Advertisement

Psychological Research

, Volume 79, Issue 1, pp 83–94 | Cite as

Time pressure affects the efficiency of perceptual processing in decisions under conflict

  • Michael DambacherEmail author
  • Ronald Hübner
Original Article

Abstract

The negative correlation between speed and accuracy in perceptual decision making is often explained as a tradeoff, where lowered decision boundaries under time pressure result in faster but more error-prone responses. Corresponding implementations in sequential sampling models confirmed the success of this account, which has led to the prevalent assumption that a second component of decision making, the efficiency of perceptual processing, is largely independent from temporal demands. To test the generality of this claim, we examined time pressure effects on decisions under conflict. Data from a flanker task were fit with a sequential sampling model that incorporates two successive phases of response selection, driven by the output of an early and late stage of stimulus selection, respectively. The fits revealed the canonical decrease of response boundaries with increasing time pressure. In addition, time pressure reduced the duration of non-decisional processes and impaired the early stage of stimulus selection, together with the subsequent first phase of response selection. The results show that the relation between speed and accuracy not only relies on the strategic adjustment of response boundaries but involves variations of processing efficiency. The findings support recent evidence of drift rate modulations in response to time pressure in simple perceptual decisions and confirm their validity in the context of more complex tasks.

Keywords

Time Pressure Response Selection Congruency Effect Drift Rate Incongruent Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Michaela Rach for data acquisition and Leendert van Maanen for valuable comments on a previous version of this article. This research was supported by the German Research Foundation (DFG) through research unit FOR 1882 Psychoeconomics.

Conflict of interest

The authors declare that no competing interests exist.

References

  1. Abdi, H., & Williams, L. J. (2010). Jackknife. In N. Salkind (Ed.), Encyclopedia of Research Design (pp. 655–661). Thousand Oaks: Sage.Google Scholar
  2. Bausenhart, K. M., Rolke, B., Seibold, V. C., & Ulrich, R. (2010). Temporal preparation influences the dynamics of information processing: evidence for early onset of information accumulation. Vision Research, 50(11), 1025–1034. doi: 10.1016/j.visres.2010.03.011.PubMedCrossRefGoogle Scholar
  3. Bogacz, R., Wagenmakers, E.-J., Forstmann, B. U., & Nieuwenhuis, S. (2010). The neural basis of the speed-accuracy tradeoff. Trends in Neurosciences, 33(1), 10–16. doi: 10.1016/j.tins.2009.09.002.PubMedCrossRefGoogle Scholar
  4. Brent, R. P. (1973). Algorithms for function minimization without derivatives. Englewood-Cliffs: Prentice-Hall.Google Scholar
  5. Brown, S., & Heathcote, A. (2005). A ballistic model of choice response time. Psychological Review, 112(1), 117–128. doi: 10.1037/0033-295X.112.1.117.PubMedCrossRefGoogle Scholar
  6. Brown, S., & Heathcote, A. (2008). The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology, 57(3), 153–178. doi: 10.1016/j.cogpsych.2007.12.002.PubMedCrossRefGoogle Scholar
  7. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432–459.PubMedCrossRefGoogle Scholar
  8. Carrasco, M., & McElree, B. (2001). Covert attention accelerates the rate of visual information processing. Proceedings of the National Academy of Sciences of the United States of America, 98(9), 5363–5367. doi: 10.1073/pnas.081074098.PubMedCentralPubMedCrossRefGoogle Scholar
  9. Dambacher, M., & Hübner, R. (2013). Investigating the speed-accuracy trade-off: better use deadlines or response signals? Behavior Research Methods, 45(3), 702–717. doi: 10.3758/s13428-012-0303-0.PubMedCrossRefGoogle Scholar
  10. Dambacher, M., Hübner, R., & Schlösser, J. (2011). Monetary incentives in speeded perceptual decision: effects of penalizing errors versus slow responses. Frontiers in Psychology, 2, 248. doi: 10.3389/fpsyg.2011.00248.PubMedCentralPubMedCrossRefGoogle Scholar
  11. Diederich, A., & Busemeyer, J. R. (2006). Modeling the effects of payoff on response bias in a perceptual discrimination task: bound-change, drift-rate-change, or two-stage-processing hypothesis. Perception and Psychophysics, 68(2), 194–207.PubMedCrossRefGoogle Scholar
  12. Dosher, B. A. (1976). The retrieval of sentences from memory: a speed-accuracy study. Cognitive Psychology, 8(3), 291–310. doi: 10.1016/0010-0285(76)90009-8.CrossRefGoogle Scholar
  13. Efron, B. (1979). Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1), 1–26.CrossRefGoogle Scholar
  14. Efron, B. (1982). The jackknife, the bootstrap and other resampling plans. Montpelier: Capital City Press.CrossRefGoogle Scholar
  15. Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception and Psychophysics, 16(1), 143–149. doi: 10.3758/BF03203267.CrossRefGoogle Scholar
  16. Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: advancing the debate. Perspectives on Psychological Science, 8(3), 223–241. doi: 10.1177/1745691612460685.CrossRefGoogle Scholar
  17. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 121(3), 262–269.CrossRefGoogle Scholar
  18. Forstmann, B. U., Anwander, A., Schäfer, A., Neumann, J., Brown, S., Wagenmakers, E.-J., et al. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proceedings of the National Academy of Sciences of the United States of America, 107(36), 15916–15920. doi: 10.1073/pnas.1004932107.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Forstmann, B. U., Dutilh, G., Brown, S., Neumann, J., von Cramon, D. Y., Ridderinkhof, K. R., et al. (2008). Striatum and pre-SMA facilitate decision-making under time pressure. Proceedings of the National Academy of Sciences of the United States of America, 105(45), 17538–17542. doi: 10.1073/pnas.0805903105.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Forstmann, B. U., Tittgemeyer, M., Wagenmakers, E.-J., Derrfuss, J., Imperati, D., & Brown, S. (2011). The speed-accuracy tradeoff in the elderly brain: a structural model-based approach. The Journal of Neuroscience, 31(47), 17242–17249. doi: 10.1523/jneurosci.0309-11.2011.PubMedCrossRefGoogle Scholar
  21. Garrett, H.E. (1922). A study of the relation of accuracy to speed. Archives of Psychology, 56, 1–104.Google Scholar
  22. Gegenfurtner, K. R. (1992). PRAXIS: Brent’s algorithm for function minimization. Behavior Research Methods, Instruments, and Computers, 24(4), 560–564. doi: 10.3758/BF03203605.CrossRefGoogle Scholar
  23. Gratton, G., Coles, M. G. H., & Donchin, E. (1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General, 121(4), 480–506.CrossRefGoogle Scholar
  24. Gray, H. L., & Schucany, W. R. (1972). The generalized jackknife statistic. New York: Marcel Dekker.Google Scholar
  25. Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394(20), 780–784.PubMedCrossRefGoogle Scholar
  26. Heathcote, A., & Love, J. (2012). Linear deterministic accumulator models of simple choice. Frontiers in Psychology, 3, 292. doi: 10.3389/fpsyg.2012.00292.PubMedCentralPubMedCrossRefGoogle Scholar
  27. Heitz, R. P., & Schall, J. D. (2012). Neural mechanisms of speed-accuracy tradeoff. Neuron, 76(3), 616–628. doi: 10.1016/j.neuron.2012.08.030.PubMedCentralPubMedCrossRefGoogle Scholar
  28. Ho, T., Brown, S., van Maanen, L., Forstmann, B. U., Wagenmakers, E.-J., & Serences, J. T. (2012). The optimality of sensory processing during the speed-accuracy tradeoff. The Journal of Neuroscience, 32(23), 7992–8003. doi: 10.1523/jneurosci0340-12.2012.PubMedCentralPubMedCrossRefGoogle Scholar
  29. Hübner, R., & Schlösser, J. (2010). Monetary reward increases attentional effort in the flanker task. Psychonomic Bulletin and Review, 17(6), 821–826. doi: 10.3758/pbr.17.6.821.PubMedCrossRefGoogle Scholar
  30. Hübner, R., Steinhauser, M., & Lehle, C. (2010). A dual-stage two-phase model of selective attention. Psychological Review, 117(3), 759–784. doi: 10.1037/a0019471.PubMedCrossRefGoogle Scholar
  31. Hübner, R., & Töbel, L. (2012). Does attentional selectivity in the flanker task improve discretely or gradually? Frontiers in Psychology, 3, 434. doi: 10.3389/fpsyg.2012.00434.
  32. Ivanoff, J., Branning, P., & Marois, R. (2008). fMRI evidence for a dual process account of the speed-accuracy tradeoff in decision-making. PLoS One, 3(7), e2635. doi: 10.1371/journal.pone.0002635.PubMedCentralPubMedCrossRefGoogle Scholar
  33. Jackson, P. R. (1986). Robust methods in statistics. In A. D. Lovie (Ed.), New developments in statistics for psychology and the social sciences (pp. 22–43). London: The British Psychological Society and Methuen.Google Scholar
  34. Kleinsorge, T. (2001). The time course of effort mobilization and strategic adjustments of response criteria. Psychological Research, 65(3), 216–223.PubMedCrossRefGoogle Scholar
  35. Logan, G. D., & Gordon, R. D. (2001). Executive control of visual attention in dual-task situations. Psychological Review, 108(2), 393–434.PubMedCrossRefGoogle Scholar
  36. McElree, B., & Carrasco, M. (1999). The temporal dynamics of visual search: evidence for parallel processing in feature and conjunction searches. Journal of Experimental Psychology: Human Perception and Performance, 25(6), 1517–1539.PubMedCentralPubMedGoogle Scholar
  37. Miller, J., Patterson, T., & Ulrich, R. (1998). Jackknife-based method for measuring LRP onset latency differences. Psychophysiology, 35(1), 99–115.PubMedCrossRefGoogle Scholar
  38. Miller, J., Sproesser, G., & Ulrich, R. (2008). Constant versus variable response signal delays in speed–accuracy trade-offs: effects of advance preparation for processing time. Perception and Psychophysics, 70(5), 878–886.PubMedCrossRefGoogle Scholar
  39. Mosteller, F., & Tukey, J. (1977). Data analysis and regression. Reading: Addison-Wesley.Google Scholar
  40. Osman, A., Lou, L., Müller-Gethmann, H., Rinkenauer, G., Mattes, S., & Ulrich, R. (2000). Mechanisms of speed–accuracy tradeoff: evidence from covert motor processes. Biological Psychology, 51(2–3), 173–199.PubMedCrossRefGoogle Scholar
  41. Palmer, J., Huk, A. C., & Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision, 5(5), 376–404. doi:http://www.ncbi.nlm.nih.gov/pubmed/16097871.PubMedCrossRefGoogle Scholar
  42. Philiastides, M. G., Ratcliff, R., & Sajda, P. (2006). Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram. The Journal of Neuroscience, 26(35), 8965–8975. doi: 10.1523/jneurosci.1655-06.2006.PubMedCrossRefGoogle Scholar
  43. Rae, B., Heathcote, A., Donkin, C., Averell, L., & Brown, S. (2014). The hare and the tortoise: emphasizing speed can change the evidence used to make decisions. Journal of Experimental Psychology: Learning, Memory, and Cognition (in press).Google Scholar
  44. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108.CrossRefGoogle Scholar
  45. Ratcliff, R. (1979). Group reaction time distributions and an analysis of distribution statistics. Psychological Bulletin, 86(3), 446–461.PubMedCrossRefGoogle Scholar
  46. Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation, 20(4), 873–922. doi: 10.1162/neco.2008.12-06-420.PubMedCentralPubMedCrossRefGoogle Scholar
  47. Ratcliff, R., & Rouder, J. N. (1998). Modeling response times for two-choice decisions. Psychological Science, 9(5), 347–356. doi: 10.1111/1467-9280.00067.CrossRefGoogle Scholar
  48. Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111(2), 333–367. doi: 10.1037/0033-295X.111.2.333.PubMedCentralPubMedCrossRefGoogle Scholar
  49. Ratcliff, R., Thapar, A., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on brightness discrimination. Perception and Psychophysics, 65(4), 523–535.PubMedCentralPubMedCrossRefGoogle Scholar
  50. Reed, A. V. (1973). Speed-accuracy trade-off in recognition memory. Science, 181(4099), 574–576. doi: 10.1126/science.181.4099.574.PubMedCrossRefGoogle Scholar
  51. Rinkenauer, G., Osman, A., Ulrich, R., Müller-Gethmann, H., & Mattes, S. (2004). On the locus of speed-accuracy trade-off in reaction time: inferences from the lateralized readiness potential. Journal of Experimental Psychology: General, 133(2), 261–282. doi: 10.1037/0096-3445.133.2.261.CrossRefGoogle Scholar
  52. Seibold, V. C., Bausenhart, K. M., Rolke, B., & Ulrich, R. (2011). Does temporal preparation increase the rate of sensory information accumulation? Acta Psychologica, 137(1), 56–64. doi: 10.1016/j.actpsy.2011.02.006.PubMedCrossRefGoogle Scholar
  53. Simon, J. R. (1990). The effects of an irrelevant directional cue on human information processing. In R. W. Proctor & T. G. Reeve (Eds.), Stimulus-response compatibility: an integrated perspective (pp. 31–86). Amsterdam: North-Holland.Google Scholar
  54. Starns, J. J., Ratcliff, R., & McKoon, G. (2012). Evaluating the unequal-variance and dual-process explanations of zROC slopes with response time data and the diffusion model. Cognitive Psychology, 64(1–2), 1–34. doi: 10.1016/j.cogpsych.2011.10.002.PubMedCentralPubMedCrossRefGoogle Scholar
  55. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662. doi: 10.1037/h0054651.CrossRefGoogle Scholar
  56. Ulrich, R., & Miller, J. (2001). Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs. Psychophysiology, 38(5), 816–827.PubMedCrossRefGoogle Scholar
  57. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: the leaky, competing accumulator model. Psychological Review, 108(3), 550–592. doi: 10.1037//0033-295X.108.3.550.PubMedCrossRefGoogle Scholar
  58. Van der Lubbe, R. H. J., Jaśkowski, P., Wauschkuhn, B., & Verleger, R. (2001). Influence of time pressure in a simple response task, a choice-by-location task, and the Simon task. Journal of Psychophysiology, 15(4), 241–255. doi: 10.1027//0269-8803.15.4.241.CrossRefGoogle Scholar
  59. Van Veen, V., Krug, M. K., & Carter, C. S. (2008). The neural and computational basis of controlled speed-accuracy tradeoff during task performance. Journal of Cognitive Neuroscience, 20(11), 1952–1965. doi: 10.1162/jocn.2008.20146.PubMedCrossRefGoogle Scholar
  60. Vandekerckhove, J., Tuerlinckx, F., & Lee, M.D. (2008). A Bayesian approach to diffusion process models of decision-making. In V. Sloutsky, B. Love, & K. McRae (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1429–1434). Austin.Google Scholar
  61. Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion models in experimental psychology: a practical introduction. Experimental Psychology, 60, 385–402. doi: 10.1027/1618-3169/a000218.Google Scholar
  62. White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion models of the flanker task: discrete versus gradual attentional selection. Cognitive Psychology, 63(4), 210–238. doi: 10.1016/j.cogpsych.2011.08.001.PubMedCentralPubMedCrossRefGoogle Scholar
  63. Wickelgren, W. A. (1977). Speed-accuracy tradeoff and information processing dynamics. Acta Psychologica, 41(1), 67–85. doi: 10.1016/0001-6918(77)90012-9.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Psychology (Box D29)Universität KonstanzConstanceGermany

Personalised recommendations