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Item repetition and retrieval processes in cued recall: Analysis of recall-latency distributions

  • Yoonhee JangEmail author
  • Heungchul Lee
Article
  • 7 Downloads

Abstract

The SAM (search of associative memory) model provides a unified account of accuracy effects, assuming that retrieval is a cue-dependent two-stage process of sampling and recovery, which depends on the strength of items relative to all others and on that item associated with the sampling trace, respectively. On the other hand, the relative strength model uniquely provides latency predictions, assuming that recall latency is determined solely by relative strength (similar to the sampling rule in SAM): Latency should remain unchanged for strong and weak items in pure lists, but will be shorter for strong items than for weak items in mixed lists. To test the predictions, the present study examined accuracy and latency distributions, which were fit with the ex-Gaussian, using item repetition as a means of strengthening. Massed versus spaced repetitions were used where repetitions were either cue–target pairs or cue alone. When repetitions were spaced in mixed lists, accuracy and latency both increased with cue–target repetitions, relative to cue-only repetitions, and slow recall for cue–target repetitions was due to initially nonretrievable items. However, even after successful recall on a pretest, cue–target repetitions led to an increase in latency in pure lists. These findings are difficult to reconcile with relative-strength explanations of latency. They indeed suggest that (1) separate traces are created for each repetition, (2) memory traces are updated if the item is retrieved (otherwise, new traces are stored), and (3) recovery plays a role in latency, which are discussed with the distinction between sampling and recovery of SAM.

Keywords

Item repetition Recall latency Sampling and recovery 

Notes

References

  1. Atkinson, R. C., & Shiffrin, R. M., (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). London, UK: Academic Press.  https://doi.org/10.1016/S0079-7421(08)60422-3 Google Scholar
  2. Bousfield, W. A., Sedgewick, C. H. W., & Cohen, B. H. (1954). Certain temporal characteristics of the recall of verbal associates. American Journal of Psychology, 67, 111–118.  https://doi.org/10.2307/1418075 CrossRefGoogle Scholar
  3. Delaney, P. F., Verkoeijen, P. P. J. L., & Spirgel, A. (2010). Spacing and testing effects: A deeply critical, lengthy, and at times discursive review of the literature. In B. H. Ross (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 53, pp. 63–147). San Diego, CA: Elsevier Academic Press.  https://doi.org/10.1016/S0079-7421(10)53003-2 CrossRefGoogle Scholar
  4. Diller, D. E., Nobel, P. A., & Shiffrin, R. M. (2001). An ARC-REM model for accuracy and response time in recognition and recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 414–435.  https://doi.org/10.1037//0278-7393.27.2.414 Google Scholar
  5. Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, NY: Chapman & Hall.CrossRefGoogle Scholar
  6. Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Review, 91, 1–67.  https://doi.org/10.1037/0033-295X.91.1.1 CrossRefGoogle Scholar
  7. Heathcote, A., Popiel, S. J., & Mewhort, D. J. K. (1991). Analysis of response time distributions: An example using the Stroop task. Psychological Bulletin, 109, 340–347.  https://doi.org/10.1037/0033-2909.109.2.340 CrossRefGoogle Scholar
  8. Hockley, W. E. (1984). Analysis of response time distributions in the study of cognitive processes. Journal of Experimental Psychology: Learning, Memory, & Cognition, 10, 598–615.  https://doi.org/10.1037/0278-7393.10.4.598 Google Scholar
  9. Hopper, W. J., & Huber, D. E. (2018a). Learning to recall: Examining recall latencies to test an intra-item learning theory of testing effects. Journal of Memory and Language, 102, 1–15.  https://doi.org/10.1016/j.jml.2018.04.005 CrossRefGoogle Scholar
  10. Hopper, W. J., & Huber, D. E. (2018b). Testing the PCR model of recall: Retrieval practice produces faster recall success but also faster recall failure. Paper presented at the 59th annual meeting of the Psychonomic Society, New Orleans, LA.Google Scholar
  11. Huber, D. E., Tomlinson, T. D., Jang, Y., & Hopper, W. J. (2015). The search of associative memory and recovery interference (SAM-RI) memory model and its application to retrieval practice paradigms. In J. G. W. Raaijmakers, A. H. Criss, R. L. Goldstone, R. M. Nosofsky, & M. Steyvers (Eds.), Cognitive modeling in perception and memory: A festschrift for Richard M. Shiffrin (pp. 81–98). New York, NY: Psychology Press.Google Scholar
  12. Jang, Y., Lee, H., & Huber, D. E. (in press). How many dimensions underlie judgments of learning and recall redux: Consideration of recall latency reveals a previously hidden nonmonotonicity. Journal of Mathematical Psychology.Google Scholar
  13. Jang, Y., Wixted, J. T., & Huber, D. E. (2011). The diagnosticity of individual data for model selection: Comparing signal-detection models of recognition memory. Psychonomic Bulletin & Review, 18, 751–757.  https://doi.org/10.3758/s13423-011-0096-7 CrossRefGoogle Scholar
  14. Jang, Y., Wixted, J. T., Pecher, D., Zeelenberg, R., & Huber, D. E. (2012). Decomposing the interaction between retention interval and study/test practice: The role of retrievability. Quarterly Journal of Experimental Psychology, 65, 962–975.  https://doi.org/10.1080/17470218.2011.638079 CrossRefGoogle Scholar
  15. Kornell, N., Bjork, R. A., & Garcia, M. A. (2011). Why tests appear to prevent forgetting: A distribution-based bifurcation model. Journal of Memory and Language, 65, 85–97.  https://doi.org/10.1016/j.jml.2011.04.002 CrossRefGoogle Scholar
  16. Kucera, H., & Francis, W. H. (1967). Computational analysis of present-day American English. Providence, RI: Brown University.Google Scholar
  17. Lacouture, Y., & Cousineau, D. (2008). How to use MATLAB to fit the ex-Gaussian and other probability functions to a distribution of response times. Tutorials in Quantitative Methods for Psychology, 4, 35–45.  https://doi.org/10.20982/tqmp.04.1.p035 CrossRefGoogle Scholar
  18. Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Thousand Oaks, CA: SAGE.  https://doi.org/10.4135/9781483349428
  19. MacLeod, C. M., & Nelson, T. O. (1984). Response latency and response accuracy as measures of memory. Acta Psychologica, 57, 215–235.  https://doi.org/10.1016/0001-6918(84)90032-5 CrossRefGoogle Scholar
  20. Malmberg, K. J., & Shiffrin, R. M. (2005). The “one-shot” hypothesis for context storage. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 322–336.  https://doi.org/10.1037/0278-7393.31.2.322 Google Scholar
  21. Murnane, K., & Shiffrin, R. M. (1991). Word repetitions in sentence recognition. Memory & Cognition, 19, 119–130.  https://doi.org/10.3758/BF03197109 CrossRefGoogle Scholar
  22. Nobel, P. A., & Shiffrin, R. M. (2001). Retrieval processes in recognition and cued recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 384–413.  https://doi.org/10.1037/0278-7393.27.2.384 Google Scholar
  23. Raaijmakers, J. G. W. (2003). Spacing and repetition effects in human memory: Application of the SAM model. Cognitive Science, 27, 431–452.  https://doi.org/10.1016/S0364-0213(03)00007-7 CrossRefGoogle Scholar
  24. Raaijmakers, J. G. W., & Shiffrin, R. M. (1980). SAM: A theory of probabilistic search of associative memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 14, pp. 207–262). New York, NY: Academic Press.Google Scholar
  25. Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93–134.  https://doi.org/10.1037/0033-295X.88.2.93 CrossRefGoogle Scholar
  26. Ratcliff, R., Clark, S., & Shiffrin, R. M. (1990). The list-strength effect: I. data and discussion. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 163–178.  https://doi.org/10.1037/0278-7393.16.2.163 Google Scholar
  27. Ratcliff, R., & Murdock, B. B., Jr. (1976). Retrieval processes in recognition memory. Psychological Review, 83, 190–214.  https://doi.org/10.1037/0033-295X.83.3.190 CrossRefGoogle Scholar
  28. Roediger, H. L., III, & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1, 181–210.  https://doi.org/10.1111/j.1745-6916.2006.00012.x CrossRefGoogle Scholar
  29. Rohrer, D. (1996). On the relative and absolute strength of a memory trace. Memory & Cognition, 24, 188–201.  https://doi.org/10.3758/BF03200880 CrossRefGoogle Scholar
  30. Rohrer, D., & Wixted, J. T. (1994). An analysis of latency and interresponse time in free recall. Memory & Cognition, 22, 511–524.  https://doi.org/10.3758/BF03198390 CrossRefGoogle Scholar
  31. Rotello, C. M., & Zeng, M. (2008). Analysis of RT distributions in the remember–know paradigm. Psychonomic Bulletin & Review, 15, 825–832.  https://doi.org/10.3758/PBR.15.4.825 CrossRefGoogle Scholar
  32. Rouder, J. N., Lu, J., Speckman, P., Sun, D., & Jiang, Y. (2005). A hierarchical model for estimating response time distributions. Psychonomic Bulletin & Review, 12, 195–223.  https://doi.org/10.3758/BF03257252 CrossRefGoogle Scholar
  33. Shiffrin, R. M. (1970). Memory search. In D. A. Norman (Ed.), Models of human memory (pp. 375–447). New York, NY: Academic Press.CrossRefGoogle Scholar
  34. Shiffrin, R. M. (2003). Modeling memory and perception. Cognitive Science, 27, 341–378.  https://doi.org/10.1016/S0364-0213(03)00027-2 CrossRefGoogle Scholar
  35. Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM—Retrieving effectively from memory. Psychonomic Bulletin & Review, 4, 145–166.  https://doi.org/10.3758/BF03209391 CrossRefGoogle Scholar
  36. Shiffrin, R. M., Ratcliff, R., & Clark, S. (1990). The list-strength effect: II. Theoretical mechanisms. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 179–195.  https://doi.org/10.1037/0278-7393.16.2.179 Google Scholar
  37. Unsworth, N. (2015). The influence of encoding manipulations on the dynamics of free recall. Memory & Cognition, 43, 60–69.  https://doi.org/10.3758/s13421-014-0447-5 CrossRefGoogle Scholar
  38. van den Broek, G. S. E., Segers, E., Takashima, A., & Verhoeven, L. (2014). Do testing effects change over time? Insights from immediate and delayed retrieval speed. Memory, 22, 803-812.  https://doi.org/10.1080/09658211.2013.831455 CrossRefGoogle Scholar
  39. Wilson, J. H., & Criss, A. H. (2017). The list strength effect in cued recall. Journal of Memory and Language, 95, 78–88.  https://doi.org/10.1016/j.jml.2017.01.006 CrossRefGoogle Scholar
  40. Wixted, J. T., Ghadisha, H., & Vera, R. (1997). Recall latency following pure- and mixed-strength lists: A direct test of the relative strength model of free recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23, 523–538.  https://doi.org/10.1037/0278-7393.23.3.523 Google Scholar
  41. Zandbelt, B. (2014). exgauss: a MATLAB toolbox for fitting the ex-Gaussian distribution to response time data. Computer software.  https://doi.org/10.6084/m9.figshare.971318

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of MontanaMissoulaUSA
  2. 2.Net Intelligence & ResearchSeoulSouth Korea

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