracking and selective attention in category learning. Cognitive Psychology, 51, 1-41. doi:10.1016/ j.cogpsych.2004.11.001
81. Rehder, B., & Hoffman, A. B. (2005b). Thirty-something categorization results explained: Selective attention, eyetracking, and models of category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 811-829. doi:10.1037/0278-7393.31.5.811
82. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning ii: Current research and theory (pp. 64-99). New York, NY: Appleton-Century-Crofts.
83. Salthouse, T. A., Babcock, R. L., & Shaw, R. J. (1991). Effects of adult age on structural and operational capacities in working memory. Psychology and Aging, 6, 118-127. doi:10.1037/0882-7974.6.1.118
84. Sewell, D. K., & Lewandowsky, S. (2011). Restructuring partitioned knowledge: The role of recoordination in category learning. Cognitive Psychology, 62, 81-122. doi:10.1016/j.cogpsych.2010.09.003
85. Shanks, D. R. (1985). Forward and backward blocking in human contingency judgement. The Quarterly Journal of Experimental Psychology B: Comparative and Physiological Psychology, 37(B), 1-21.
86. Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75, 1-42. doi:10.1037/h0093825
87. Smith, J. D., & Minda, J. P. (2000). Thirty categorization results in search of a model. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 3-27. doi:10.1037/0278-7393.26.1.3
88. Spearman, C. (1904). The proof and measurement of association between two things. The American Journal of Psychology, 15, 72-101. doi: 10.2307/1412159
89. Tharp, I. J., & Pickering, A. D. (2009). A note on DeCaro, Thomas, and Beilock (2008): Further data demonstrate complexities in the assessment of information-integration category learning. Cognition, 111, 410-414. doi:10.1016/j.cognition.2008.10.003
90. Treisman, A. M. (1969). Strategies and models of selective attention. Psychological Review, 76, 282-299. doi:10.1037/h0027242
91. Turner, M., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language, 28, 127-154. doi: 10.1016/0749-596X(89)90040-5
92. Yang, L.-X., & Lewandowsky, S. (2003). Context-gated knowledge partitioning in categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 663-679. doi:10.1037/0278-7393.29.4.663
93. 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. doi: 10.1037/0278-7393.30.5.1045