A one-boundary drift-diffusion model for time to collision estimation in a simple driving task

References

• Balcı, F., & Simen, P. (2016). A decision model of timing. Current Opinion in Behavioral Sciences, 8, 94–101. doi: 10.1016/j.cobeha.2016.02.002
   Web of Science ®Google Scholar
• Balci, F., Simen, P., Niyogi, R., Saxe, A., Hughes, J. A., Holmes, P., & Cohen, J. D. (2011). Acquisition of decision making criteria: Reward rate ultimately beats accuracy. Attention, Perception, & Psychophysics, 73(2), 640–657. doi: 10.3758/s13414-010-0049-7
   PubMed Web of Science ®Google Scholar
• Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., … Pouget, A. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60(6), 1142–1152. doi: 10.1016/j.neuron.2008.09.021
   PubMed Web of Science ®Google Scholar
• Bogacz, R. (2007). Optimal decision-making theories: Linking neurobiology with behaviour. Trends in Cognitive Sciences, 11(3), 118–125. doi: 10.1016/j.tics.2006.12.006
   PubMed Web of Science ®Google Scholar
• Bogacz, R., Hu, P. T., Holmes, P. J., & Cohen, J. D. (2010). Do humans produce the speed–accuracy trade-off that maximizes reward rate? The Quarterly Journal of Experimental Psychology, 63(5), 863–891. doi: 10.1080/17470210903091643
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Daneshi, A., Azarnoush, H., Towhidkhah, F., Gohari, A., & Ghazizadeh, A. (2019). Drift-diffusion explains response variability and capacity for tracking objects. Scientific Reports, 9, 1–15. doi: 10.1038/s41598-019-47624-4
   PubMed Web of Science ®Google Scholar
• 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(45), 17538–17542. doi: 10.1073/pnas.0805903105
   PubMed Web of Science ®Google Scholar
• Gold, J. I., & Shadlen, M. N. (2000). Representation of a perceptual decision in developing oculomotor commands. Nature, 404(6776), 390–394. doi: 10.1038/35006062
   PubMed Web of Science ®Google Scholar
• Gold, J. I., & Shadlen, M. N. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences, 5(1), 10–16. doi: 10.1016/S1364-6613(00)01567-9
   PubMed Web of Science ®Google Scholar
• 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 Web of Science ®Google Scholar
• Grossberg, S., & Pilly, P. K. (2008). Temporal dynamics of decision-making during motion perception in the visual cortex. Vision Research, 48(12), 1345–1373. doi: 10.1016/j.visres.2008.02.019
   PubMed Web of Science ®Google Scholar
• Hanes, D. P., & Schall, J. D. (1996). Neural control of voluntary movement initiation. Science, 274(5286), 427–430. doi: 10.1126/science.274.5286.427
   PubMed Web of Science ®Google Scholar
• Heekeren, H. R., Marrett, S., Bandettini, P. A., & Ungerleider, L. G. (2004). A general mechanism for perceptual decision-making in the human brain. Nature, 431(7010), 859–862. doi: 10.1038/nature02966
   PubMed Web of Science ®Google Scholar
• Johansson, G., & Rumar, K. (1971). Drivers’ brake reaction times. Human Factors, 13(1), 23–27. doi: 10.1177/001872087101300104
   PubMed Web of Science ®Google Scholar
• Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108(33), 13852–13857. doi: 10.1073/pnas.1101328108
   PubMed Web of Science ®Google Scholar
• Meyer, F., & Bouthemy, P. (1992). Estimation of time-to-collision maps from first order motion models and normal flows. Paper presented at the [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition. Los Alamitos: IEEE Computer Society Press.
   Google Scholar
• 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), 1–1. doi: 10.1167/5.5.1
   PubMed Web of Science ®Google Scholar
• Philiastides, M. G., Ratcliff, R., & Sajda, P. (2006). Neural representation of task difficulty and decision making during perceptual categorization: A timing diagram. Journal of Neuroscience, 26(35), 8965–8975. doi: 10.1523/JNEUROSCI.1655-06.2006
   PubMed Web of Science ®Google Scholar
• Philiastides, M. G., & Sajda, P. (2007). EEG-informed fMRI reveals spatiotemporal characteristics of perceptual decision making. Journal of Neuroscience, 27(48), 13082–13091. doi: 10.1523/JNEUROSCI.3540-07.2007
   PubMed Web of Science ®Google Scholar
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108. doi: 10.1037/0033-295X.85.2.59
   Web of Science ®Google Scholar
• Ratcliff, R. (2015). Modeling one-choice and two-choice driving tasks. Attention, Perception, & Psychophysics, 77(6), 2134–2144. doi: 10.3758/s13414-015-0911-8
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., Cherian, A., & Segraves, M. (2003). A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. Journal of Neurophysiology, 90(3), 1392–1407. doi: 10.1152/jn.01049.2002
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Childers, R. (2015). Individual differences and fitting methods for the two-choice diffusion model of decision making. Decision, 2(4), 237–279. doi: 10.1037/dec0000030
   Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Strayer, D. (2014). Modeling simple driving tasks with a one-boundary diffusion model. Psychonomic Bulletin & Review, 21(3), 577–589. doi: 10.3758/s13423-013-0541-x
   PubMed Web of Science ®Google Scholar
• Ratcliff, R., & Van Dongen, H. P. (2011). Diffusion model for one-choice reaction-time tasks and the cognitive effects of sleep deprivation. Proceedings of the National Academy of Sciences, 108(27), 11285–11290. doi: 10.1073/pnas.1100483108
   PubMed Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Rivest, F., & Bengio, Y. (2011). Adaptive drift-diffusion process to learn time intervals. arXiv preprint arXiv:1103.2382.
   Google Scholar
• Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001). Multialternative decision field theory: A dynamic connectionst model of decision making. Psychological Review, 108(2), 370–392. doi: 10.1037/0033-295X.108.2.370
   PubMed Web of Science ®Google Scholar
• 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(21), 9475–9489. doi: 10.1523/JNEUROSCI.22-21-09475.2002
   PubMed Web of Science ®Google Scholar
• Schall, J. D. (2001). Neural basis of deciding, choosing and acting. Nature Reviews Neuroscience, 2(1), 33–42. doi: 10.1038/35049054
   PubMed Web of Science ®Google Scholar
• Schall, J. D., Purcell, B. A., Heitz, R. P., Logan, G. D., & Palmeri, T. J. (2011). Neural mechanisms of saccade target selection: Gated accumulator model of the visual–motor cascade. European Journal of Neuroscience, 33(11), 1991–2002. doi: 10.1111/j.1460-9568.2011.07715.x
   PubMed Web of Science ®Google Scholar
• Simen, P., Balci, F., deSouza L., Cohen, J. D., & Holmes, P. (2011). A model of interval timing by neural integration. Journal of Neuroscience, 31(25), 9238–9253. doi: 10.1523/JNEUROSCI.3121-10.2011
   PubMed Web of Science ®Google Scholar
• Simen, P., Cohen, J. D., & Holmes, P. (2006). Rapid decision threshold modulation by reward rate in a neural network. Neural Networks, 19(8), 1013–1026. doi: 10.1016/j.neunet.2006.05.038
   PubMed Web of Science ®Google Scholar
• Simen, P., Contreras, D., Buck, C., Hu, P., Holmes, P., & Cohen, J. D. (2009). Reward rate optimization in two-alternative decision making: Empirical tests of theoretical predictions. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1865–1897. doi: 10.1037/a0016926
   PubMed Web of Science ®Google Scholar
• Simen, P., Rivest, F., Ludvig, E. A., Balci, F., & Killeen, P. (2013). Timescale invariance in the pacemaker-accumulator family of timing models. Timing & Time Perception, 1(2), 159–188. doi: 10.1163/22134468-00002018
   Google Scholar
• Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27(3), 161–168. doi: 10.1016/j.tins.2004.01.006
   PubMed Web of Science ®Google Scholar
• Taimela, S. (1991). Factors affecting reaction-time testing and the interpretation of results. Perceptual and Motor Skills, 73(3_suppl), 1195–1202. doi: 10.2466/pms.1991.73.3f.1195
   Web of Science ®Google Scholar
• Tuerlinckx, F., Maris, E., Ratcliff, R., & De Boeck, P. (2001). A comparison of four methods for simulating the diffusion process. Behavior Research Methods, Instruments, & Computers, 33(4), 443–456. doi: 10.3758/BF03195402
   PubMedGoogle Scholar
• Wagenmakers, E.-J. (2009). Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy. European Journal of Cognitive Psychology, 21(5), 641–671. doi: 10.1080/09541440802205067
   Web of Science ®Google Scholar
• Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36(5), 955–968. doi: 10.1016/S0896-6273(02)01092-9
   PubMed Web of Science ®Google Scholar
• White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010). Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology, 54(1), 39–52. doi: 10.1016/j.jmp.2010.01.004
   PubMed Web of Science ®Google Scholar
• Wong, K.-F., & Wang, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. The Journal of Neuroscience, 26(4), 1314–1328. doi: 10.1523/JNEUROSCI.3733-05.2006
   PubMed Web of Science ®Google Scholar
• Woods, D. L., Wyma, J. M., Yund, E. W., Herron, T. J., & Reed, B. (2015). Factors influencing the latency of simple reaction time. Frontiers in Human Neuroscience, 9, 131. doi:10.3389/fnhum.2015.00131.
   PubMed Web of Science ®Google Scholar