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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    Article  Google 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

    Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google Scholar 

  27. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. doi:10.1037/0033-295X.85.2.59

    Article  Google 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.

    PubMed  Google 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.

    PubMed  Google 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

    PubMed Central  PubMed  Article  Google 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

    PubMed Central  PubMed  Article  Google Scholar 

  33. Teodorescu, A. R., & Usher, M. (2013). Disentangling decision models: From independence to competition. Psychological Review, 120, 1–38. doi:10.1037/a0030776

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    Article  Google 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

    PubMed  Article  Google 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

    PubMed  Article  Google 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

    PubMed Central  PubMed  Google 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

    PubMed  Article  Google 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

    PubMed Central  PubMed  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jasper Winkel.

Additional information

A comment to this article is available at http://dx.doi.org/10.3758/s13423-015-0851-2.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(ZIP 5.75 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Winkel, J., Keuken, M.C., van Maanen, L. et al. Early evidence affects later decisions: Why evidence accumulation is required to explain response time data. Psychon Bull Rev 21, 777–784 (2014). https://doi.org/10.3758/s13423-013-0551-8

Download citation

Keywords

  • Decision making
  • Drift diffusion model
  • Urgency gating model
  • Evidence accumulation