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Monitoring the ebb and flow of attention: Does controlling the onset of stimuli during encoding enhance memory?

  • Trisha N. PatelEmail author
  • Mark Steyvers
  • Aaron S. Benjamin
Article

Abstract

Central to the operation of the Atkinson and Shiffrin’s (Psychology of learning and motivation, 2, 89-195, 1968) model of human memory are a variety of control processes that manage information flow. Research on metacognition reveals that provision of control in laboratory learning tasks is generally beneficial to memory. In this paper, we investigate the novel domain of attentional fluctuations during study. If learners are able to monitor attention, then control over the onset of stimuli should also improve performance. Across four experiments, we found no evidence that control over the onset of stimuli enhances learning. This result stands in notable contrast to the fact that control over stimulus offset does enhance memory (Experiment 1; Tullis & Benjamin, Journal of memory and language, 64 (2), 109-118, 2011). This null finding was replicated across laboratory and online samples of subjects, and with both words and faces as study material. Taken together, the evidence suggests that people either cannot monitor fluctuations in attention effectively or cannot precisely time their study to those fluctuations.

Keywords

memory metamemory attention recognition 

Notes

References

  1. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In Psychology of learning and motivation (Vol. 2, pp. 89–195). Academic Press.Google Scholar
  2. Benjamin, A. S. (2007). Memory is more than just remembering: Strategic control of encoding, accessing memory, and making decisions. In A. S. Benjamin & B. H. Ross (Eds.), The Psychology of Learning and Motivation: Skill and Strategy in Memory Use (Vol. 48; 175-223). London: Academic PressCrossRefGoogle Scholar
  3. deBettencourt MT, Cohen JD, Lee RF, Norman KA, Turk-Browne NB (2015) Closed-loop training of attention with real-time brain imaging. Nature Neuroscience. 18(3): 470–475CrossRefPubMedGoogle Scholar
  4. deBettencourt MT, Norman KA, Turk-Browne NB (2018) Forgetting from lapses of sustained attention. Psychonomic Bulletin & Review. 25:605–611CrossRefGoogle Scholar
  5. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual review of neuroscience, 18(1), 193–222.CrossRefPubMedGoogle Scholar
  6. Dixon, P., & Bortolussi, M. (2013). Construction, integration, and mind wandering in reading. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 67(1), 1–10.CrossRefPubMedGoogle Scholar
  7. Dunlosky, J., & Thiede, K. W. (1998). What makes people study more? An evaluation of factors that affect self-paced study. Acta psychologica, 98(1), 37–56.CrossRefPubMedGoogle Scholar
  8. Farley, J., Risko, E., & Kingstone, A. (2013). Everyday attention and lecture retention: the effects of time, fidgeting, and mind wandering. Frontiers in psychology, 4, 1–9.CrossRefGoogle Scholar
  9. Feng, S., D’Mello, S., & Graesser, A. C. (2013). Mind wandering while reading easy and difficult texts. Psychonomic bulletin & review, 20(3), 586–592.CrossRefGoogle Scholar
  10. Fiechter, J. L., Benjamin, A. S., & Unsworth, N. (2016). The metacognitive foundations of effective remembering. In J. Dunlosky & S. Tauber (Eds.), The Oxford Handbook of Metamemory (pp. 307–324). New York, NY: Oxford University Press.Google Scholar
  11. Finley, J. R., & Benjamin, A. S. (2012). Adaptive and qualitative changes in encoding strategy with experience: Evidence from the test-expectancy paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(3), 632.PubMedGoogle Scholar
  12. Finley, J. R., Tullis, J. G., & Benjamin, A. S. (2010). Metacognitive control of learning and remembering. In M. S. Khine & I. Saleh (Eds.), New science of learning: cognition, computers and collaboration in education (pp. 108–132). New York: Springer.Google Scholar
  13. Franklin, M. S., Smallwood, J., & Schooler, J. W. (2011). Catching the mind in flight: Using behavioral indices to detect mindless reading in real time. Psychonomic Bulletin & Review, 18(5), 992–997.CrossRefGoogle Scholar
  14. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Oxford: John Wiley.Google Scholar
  15. Jackson, J. D., & Balota, D. A. (2012). Mind-wandering in younger and older adults: Converging evidence from the sustained attention to response task and reading for comprehension. Psychology and Aging, 27(1), 106–119.CrossRefPubMedGoogle Scholar
  16. Jeffreys, H. (1961). Theory of Probability. London: Oxford University Press.Google Scholar
  17. Kane, M. J., & McVay, J. C. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of Experimental Psychology: General141(2), 302.Google Scholar
  18. Koriat, A., Ma'ayan, H., & Nussinson, R. (2006). The intricate relationships between monitoring and control in metacognition: Lessons for the cause-and-effect relation between subjective experience and behavior. Journal of Experimental Psychology: General, 135(1), 36–69.CrossRefGoogle Scholar
  19. Kornell, N., & Finn, B. (2016). Self-regulated learning: An overview of theory and data. The Oxford Handbook of Metamemory, 325–340.Google Scholar
  20. Kornell, N., & Metcalfe, J. (2006). Study efficacy and the region of proximal learning framework. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 609–622.PubMedGoogle Scholar
  21. Le Ny, J. F., Denhiere, G., & Le Taillanter, D. (1972). Regulation of study-time and interstimulus similarity in self-paced learning conditions. Acta Psychologica, 36(4), 280–289.CrossRefGoogle Scholar
  22. Lindley, D. V. (1957). A statistical paradox. Biometrika44(1/2), 187–192.Google Scholar
  23. Markant, D., DuBrow, S., Davachi, L., & Gureckis, T. M. (2014). Deconstructing the effect of self-directed study on episodic memory. Memory & cognition, 42(8), 1211–1224.CrossRefGoogle Scholar
  24. Markant, D. B., & Gureckis, T. M. (2014). Is it better to select or to receive? Learning via active and passive hypothesis testing. Journal of Experimental Psychology: General, 143(1), 94–122.CrossRefGoogle Scholar
  25. Markant, D. B., Ruggeri, A., Gureckis, T. M., & Xu, F. (2016). Enhanced memory as a common effect of active learning. Mind, Brain, and Education, 10(3), 142–152.CrossRefGoogle Scholar
  26. Mazzoni, G., & Cornoldi, C. (1993). Strategies in study time allocation: Why is study time sometimes not effective?. Journal of Experimental Psychology: General, 122(1), 47–60.CrossRefGoogle Scholar
  27. Metcalfe, J., & Kornell, N. (2003). The dynamics of learning and allocation of study time to a region of proximal learning. Journal of Experimental Psychology: General, 132(4), 530–542.CrossRefGoogle Scholar
  28. Middlebrooks, C. D., & Castel, A. D. (2018). Self-regulated learning of important information under sequential and simultaneous encoding conditions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 44(5), 779–792PubMedGoogle Scholar
  29. Minear, M., & Park, D. C. (2004). A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments & Computers, 36(4), 630–633.Google Scholar
  30. Murty, V. P., DuBrow, S., & Davachi, L. (2015). The simple act of choosing influences declarative memory. Journal of Neuroscience, 35(16), 6255–6264.CrossRefPubMedGoogle Scholar
  31. Nelson, T. O., & Leonesio, R. J. (1988). Allocation of self-paced study time and the “labor-in-vain effect”. Journal of experimental psychology: Learning, Memory, and Cognition, 14(4), 676.PubMedGoogle Scholar
  32. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual review of neuroscience, 13(1), 25–42.CrossRefPubMedGoogle Scholar
  33. Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). Oops!': performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35(6), 747–758.CrossRefPubMedGoogle Scholar
  34. Rouder, J. N. (2014). Optional stopping: No problem for Bayesians. Psychonomic Bulletin & Review, 21(2), 301–308.Google Scholar
  35. Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374.Google Scholar
  36. Smallwood, J., McSpadden, M., & Schooler, J. W. (2008). When attention matters: The curious incident of the wandering mind. Memory & Cognition, 36(6), 1144–1150.CrossRefGoogle Scholar
  37. Son, L. K. & Metcalfe, J. (2000). Metacognitive and control strategies in study-time allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 204–221.PubMedGoogle Scholar
  38. Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), 6313–6317.CrossRefGoogle Scholar
  39. Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive psychology, 12(1), 97–136.CrossRefPubMedGoogle Scholar
  40. Tullis, J. G., & Benjamin, A. S. (2011). On the effectiveness of self-paced learning. Journal of memory and language, 64(2), 109–118.CrossRefPubMedGoogle Scholar
  41. Tullis, J. G., Benjamin, A. S., & Liu, X. (2014). Self-pacing study of faces of different races: Metacognitive control over study does not eliminate the cross-race recognition effect. Memory & cognition, 42(6), 863–875.CrossRefGoogle Scholar
  42. Unsworth, N., & McMillan, B. D. (2013). Mind wandering and reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(3), 832–842.PubMedGoogle Scholar
  43. Voss, J. L., Gonsalves, B. D., Federmeier, K. D., Tranel, D., & Cohen, N. J. (2011). Hippocampal brain-network coordination during volitional exploratory behavior enhances learning. Nature neuroscience, 14(1), 115–120.CrossRefPubMedGoogle Scholar
  44. Wilson, M. (1988). MRC psycholinguistic database: Machine-usable dictionary, version 2.00. Behavior Research Methods, Instruments, & Computers, 20(1), 6–10.Google Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  • Trisha N. Patel
    • 1
    Email author
  • Mark Steyvers
    • 2
  • Aaron S. Benjamin
    • 1
  1. 1.Department of PsychologyUniversity of Illinois at Urbana-ChampaignUrbana-ChampaignUSA
  2. 2.Department of PsychologyUniversity of CaliforniaIrvineUSA

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