Precrastination and individual differences in working memory capacity

Abstract

When ordering tasks, people tend to first perform the task that can be started or completed sooner (precrastination) even if it requires more physical effort. Evidence from transport tasks suggests that precrastination can reduce cognitive effort and will likely not occur if it increases cognitive effort. However, some individuals precrastinate even when it increases cognitive effort. We examined whether individual differences in working memory capacity (WMC) influence this suboptimal choice. Participants retrieved two cups of water along a corridor, in the order of their choosing. We measured the frequency of choosing the close cup first (precrastination) while varying water levels in each cup (attention demand) located at different distances. Results showed that the tendency to select the far cup first (avoid precrastination) increases when the close cup is full (high attention demand) vs. not full (low attention demand). Post-hoc results showed high (vs. low) WMC individuals more frequently bypass decisions with relatively higher costs of cognitive effort, avoiding precrastination when the attentional demand of carrying the close (vs. far) cup is relatively high (close-cup full and far-cup half full), but not when it is relatively low (far-cup full). However, there was no evidence that WMC could explain why some individuals always precrastinated, at costs of cognitive effort. Instead, individuals who always precrastinated reported automatic behavior, and those who avoided precrastinating reported decisions of efficiency. Learning, the relationship between precrastination and tendencies to enjoy/engage in thinking or procrastinate, and evidence that precrastination required more cognitive effort in our task, are discussed.

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Notes

  1. 1.

    See Fournier, Stubblefield, Dyre & Rosenbaum, 2018.

  2. 2.

    Almost never means participants precrastinated (i.e., chose the close cup first) no more than 1 out of 12 trials.

  3. 3.

    The statistical conclusions were similar when each memory span task was separately included in the model.

  4. 4.

    It is also possible that participants made choices that would minimize time to complete the task, and those with higher working memory capacities were more sensitive to conserving time. It is difficult to separate time on task and attention demand, as economical strategies in terms of time (e.g., Gray, Sims, Fu & Schoelles, 2006) would affect the duration of attention deployed in this task—and hence it does not pose a problem for our hypothesis. Importantly, our participants appeared very concerned about not spilling, and travel time was influenced by this factor with participants walking very slowly when transporting the cup(s). Also, anyone who spilled even a little, stopped and steadied themselves, and also appeared distressed and often vocalized their distress. Finally, subjective reports showed that 22 participants explicitly stated they made their first cup choice to minimize spilling while only 6 participants mentioned that time or distance of the cups played a role in their first-cup choices. Thus, we believe choices appear to be driven to minimize relative carrying time of the more attention-demanding, full cup—and those with high working memory capacities may be more proactively engaged in this strategy.

References

  1. Ballard, D. H., Hayhoe, M. M., & Pelz, J. B. (1995). Memory representations in natural tasks. Journal of Cognitive Neuroscience,7(1), 66–80. https://doi.org/10.1162/jocn.1995.7.1.66.

    Article  PubMed  Google Scholar 

  2. Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. N. (1997). Deictic codes for the embodiment of cognition. Behavioral and Brain Sciences,20(4), 723–742. https://doi.org/10.1017/S0140525X97001611.

    Article  PubMed  Google Scholar 

  3. Bates, D., Achler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed effects models using lme4. Journal of Statistical Software,67(1), 1–48. https://doi.org/10.18637/jss.v067.i01.

    Article  Google Scholar 

  4. Behmer, L. P., Jr., & Fournier, L. R. (2014). Working memory modulates neural efficiency over motor components during a novel action planning task: An EEG study. Behavioral Brain Research,260, 1–7. https://doi.org/10.1016/j.bbr.2013.11.031.

    Article  Google Scholar 

  5. Blinch, J., & DeWinne, C. R. (2019). Pre-crastination and procrastination effects occur in a reach-to-grasp task. Experimental Brain Research. https://doi.org/10.1007/s00221-019-05493-3. (Advance online publication).

    Article  PubMed  Google Scholar 

  6. Botvinick, M. M., Buxbaum, L. J., Bylsma, L. M., & Jax, S. A. (2009a). Toward an integrated account of object and action selection: A computational analysis and empirical findings from reaching-to-grasp and tool-use. Neuropsychologia,47(3), 671–683. https://doi.org/10.1016/j.neuropsychologia.2008.11.024.

    Article  PubMed  Google Scholar 

  7. Botvinick, M. M., Huffstetler, S., & McGuire, J. T. (2009b). Effort discounting in human nucleus accumbens. Cognitive, Affective, & Behavioral Neuroscience,9(1), 16–27. https://doi.org/10.3758/CABN.9.1.16.

    Article  Google Scholar 

  8. Botvinick, M. M., & Rosen, Z. B. (2008). Anticipation of cognitive demand during decision making. Psychological Research,73(6), 835–842. https://doi.org/10.1007/s00426-008-0197-8.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Braver, T. S. (2012). The variable nature of cognitive control: A dual-mechanisms framework. Trends in Cognitive Sciences,16(2), 106–113. https://doi.org/10.1016/j.tics.2011.12.010.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Braver, T. S., Gray, J. R., & Burgess, G. C. (2007). Explaining the many varieties of working memory variation: Dual mechanisms of cognitive control. In A. R. A. Conway, C. Jarrold, M. J. Kane, A. Miyake, & J. N. Towse (Eds.), Variation in working memory (pp. 76–106). Oxford: Oxford University Press.

    Google Scholar 

  11. Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment,48(3), 306–307. https://doi.org/10.1207/s15327752jpa4803_13.

    Article  PubMed  Google Scholar 

  12. Castiello, U. (1996). Grasping a fruit: Selection for action. Journal of Experimental Psychology: Human Perception and Performance,22(3), 582–603. https://doi.org/10.1037/0096-1523.22.3.582.

    Article  PubMed  Google Scholar 

  13. Christenfeld, N. (1995). Choices from identical situations. Psychological Science,6(1), 550–555. https://doi.org/10.1111/j.1467-9280.1995.tb00304.x.

    Article  Google Scholar 

  14. Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilheim, O., & Engle, R. W. (2005). Working memory span tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review,12(5), 769–786. https://doi.org/10.3758/BF03196772.

    Article  Google Scholar 

  15. Craighero, L., Fadiga, L., Umiltà, C. A., & Rizzolatti, G. (1996). Evidence for visuomotor priming effect. NeuroReport,8(1), 347–349. https://doi.org/10.1037/0096-1523.25.6.1673.

    Article  PubMed  Google Scholar 

  16. Droll, J. A., & Hayhoe, M. M. (2007). Trade-offs between gaze and working memory use. Journal of Experimental Psychology: Human Perception and Performance,33(6), 1352–1365. https://doi.org/10.1037/0096-1523.33.6.1352.

    Article  PubMed  Google Scholar 

  17. Dunn, T. L., Lutes, D. J., & Risko, E. F. (2016). Metacognitive evaluation in the avoidance of demand. Journal of Experimental Psychology: Human Perception and Performance,42, 1372–1388. https://doi.org/10.1037/xhp0000236.

    Article  PubMed  Google Scholar 

  18. Einstein, G. O., & McDaniel, M. A. (2005). Prospective memory: Multiple retrieval processes. Current Directions in Psychological Science,14(6), 286–290. https://doi.org/10.1111/j.0963-7214.2005.00382.x.

    Article  Google Scholar 

  19. Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science,11(19), 19–23. https://doi.org/10.1111/1467-8721.00160.

    Article  Google Scholar 

  20. Engle, R. W. (2010). Role of working-memory capacity in cognitive control. Current Anthropology,51(1), S17–S26. https://doi.org/10.1086/650572.

    Article  Google Scholar 

  21. Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. Ross (Ed.), The psychology of learning and motivation (Vol. 44, pp. 145–199). New York: Elsevier.

    Google Scholar 

  22. Feghhi, I., & Rosenbaum, D. A. (2020). Effort avoidance is not simply error avoidance. Psychological Research. https://doi.org/10.1007/s00426-020-01331-2. (in press).

    Article  PubMed  Google Scholar 

  23. Foster, J. L., Shipstead, Z., Harrison, T. L., Hicks, K. L., Redick, T. S., & Engle, R. W. (2015). Shortened complex span tasks can reliably measure working memory capacity. Memory and Cognition,43(2), 226–236. https://doi.org/10.3758/s13421-014-0461-7.

    Article  PubMed  Google Scholar 

  24. Fournier, L. R., Behmer, L. P., Jr., & Stubblefield, A. M. (2014). Interference due to shared features between action plans is influenced by working memory span. Psychonomic Bulletin & Review,21, 1524–1529. https://doi.org/10.3758/s13423-014-0627-0.

    Article  Google Scholar 

  25. Fournier, L. R., Coder, E., Kogan, C., Raghunath, N., Taddese, E. T., & Rosenbaum, D. A. (2019). Which task will we choose first? Precrastination and cognitive load in task ordering. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-018-1633-5.

    Article  Google Scholar 

  26. Fournier, L. R., Stubblefield, A., Dyre, B. P., & Rosenbaum, D. A. (2018). Starting or finishing sooner? Sequencing preferences in object transfer tasks. Psychological Research. https://doi.org/10.1007/s00426-018-1022-7.

    Article  PubMed  Google Scholar 

  27. Gray, W. D., Sims, C. R., Fu, W.-T., & Schoelles, M. J. (2006). The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review,113(3), 461–482. https://doi.org/10.1037/0033-295X.113.3.461.

    Article  PubMed  Google Scholar 

  28. Harrell Jr., F. E. (2018). RMS: Regression modeling strategies. R package version 5.1-2. https://CRAN.R-project.org/package=rms.

  29. Hull, C. L. (1943). Principles of behavior. New York: Appleton-Century.

    Google Scholar 

  30. Humphreys, G. W., & Riddoch, M. J. (2001). Detection by action: Neuropsychological evidence for action-defined templates in search. Nature Neuroscience,4(1), 84–88. https://doi.org/10.1038/82940.

    Article  PubMed  Google Scholar 

  31. Jax, S. A., & Buxbaum, L. J. (2010). Response interference between functional and structural actions linked to the same familiar object. Cognition,115(2), 350–355. https://doi.org/10.1016/j.cognition.2010.01.004.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Jax, S. A., & Rosenbaum, D. A. (2007). Hand path priming in manual obstacle avoidance: Evidence that the dorsal stream does not only control visually guided actions in real time. Journal of Experimental Psychology: Human Perception and Performance,33(2), 425–441. https://doi.org/10.1037/0096-1523.33.2.425.

    Article  PubMed  Google Scholar 

  33. Kane, M. J., Brown, L. H., McVay, J. C., Silvia, P. J., Myin-Germeys, I., & Kwapil, T. R. (2007). For whom the mind wanders, and when. Psychological Science,18(7), 614–621. https://doi.org/10.1111/j.1467-9280.2007.01948.x.

    Article  PubMed  Google Scholar 

  34. Kane, M. J., & Engle, R. W. (2000). Working-memory capacity, proactive interference, and divided attention: Limits on long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition,26(2), 336–358. https://doi.org/10.1037/0278-7393.26.2.336.

    Article  PubMed  Google Scholar 

  35. Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology: General,139(4), 665–682. https://doi.org/10.1037/a0020198.

    Article  Google Scholar 

  36. Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences,36(6), 661–679. https://doi.org/10.1017/S0140525X12003196.

    Article  PubMed  Google Scholar 

  37. Lay, C. H. (1986). At last, my research article on procrastination. Journal of Research in Personality,20(4), 474–495. https://doi.org/10.1016/0092-6566(86)90127-3.

    Article  Google Scholar 

  38. McGuire, W. J. (1969). The nature of attitudes and attitude change. In G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (Vol. 3, pp. 136–314). Boston, MA: Addison-Wesley

  39. McGuire, J. T., & Botvinick, M. M. (2010). Prefrontal cortex, cognitive control, and the registration of decision costs. Proceedings of the National Academy of Sciences of the United States of America,107(17), 7922–7926. https://doi.org/10.1073/pnas.0910662107.

    Article  PubMed  PubMed Central  Google Scholar 

  40. McNamara, D. S., & Scott, J. L. (2001). Working memory capacity and strategy use. Memory and Cognition,29(1), 10–17. https://doi.org/10.3758/BF03195736.

    Article  PubMed  Google Scholar 

  41. Pavese, A., & Buxbaum, L. J. (2002). Action matters: The role of action plans and object affordances in selection for action. Visual Cognition,9(4–5), 559–590. https://doi.org/10.1080/13506280143000584.

    Article  Google Scholar 

  42. Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition,14(3), 534–552. https://doi.org/10.1037/0278-7393.14.3.534.

    Article  Google Scholar 

  43. Qualtrics (2005). Version March, 2019. Provo, Utah, USA. https://www.qualtrics.com. Accessed Mar 2019

  44. Redick, T. S. (2014). Cognitive control in context: Working memory capacity and proactive control. Acta Psychologica,145, 1–9. https://doi.org/10.1016/j.actpsy.2013.10.010.

    Article  PubMed  Google Scholar 

  45. Rizopoulos, D. (2020). GLMMadaptive: Generalized linear mixed models using adaptive Gaussian quadrature. R package version 0.6-8. https://CRAN.R-project.org/package=GLMMadaptive.

  46. Rosch, E. (1999). Principles of categorization. In E. Margolis & S. Laurence (Eds.), Concepts: Core readings (pp. 189–206). Cambridge: MIT Press.

    Google Scholar 

  47. Rosenbaum, D. A., Fournier, L. R., Levy-Tzedek, S., McBride, D. M., Rosenthal, R., Sauerberger, K., VonderHaar, R. L., Wasserman, E. A., Zentall, T. R. (2019). Sooner rather than later: Precrastination rather than procrastination. Current Directions in Psychological Science,28(3), 229–233. https://doi.org/10.1177/0963721419833652.

    Article  Google Scholar 

  48. Rosenbaum, D. A., Gong, L., & Potts, C. A. (2014). Precrastination: Hastening sub-goal completion at the expense of extra physical effort. Psychological Science,25(7), 1487–1496. https://doi.org/10.1177/0956797614532657.

    Article  PubMed  Google Scholar 

  49. Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-reduction framework. Psychological Bulletin,134(2), 207–222. https://doi.org/10.1037/0033-2909.134.2.207.

    Article  PubMed  Google Scholar 

  50. R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

  51. Tipper, S. P., Paul, M., & Hayes, A. (2006). Vision-for action: The effects of object property discrimination and action state on affordance compatibility effects. Psychonomic Bulletin & Review,13, 493–498. https://doi.org/10.3758/BF03193875.

    Article  Google Scholar 

  52. Tucker, M., & Ellis, R. (1998). On the relations between seen objects and components of potential actions. Journal of Experimental Psychology: Human Perception and Performance,24(3), 830–846. https://doi.org/10.1037/0096-1523.24.3.830.

    Article  PubMed  Google Scholar 

  53. Turner, M. L., & Engle, R. W. (1989). Is working memory capacity task dependent? Journal of Memory and Language,28(2), 127–154. https://doi.org/10.1016/0749-596X(89)90040-5.

    Article  Google Scholar 

  54. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science,185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124.

    Article  PubMed  Google Scholar 

  55. VonderHaar, R. L., McBride, D. M., & Rosenbaum, D. A. (2019). Task order choices in cognitive and perceptual-motor tasks: The cognitive-load-reduction (CLEAR) hypothesis. Attention Perception & Psychophysics,81(7), 2517–2525. https://doi.org/10.3758/s13414-019-01754-z.

    Article  Google Scholar 

  56. Wasserman, E. A., & Brzykcy, S. J. (2015). Precrastination in the pigeon. Psychonomic Bulletin & Review,22(4), 1130–1134. https://doi.org/10.3758/s13423-014-0758-3.

    Article  Google Scholar 

  57. Zbrodoff, N. J. (1999). Effects of counting in alphabet arithmetic: Opportunistic stopping and priming of intermediate steps. Journal of Experimental Psychology: Learning, Memory, and Cognition,25(2), 299–317. https://doi.org/10.1037/0278-7393.25.2.299.

    Article  Google Scholar 

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Acknowledgements

We thank Washington State University undergraduates Hannah Jensen and Erin McMeekin for their help with data collection. We also thank Cody Mashburn for his help with the span tasks used in this study that came from Randall Engle’s laboratory. Correspondence should be directed to Lisa R. Fournier at lfournier@wsu.edu.

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Contributions

NR and LF contributed to the study conception and design. Material preparation and data collection were performed by NR. Data analysis was performed by CK and NR. Initial drafts and the final manuscript were written by NR, as part of her master’s thesis, and LF. All authors read and approved the final manuscript.

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Correspondence to Lisa R. Fournier.

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The behavioral data that support the findings of this study are available at https://github.com/ckogan/mto_wm/. All other data, except those that may compromise research participant privacy/consent, are available upon request from the corresponding author.

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The R-Studio code that was used to analyze the data of this study is available at https://github.com/ckogan/mto_wm/.

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Appendix

Appendix

Results of the mixed-effects, logistic regression to characterize the association of memory span, ratio, trial and participant with the probability of selecting the close cup first.

CoefficientEstimateSEWalddfp valueOdds ratio
Constant2.0690.9982.07310.0387.916
Memory span0.6680.8810.75810.4481.950
Ratio 1 (100:100)− 0.5760.764− 0.75310.4520.562
Ratio 2 (100:50)− 3.3610.774− 4.3421< 0.00010.0004
Trial− 0.4410.123− 3.5841< 0.0010.644
Trial′0.2940.1262.33510.0201.342
Ratio 1 × memory span− 0.0170.338− 0.05010.960983
Ratio 2 × memory span− 0.8300.368− 2.25610.0240.436
Memory span × trial− 0.0470.043− 1.09010.2760.955
Ratio 1 × trial− 0.3340.116− 2.88110.0040.716
Ratio 2 × trial0.0030.1170.02210.9821.003
  1. B is the coefficient (slope) or amount of change predicted in the log odds of precrastination for every 1-unit change of each effect in the logistic regression equation. SE B is the standard error of the coefficient. The Wald statistic and associated p-value provide evidence against the null hypothesis of a zero-magnitude effect. The source trial and trial′ represent coefficients for the natural cubic spline

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Raghunath, N., Fournier, L.R. & Kogan, C. Precrastination and individual differences in working memory capacity. Psychological Research (2020). https://doi.org/10.1007/s00426-020-01373-6

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