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Psychonomic Bulletin & Review

, Volume 21, Issue 3, pp 777–784 | Cite as

Early evidence affects later decisions: Why evidence accumulation is required to explain response time data

  • Jasper Winkel
  • Max C. Keuken
  • Leendert van Maanen
  • Eric-Jan Wagenmakers
  • Birte U. Forstmann
Brief Report

Abstract

Models of decision making differ in how they treat early evidence as it recedes in time. Standard models, such as the drift diffusion model, assume that evidence is gradually accumulated until it reaches a boundary and a decision is initiated. One recent model, the urgency gating model, has proposed that decision making does not require the accumulation of evidence at all. Instead, accumulation could be replaced by a simple urgency factor that scales with time. To distinguish between these fundamentally different accounts of decision making, we performed an experiment in which we manipulated the presence, duration, and valence of early evidence. We simulated the associated response time and error rate predictions from the drift diffusion model and the urgency gating model, fitting the models to the empirical data. The drift diffusion model predicted that variations in the evidence presented early in the trial would affect decisions later in that same trial. The urgency gating model predicted that none of these variations would have any effect. The behavioral data showed clear effects of early evidence on the subsequent decisions, in a manner consistent with the drift diffusion model. Our results cannot be explained by the urgency gating model, and they provide support for an evidence accumulation account of perceptual decision making.

Keywords

Decision making Drift diffusion model Urgency gating model Evidence accumulation 

Notes

Author Note

This work was supported by VENI and by an open competition grant (BUF) from the Netherlands Organization for Scientific Research (NWO). The authors thank Mascha Kraak, Eline Scheper, Monique Mendriks, and Josien Stam for their help in running the experiment.

Supplementary material

13423_2013_551_MOESM1_ESM.zip (6 kb)
ESM 1 (ZIP 5.75 kb)

References

  1. Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. D. (2006). The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review, 113, 700–765. doi: 10.1037/0033-295X.113.4.700 PubMedCrossRefGoogle Scholar
  2. Bogacz, R., Wagenmakers, E.-J., Forstmann, B. U., & Nieuwenhuis, S. (2010). The neural basis of the speed–accuracy tradeoff. Trends in Neurosciences, 33, 10–16. doi: 10.1016/j.tins.2009.09.002 PubMedCrossRefGoogle Scholar
  3. Brown, S. D., & Heathcote, A. (2008). The simplest complete model of choice response time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178. doi: 10.1016/j.cogpsych.2007.12.002 PubMedCrossRefGoogle Scholar
  4. Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011). Variance as a signature of neural computations during decision making. Neuron, 69, 818–831. doi: 10.1016/j.neuron.2010.12.037 PubMedCentralPubMedCrossRefGoogle Scholar
  5. Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008). Decision-making with multiple alternatives. Nature Neuroscience, 11, 693–702. doi: 10.1038/nn.2123 PubMedCentralPubMedCrossRefGoogle Scholar
  6. Cisek, P., Puskas, G. A., & El-Murr, S. (2009). Decisions in changing conditions: The urgency-gating model. Journal of Neuroscience, 29, 11560–11571. doi: 10.1523/JNEUROSCI.1844-09.2009 PubMedCrossRefGoogle Scholar
  7. Deneve, S. (2012). Making decisions with unknown sensory reliability. Frontiers in Decision Neuroscience, 6, 75. doi: 10.3389/fnins.2012.00075 Google Scholar
  8. Ditterich, J. (2006). Evidence for time-variant decision making. European Journal of Neuroscience, 24, 3628–3641. doi: 10.1111/j.1460-9568.2006.05221.x PubMedCrossRefGoogle Scholar
  9. Domenech, P., & Dreher, J.-C. (2010). Decision threshold modulation in the human brain. Journal of Neuroscience, 30, 14305–14317. doi: 10.1523/JNEUROSCI.2371-10.2010 PubMedCrossRefGoogle Scholar
  10. Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., & Pouget, A. (2012). The cost of accumulating evidence in perceptual decision making. Journal of Neuroscience, 32, 3612–3628. doi: 10.1523/JNEUROSCI.4010-11.2012 PubMedCentralPubMedCrossRefGoogle Scholar
  11. Forstmann, B. U., Dutilh, G., Brown, S., Neumann, J., von Cramon, D. Y., Ridderinkhof, K. R., & Wagenmakers, E.-J. (2008). Striatum and pre-SMA facilitate decision-making under time pressure. Proceedings of the National Academy of Sciences, 105, 17538–17542. doi: 10.1073/pnas.0805903105
  12. Forstmann, B. U., Brown, S., Dutilh, G., Neumann, J., & Wagenmakers, E.-J. (2010). The neural substrate of prior information in perceptual decision making: A model-based analysis. Frontiers in Human Neuroscience, 4, 40. doi: 10.3389/fnhum.2010.00040 PubMedCentralPubMedCrossRefGoogle Scholar
  13. Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30, 535–574. doi: 10.1146/annurev.neuro.29.051605.113038 PubMedCrossRefGoogle Scholar
  14. Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E., & Shadlen, M. N. (2011). Elapsed decision time affects the weighting of prior probability in a perceptual decision task. Journal of Neuroscience, 31, 6339–6352. doi: 10.1523/JNEUROSCI.5613-10.2011 PubMedCentralPubMedCrossRefGoogle Scholar
  15. Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 467–479. doi: 10.1038/nrn2374 PubMedCrossRefGoogle Scholar
  16. Huk, A. C., & Shadlen, M. N. (2005). Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making. Journal of Neuroscience, 25, 10420–10436. doi: 10.1523/JNEUROSCI.4684-04.2005 PubMedCrossRefGoogle Scholar
  17. Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008). Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. Journal of Neuroscience, 28, 3017–3029. doi: 10.1523/JNEUROSCI.4761-07.2008 PubMedCrossRefGoogle Scholar
  18. Laming, D. R. J. (1968). Information theory of choice-reaction times. London, UK: Academic Press.Google Scholar
  19. Mansfield, E. L., Karayanidis, F., Jamadar, S., Heathcote, A., & Forstmann, B. U. (2011). Adjustments of response threshold during task switching: A model-based functional magnetic resonance imaging study. Journal of Neuroscience, 31, 14688–14692. doi: 10.1523/JNEUROSCI.2390-11.2011 PubMedCrossRefGoogle Scholar
  20. Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., & Rangel, A. (2010). The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgment and Decision Making, 5, 437–449. doi: 10.2139/ssrn.1901533 Google Scholar
  21. Mulder, M. J., Wagenmakers, E.-J., Ratcliff, R., Boekel, W., & Forstmann, B. U. (2012). Bias in the brain: A diffusion model analysis of prior probability and potential payoff. Journal of Neuroscience, 32, 2335–2343. doi: 10.1523/JNEUROSCI.4156-11.2012 PubMedCrossRefGoogle Scholar
  22. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308–313. doi: 10.1093/comjnl/7.4.308 CrossRefGoogle Scholar
  23. Ossmy, O., Moran, R., Pfeffer, T., Tsetsos, K., Usher, M., & Donner, T. H. (2013). The timescale of perceptual evidence integration can be adapted to the environment. Current Biology, 23, 1–6. doi: 10.1016/j.cub.2013.04.039 CrossRefGoogle Scholar
  24. 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):1, 376–404. doi: 10.1167/5.5.1 Google Scholar
  25. Philiastides, M. G., Auksztulewicz, R., Heekeren, H. R., & Blankenburg, F. (2011). Causal role of dorsolateral prefrontal cortex in human perceptual decision making. Current Biology, 21, 980–983. doi: 10.1016/j.cub.2011.04.034 PubMedCrossRefGoogle Scholar
  26. Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed choice. Current Opinion in Neurobiology, 20, 262–70. doi: 10.1016/j.conb.2010.03.001 PubMedCrossRefGoogle Scholar
  27. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. doi: 10.1037/0033-295X.85.2.59 CrossRefGoogle Scholar
  28. Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22, 9475–9489.PubMedGoogle Scholar
  29. Ruff, D. A., Marrett, S., Heekeren, H. R., Bandettini, P. A., & Ungerleider, L. G. (2010). Complementary roles of systems representing sensory evidence and systems detecting task difficulty during perceptual decision making. Frontiers in Decision Neuroscience, 4, 190. doi: 10.3389/fnins.2010.00190 Google Scholar
  30. Shadlen, M. N., & Newsome, W. T. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. Journal of Neurophysiology, 86, 1916–1936.PubMedGoogle Scholar
  31. Simen, P. (2012). Evidence accumulator or decision threshold—Which cortical mechanism are we observing? Frontiers in Psychology, 3, 183. doi: 10.3389/fpsyg.2012.00183 PubMedCentralPubMedCrossRefGoogle Scholar
  32. Standage, D., You, H., Wang, D.-H., & Dorris, M. C. (2011). Gain modulation by an urgency signal controls the speed–accuracy trade-off in a network model of a cortical decision circuit. Frontiers in Computational Neuroscience, 5, 7. doi: 10.3389/fncom.2011.00007 PubMedCentralPubMedCrossRefGoogle Scholar
  33. Teodorescu, A. R., & Usher, M. (2013). Disentangling decision models: From independence to competition. Psychological Review, 120, 1–38. doi: 10.1037/a0030776 PubMedCrossRefGoogle Scholar
  34. Thura, D., Beauregard-Racine, J., Fradet, C.-W., & Cisek, P. (2012). Decision making by urgency gating: Theory and experimental support. Journal of Neurophysiology, 108, 2912–2930. doi: 10.1152/jn.01071.2011 PubMedCrossRefGoogle Scholar
  35. Tsetsos, K., Chater, N., & Usher, M. (2012a). Salience driven value integration explains decision biases and preference reversal. Proceedings of the National Academy of Sciences, 109, 9659–9664. doi: 10.1073/pnas.1119569109 CrossRefGoogle Scholar
  36. Tsetsos, K., Gao, J., McClelland, J. L., & Usher, M. (2012b). Using time-varying evidence to test models of decision dynamics: bounded diffusion vs. the leaky competing accumulator model. Frontiers in Decision Neuroscience, 6, 79. doi: 10.3389/fnins.2012.00079 Google Scholar
  37. Tsetsos, K., Usher, M., & McClelland, J. L. (2011). Testing multi-alternative decision models with non-stationary evidence. Frontiers in Decision Neuroscience, 5, 63. doi: 10.3389/fnins.2011.00063 Google Scholar
  38. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108, 550–592. doi: 10.1037/0033-295X.111.3.757 PubMedCrossRefGoogle Scholar
  39. van Campen, A. D., Keuken, M. C., van den Wildenberg, W. P. M., & Ridderinkhof, K. R. (in press). TMS over M1 reveals expression and selective suppression of conflicting action impulses. Journal of Cognitive Neuroscience. doi: 10.1162/jocn_a_00482
  40. van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E.-J., Ho, T., Serences, J., & Forstmann, B. U. (2011). Neural correlates of trial-to-trial fluctuations in response caution. Journal of Neuroscience, 31, 17488–17495. doi: 10.1523/JNEUROSCI.2924-11.2011 PubMedCrossRefGoogle Scholar
  41. van Ravenzwaaij, D., Mulder, M. J., Tuerlinckx, F., & Wagenmakers, E.-J. (2012). Do the dynamics of prior information depend on task context? An analysis of optimal performance and an empirical test. Frontiers in Psychology, 3, 132. doi: 10.3389/fpsyg.2012.00132 PubMedCentralPubMedGoogle Scholar
  42. van Vugt, M. K., Simen, P., Nystrom, L. E., Holmes, P., & Cohen, J. D. (2012). EEG oscillations reveal neural correlates of evidence accumulation. Frontiers in Decision Neuroscience, 6, 106. doi: 10.3389/fnins.2012.00106 Google Scholar
  43. Wenzlaff, H., Bauer, M., Maess, B., & Heekeren, H. R. (2011). Neural characterization of the speed–accuracy tradeoff in a perceptual decision-making task. Journal of Neuroscience, 31, 1254–1266. doi: 10.1523/JNEUROSCI.4000-10.2011 PubMedCrossRefGoogle Scholar
  44. Winkel, J., van Maanen, L., Ratcliff, R., van der Schaaf, M. E., van Schouwenburg, M. R., Cools, R., & Forstmann, B. U. (2012). Bromocriptine does not alter speed–accuracy tradeoff. Frontiers in Decision Neuroscience, 6, 126. doi: 10.3389/fnins.2012.00126 Google Scholar
  45. Zhang, J. (2012). The effects of evidence bounds on decision-making: Theoretical and empirical developments. Frontiers in Psychology, 3, 263. doi: 10.3389/fpsyg.2012.00263 PubMedCentralPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Jasper Winkel
    • 1
  • Max C. Keuken
    • 1
    • 2
  • Leendert van Maanen
    • 1
  • Eric-Jan Wagenmakers
    • 1
  • Birte U. Forstmann
    • 1
    • 2
  1. 1.Cognitive Science Center AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany

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