Advertisement

Memory & Cognition

, Volume 46, Issue 2, pp 204–215 | Cite as

Item strength affects working memory capacity

  • Zhangfan Shen
  • Vencislav Popov
  • Anita B. Delahay
  • Lynne M. Reder
Article

Abstract

Do the processing and online manipulation of stimuli that are less familiar require more working memory (WM) resources? Is it more difficult to solve demanding problems when the symbols involved are less rather than more familiar? We explored these questions with a dual-task paradigm in which subjects had to solve algebra problems of different complexities while simultaneously holding novel symbol–digit associations in WM. The symbols were previously unknown Chinese characters, whose familiarity was manipulated by differential training frequency with a visual search task for nine hour-long sessions over 3 weeks. Subsequently, subjects solved equations that required one or two transformations. Before each trial, two different integers were assigned to two different Chinese characters of the same training frequency. Half of the time, those characters were present as variables in the equation and had to be substituted for the corresponding digits. After attempting to solve the equation, subjects had to recognize which two characters were shown immediately before that trial and to recall the integer associated with each. Solution accuracy and response times were better when the problems required one transformation only; variable substitution was not required; or the Chinese characters were high frequency. The effects of stimulus familiarity increased as the WM demands of the equation increased. Character–digit associations were also recalled less well with low-frequency characters. These results provide strong support that WM capacity depends not only on the number of chunks of information one is attempting to process but also on their strength or familiarity.

Keywords

Working memory Familiarity Knowledge Math Problem-solving 

Notes

Acknowledgements

L.R. developed the theory, idea for the study, and design. Z.S. provided the program to collect the data and collected the data. V.P. found a design bug and fixed it. Z.S. analyzed the data under V.P.’s supervision. Everyone contributed to the writing of this article, with V.P. making by far the largest contribution.

References

  1. Alloway, T. P. (2009). Working memory, but not IQ, predicts subsequent learning in children with learning difficulties. European Journal of Psychological Assessment, 25(2), 92–98.CrossRefGoogle Scholar
  2. Alloway, T. P., & Alloway, R. G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106(1), 20–29.PubMedCrossRefGoogle Scholar
  3. Anderson, J. R., Reder, L. M., & Lebiere, C. (1996). Working memory: Activation limitations on retrieval. Cognitive Psychology, 30(3), 221–256.PubMedCrossRefGoogle Scholar
  4. Appelman, I. B., & Mayzner, M. S. (1981). The letter-frequency effect and the generality of familiarity effects on perception. Perception & Psychophysics, 30(5), 436–446.CrossRefGoogle Scholar
  5. Au, J., Sheehan, E., Tsai, N., Duncan, G. J., Buschkuehl, M., & Jaeggi, S. M. (2015). Improving fluid intelligence with training on working memory: A meta-analysis. Psychonomic Bulletin & Review, 22(2), 366–377.CrossRefGoogle Scholar
  6. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.CrossRefGoogle Scholar
  7. Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559.PubMedCrossRefGoogle Scholar
  8. Baddeley, A. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63(1), 1–29.PubMedCrossRefGoogle Scholar
  9. Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General, 133(1), 83.CrossRefGoogle Scholar
  10. Beilock, S. L., Kulp, C. A., Holt, L. E., & Carr, T. H. (2004). More on the fragility of performance: Choking under pressure in mathematical problem solving. Journal of Experimental Psychology: General, 133(4), 584–600.CrossRefGoogle Scholar
  11. Blalock, L. D. (2015). Stimulus familiarity improves consolidation of visual working memory representations. Attention, Perception, & Psychophysics, 77(4), 1143–1158.CrossRefGoogle Scholar
  12. Blumenfeld, R. S., Parks, C. M., Yonelinas, A. P., & Ranganath, C. (2010). Putting the pieces together: The role of dorsolateral prefrontal cortex in relational memory encoding. Journal of Cognitive Neuroscience, 23(1), 257–265.CrossRefGoogle Scholar
  13. Blumenfeld, R. S., & Ranganath, C. (2006). Dorsolateral prefrontal cortex promotes long-term memory formation through its role in working memory organization. Journal of Neuroscience, 26(3), 916–925.PubMedCrossRefGoogle Scholar
  14. Blumenfeld, R. S., & Ranganath, C. (2007). Prefrontal cortex and long-term memory encoding: An integrative review of findings from neuropsychology and neuroimaging. The Neuroscientist, 13(3), 280–291.PubMedCrossRefGoogle Scholar
  15. Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33(3), 205–228.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Camos, V., Lagner, P., & Barrouillet, P. (2009). Two maintenance mechanisms of verbal information in working memory. Journal of Memory and Language, 61(3), 457–469.CrossRefGoogle Scholar
  17. Carroll, J. B., & White, M. N. (1973). Word frequency and age of acquisition as determiners of picture-naming latency. The Quarterly Journal of Experimental Psychology, 25(1), 85–95.CrossRefGoogle Scholar
  18. Chen, D., Eng, H. Y., & Jiang, Y. (2006). Visual working memory for trained and novel polygons. Visual Cognition, 14(1), 37–54.CrossRefGoogle Scholar
  19. Clark, R. C., Nguyen, F., & Sweller, J. (2011). Efficiency in learning: Evidence-based guidelines to manage cognitive load. New York: John Wiley & Sons.Google Scholar
  20. Clark, S. E. (1992). Word frequency effects in associative and item recognition. Memory & Cognition, 20(3), 231–243.CrossRefGoogle Scholar
  21. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. The Behavioral and Brain Sciences, 24(1), 87–114; discussion 114–185.PubMedCrossRefGoogle Scholar
  22. Cowan, N., Ricker, T. J., Clark, K. M., Hinrichs, G. A., & Glass, B. A. (2015). Knowledge cannot explain the developmental growth of working memory capacity. Developmental Science, 18(1), 132–145.PubMedCrossRefGoogle Scholar
  23. Daily, L.Z., Lovett, M.C., & Reder, L.M. (2001). Modeling individual differences in working memory performance: A source activation account. Cognitive Science, 25, 315–353.Google Scholar
  24. Dehn, M. J. (2011). Working memory and academic learning: Assessment and intervention. New York: John Wiley & Sons.Google Scholar
  25. Dewhurst, S. A., Hitch, G. J., & Barry, C. (1998). Separate effects of word frequency and age of acquisition in recognition and recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 24(2), 284.Google Scholar
  26. Diana, R. A., & Reder, L. M. (2006). The low-frequency encoding disadvantage: Word frequency affects processing demands. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(4), 805.PubMedPubMedCentralGoogle Scholar
  27. Donkin, C., Nosofsky, R. M., Gold, J. M., & Shiffrin, R. M. (2013). Discrete-slots models of visual working-memory response times. Psychological Review, 120(4), 873–902.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32(1/2), 33–58.CrossRefGoogle Scholar
  29. Giofrè, D., Mammarella, I. C., & Cornoldi, C. (2013). The structure of working memory and how it relates to intelligence in children. Intelligence, 41(5), 396–406.CrossRefGoogle Scholar
  30. Gobet, F. (2005). Chunking models of expertise: Implications for education. Applied Cognitive Psychology, 19(2), 183–204.CrossRefGoogle Scholar
  31. Gobet, F., & Clarkson, G. (2004). Chunks in expert memory: Evidence for the magical number four . . . or is it two? Memory (Hove, England), 12(6), 732–747.CrossRefGoogle Scholar
  32. Gobet, F., Lane, P. C., Croker, S., Cheng, P. C., Jones, G., Oliver, I., & Pine, J. M. (2001). Chunking mechanisms in human learning. Trends in Cognitive Sciences, 5(6), 236–243.PubMedCrossRefGoogle Scholar
  33. Grainger, J. (1990). Word frequency and neighborhood frequency effects in lexical decision and naming. Journal of Memory and Language, 29(2), 228–244.CrossRefGoogle Scholar
  34. Hambrick, D. Z., Oswald, F. L., Darowski, E. S., Rench, T. A., & Brou, R. (2010). Predictors of multitasking performance in a synthetic work paradigm. Applied Cognitive Psychology, 24(8), 1149–1167.CrossRefGoogle Scholar
  35. Hicks, K. L., Harrison, T. L., & Engle, R. W. (2015). Wonderlic, working memory capacity, and fluid intelligence. Intelligence, 50, 186–195.CrossRefGoogle Scholar
  36. Hitch, G. J. (1978). The role of short-term working memory in mental arithmetic. Cognitive Psychology, 10(3), 302–323.CrossRefGoogle Scholar
  37. Hitch, G. J., & McAuley, E. (1991). Working memory in children with specific arithmetical learning difficulties. British Journal of Psychology, 82(3), 375–386.PubMedCrossRefGoogle Scholar
  38. Hulme, C., Stuart, G., Brown, G. D., & Morin, C. (2003). High-and low-frequency words are recalled equally well in alternating lists: Evidence for associative effects in serial recall. Journal of Memory and Language, 49(4), 500–518.CrossRefGoogle Scholar
  39. Jackson, M. C., & Raymond, J. E. (2008). Familiarity enhances visual working memory for faces. Journal of Experimental Psychology. Human Perception and Performance, 34(3), 556–568.PubMedPubMedCentralCrossRefGoogle Scholar
  40. Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59(4), 434–446.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766.CrossRefGoogle Scholar
  42. Lovett, M.C., Reder, L.M., & Lebiere, C. (1999). Modeling Working Memory in a Unified Architecture: An ACT-R Perspective. In Miyake, A. & Shah, P. (Eds.), Models of Working Memory: Mechanisms of Active Maintenance and Executive Control (pp. 135–182). Cambridge: Cambridge University Press.Google Scholar
  43. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281.PubMedCrossRefGoogle Scholar
  44. MacLeod, C. M., & Kampe, K. E. (1996). Word frequency effects on recall, recognition, and word fragment completion tests. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(1), 132.PubMedGoogle Scholar
  45. Malmberg, K. J., & Murnane, K. (2002). List composition and the word-frequency effect for recognition memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(4), 616–630.PubMedGoogle Scholar
  46. Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). New York: Cambridge University Press.CrossRefGoogle Scholar
  47. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.PubMedCrossRefGoogle Scholar
  48. Oberauer, K., Farrell, S., Jarrold, C., & Lewandowsky, S. (2016). What limits working memory capacity? Psychological Bulletin, 142(7), 758–799.PubMedCrossRefGoogle Scholar
  49. Oberauer, K., Lewandowsky, S., Farrell, S., Jarrold, C., & Greaves, M. (2012). Modeling working memory: An interference model of complex span. Psychonomic Bulletin & Review, 19(5), 779–819.CrossRefGoogle Scholar
  50. Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59.PubMedCrossRefGoogle Scholar
  51. Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing instruction and study to improve student learning (IES Practice Guide, NCER 2007–2004). Washington, DC: National Center for Education Research.Google Scholar
  52. Passolunghi, M. C., & Siegel, L. S. (2001). Short-term memory, working memory, and inhibitory control in children with difficulties in arithmetic problem solving. Journal of Experimental Child Psychology, 80(1), 44–57.PubMedCrossRefGoogle Scholar
  53. Peterson, D. J., & Naveh-Benjamin, M. (2017). The role of attention in item-item binding in visual working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication.  https://doi.org/10.1037/xlm0000386
  54. Ratcliff, R., Clark, S. E., & Shiffrin, R. M. (1990). List-strength effect: I. Data and discussion. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(2), 163.PubMedGoogle Scholar
  55. Reder, L. M., Liu, X. L., Keinath, A., & Popov, V. (2016). Building knowledge requires bricks, not sand: The critical role of familiar constituents in learning. Psychonomic Bulletin & Review, 23(1), 271–277.CrossRefGoogle Scholar
  56. Reder, L. M., Nhouyvanisvong, A., Schunn, C. D., Ayers, M. S., Angstadt, P., & Hiraki, K. (2000). A mechanistic account of the mirror effect for word frequency: A computational model of remember–know judgments in a continuous recognition paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(2), 294.PubMedGoogle Scholar
  57. Reder, L. M., Paynter, C., Diana, R. A., Ngiam, J., & Dickison, D. (2007). Experience is a double-edged sword: A computational model of the encoding/retrieval trade-off with familiarity. In B. H. Ross (Ed.), Psychology of learning and motivation (Vol. 48, pp. 271–312). New York: Elsevier.Google Scholar
  58. Siedenburg, K., & McAdams, S. (2017). The role of long-term familiarity and attentional maintenance in short-term memory for timbre. Memory, 25(4), 550–564.PubMedGoogle Scholar
  59. Simon, H. A. (1974). How big is a chunk? Science, 183(4124), 482–488.PubMedCrossRefGoogle Scholar
  60. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.CrossRefGoogle Scholar
  61. Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid intelligence: Capacity, attention control, and secondary memory retrieval. Cognitive Psychology, 71, 1–26.PubMedPubMedCentralCrossRefGoogle Scholar
  62. Van den Berg, R., Awh, E., & Ma, W. J. (2014). Factorial comparison of working memory models. Psychological Review, 121(1), 124.PubMedPubMedCentralCrossRefGoogle Scholar
  63. van Geldorp, B, Parra, M. A., & Kessels, R. P. C. (2015). Cognitive and neuropsychological underpinnings of relational and conjunctive working memory binding across age. Memory, 23(8), 1112–1122.PubMedCrossRefGoogle Scholar
  64. Wagner, A. D. (1999). Working memory contributions to human learning and remembering. Neuron, 22(1), 19–22.PubMedCrossRefGoogle Scholar
  65. Wiley, J., & Jarosz, A. F. (2012). Working memory capacity, attentional focus, and problem solving. Current Directions in Psychological Science, 21(4), 258–262.CrossRefGoogle Scholar
  66. Xie, W., & Zhang, W. (2017a). Familiarity increases the number of remembered Pokémon in visual short-term memory. Memory & Cognition, 45(4), 677–689.CrossRefGoogle Scholar
  67. Xie, W., & Zhang, W. (2017b). Familiarity speeds up visual short-term memory consolidation. Journal of Experimental Psychology: Human Perception and Performance. Advance online publication.  https://doi.org/10.1037/xhp0000355
  68. Yang, J., McCandliss, B. D., Shu, H., & Zevin, J. D. (2009). Simulating language-specific and language-general effects in a statistical learning model of Chinese reading. Journal of Memory and Language, 61(2), 238–257.PubMedPubMedCentralCrossRefGoogle Scholar
  69. Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233–235.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  1. 1.School of Mechanical EngineeringSoutheast UniversityNanjingChina
  2. 2.Department of PsychologyCarnegie Mellon UniversityPittsburghUSA
  3. 3.Center for the Neural Basis of CognitionPittsburghUSA

Personalised recommendations