Informing cognitive abstractions through neuroimaging: The neural drift diffusion model

Psychological Review

Volumn 122, Issue 2, 2015, Pages 312-336

  Turner, Brandon M ^a   Van Maanen, Leendert ^b   Forstmann, Birte U ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Cognitive modeling; Default mode network; Evidence accumulation; Neural correlates; Neural drift diffusion model

Indexed keywords

BIOLOGICAL MODEL; BRAIN; FUNCTIONAL NEUROIMAGING; HUMAN; NERVE CELL NETWORK; PHYSIOLOGY; PROCEDURES; PSYCHOLOGICAL MODEL; PSYCHOMOTOR PERFORMANCE;

BRAIN; FUNCTIONAL NEUROIMAGING; HUMANS; MODELS, NEUROLOGICAL; MODELS, PSYCHOLOGICAL; NERVE NET; PSYCHOMOTOR PERFORMANCE;

EID: 84926337058     PISSN: 0033295X     EISSN: 19391471     Source Type: Journal    
DOI: 10.1037/a0038894     Document Type: Article

References (99)

1. Anderson, J. R., Betts, S., Ferris, J. L., & Fincham, J. M. (2010). Neural imaging to track mental states. Proceedings of the National Academy of Sciences, 107, 7018-7023.
2. Anderson, J. R., Carter, C. S., Fincham, J. M., Qin, Y., Ravizza, S. M., & Rosenberg-Lee, M. (2008). Using fMRI to test models of complex cognition. Cognitive Science, 32, 1323-1348.
3. Anderson, J. R., Fincham, J. M., Schneider, D. W., & Yang, J. (2012). Using brain imaging to track problem solving in a complex state space. NeuroImage, 60, 633-643.
4. Anderson, J. R., Qin, Y., Jung, K. J., & Carter, C. S. (2007). Informationprocessing modules and their relative modality specificity. Cognitive Psychology, 54, 185-217.
5. Bell, A., & Sejnowski, T. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
6. Bishop, C. M., & Lasserre, J. (2007). Generative or discriminative? Getting the best of both worlds. Bayesian Statistics, 8, 3-24.
7. Boehm, U., Van Maanen, L., Forstmann, B., & Van Rijn, H. (2014). Trial-by-trial fluctuations in CNV amplitude reflect anticipatory adjustment of response caution. NeuroImage, 96, 95-105.
8. Brown, S., & Heathcote, A. (2008). The simplest complete model of choice reaction time: Linear ballistic accumulation. Cognitive Psychology, 57, 153-178.
9. Calhoun, V., Adali, T., Pearlson, G., & Pekar, J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14, 140-151.
10. Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., Sherman, S. J., & Frank, M. J. (2011). Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold. Nature Neuroscience, 14, 1462-1467.
11. Christoff, K., Gordon, A. M., Smallwood, J., Smith, R., & Schooler, J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences, 106, 8719-8724.
12. Craigmile, P., Peruggia, M., & Zandt, T. V. (2010). Hierarchical Bayes models for response time data. Psychometrika, 75, 613-632.
13. Criss, A. J., Malmberg, K. J., & Shiffrin, R. M. (2011). Output interference in recognition memory. Journal of Memory and Language, 64, 316-326.
14. Danielmeier, C., Eichele, T., Forstmann, B. U., Tittgemeyer, M., & Ullsperger, M. (2011). Posterior medial frontal cortex activity predicts post-error adaptations in task-related visual and motor areas. Journal of Neuroscience, 31, 1780-1789.
15. DeCarlo, L. T. (2011). Signal detection theory with item effects. Journal of Mathematical Psychology, 55, 229-239.
16. de Lange, F. P., Jensen, O., & Dehaene, S. (2010). Accumulation of evidence during sequential decision making: The importance of topdown factors. Journal of Neuroscience, 30, 731-738.
17. de Lange, F. P., van Gaal, S., Lamme, V. A., & Dehaene, S. (2011). How awareness changes the relative weights of evidence during human decision making. PLoS Biology, 9, e1001203.
18. Donkin, C., Nosofsky, R. M., Gold, J. M., & Shiffrin, R. M. (2013). Discrete-slots models of visual working-memory response times. Psychological Review, 120, 873-902.
19. Eichele, T., Debener, S., Calhoun, V. D., Specht, K., Engel, A. K., Hugdahl, K.,... Ullsperger, M. (2008). Prediction of human errors by maladaptive changes in event-related brain networks. Proceedings of the National Academy of Sciences, 16, 6173-6178.
20. Fair, D. A., Cohen, A. L., Dosenbach, N. U., Church, J. A., Miezin, F. M., Barch, D. M.,... Schlaggar, B. L. (2008). The maturing architecture of the brain's default network. Proceedings of the National Academy of Sciences, 105, 4028-4032.
21. Feller, W. (1968). An introduction to probability theory and its applications (Vol. 1). New York, NY: John Wiley.
22. Fincham, J. M., Anderson, J. R., Betts, S. A., & Ferris, J. L. (2010). Using neural imaging and cognitive modeling to infer mental states while using and intelligent tutoring system. In Proceedings of the Third International Conference on Educational Data Mining (EDM2010). Pittsburgh, PA.
23. Forstmann, B. U., Anwander, A., Schäfer, A., Neumann, J., Brown, S., Wagenmakers, E.-J.,... Turner, R. (2010). Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proceedings of the National Academy of Sciences, 107, 15916-15920.
24. 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.
25. 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. Journal of Neuroscience, 31, 17242-17249.
26. Forstmann, B. U., & Wagenmakers, E. J. (2014). An introduction to model-based cognitive neuroscience. New York, NY: Springer.
27. Forstmann, B. U., Wagenmakers, E. J., Eichele, T., Brown, S., & Serences, J. T. (2011). Reciprocal relations between cognitive neuroscience and formal cognitive models: Opposites attract? Trends in Cognitive Sciences, 15, 272-279.
28. Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 102, 9673-9678.
29. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis. New York, NY: Chapman & Hall.
30. Gusnard, D. A., & Raichle, M. E. (2001). Searching for a baseline:Functional imaging and the resting human brain. Nature Reviews Neuroscience, 2, 685-694.
31. Hanes, D. P., & Schall, J. D. (1996). Neural control of voluntary movement initiation. Science, 274, 427-430.
32. Heathcote, A., Brown, S. D., & Mewhort, D. J. K. (2000). The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin and Review, 7, 185-207.
33. Himberg, J., Hyvarinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22, 1214-1222.
34. 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. Journal of Neuroscience, 32, 7992-8003.
35. Ho, T., Brown, S., & Serences, J. (2009). Domain general mechanisms of perceptual decision making in human cortex. Journal of Neuroscience, 29, 8675-8687.
36. Howard, M. W., & Kahana, M. J. (2002). A distributed representation of temporal context. Journal of Mathematical Psychology, 46, 269-299.
37. Jones, M., & Dzhafarov, E. N. (2014). Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time. Psychological Review, 121, 1-32.
38. Kac, M. (1962). A note on learning signal detection. IRE Transactions on Information Theory, 8, 126-128.
39. Kac, M. (1969). Some mathematical models in science. Science, 166, 695-699.
40. 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.
41. Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., ... Tervonen, O. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping, 30, 3865-3886.
42. Liu, T., & Pleskac, T. J. (2011). Neural correlates of evidence accumulation in a perceptual decision task. Journal of Neurophysiology, 106, 2383-2398.
43. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95, 492-527.
44. Mack, M. L., Preston, A. R., & Love, B. C. (2013). Decoding the brain's algorithm for categorization from its neural implementation. Current Biology, 23, 2023-2027.
45. Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. New York, NY:Freeman.
46. Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N. (2003). A role for neural integrators in perceptual decision making. Cerebral Cortex, 13, 1257-1269.
47. McClelland, J., & Rumelhart, D. (1986). Parallel distributed processing:Explorations in the microstructure of cognition (Vol. 2). Cambridge, MA: MIT Press.
48. Morey, R. D., Pratte, M. S., & Rouder, J. N. (2008). Problematic effects of aggregation in zROC analysis and a hierarchical modeling solution. Journal of Mathematical Psychology, 52, 376-388.
49. Mulder, M. J., Keuken, M. C., van Maanen, L., Boekel, W., Forstmann, B. U., & Wagenmakers, E. J. (2013). The speed and accuracy of perceptual decisions in a random-tone pitch task. Attention, Perception and Psychophysics, 75, 1048-1058.
50. Mulder, M. J., van Maanen, L., & Forstmann, B. U. (2014). Perceptual decision neurosciences: A model-based review. Neuroscience, 277, 872-884.
51. 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.
52. Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2012). Deconvolving bold activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59, 2636-2643.
53. Myung, I. J., & Pitt, M. (2002). Mathematical modeling. In H. Pashler & J. Wixted (Eds.), Stevens' handbook of experimental psychology (3rd ed., pp. 429-460). New York, NY: John Wiley.
54. Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53, 222-230.
55. O'Connell, R. G., Dockree, P. M., & Kelly, S. P. (2012). A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nature Neuroscience, 15, 1729-1737.
56. O'Doherty, J. P., Hampton, A., & Kim, H. (2007). Model-Based fMRI and its application to reward learning and decision making. Annals of the New York Academy of Science, 1104, 35-53.
57. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97-113.
58. O'Reilly, R. C. (2001). Generalization in interactive networks: The benefits of inhibitory competition and Hebbian learning. Neural Computation, 13, 1199-1242.
59. O'Reilly, R. C. (2006). Biologically based computational models of cortical cognition. Science, 314, 91-94.
60. O'Reilly, R. C., & Munakata, Y. (Eds.). (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press.
61. Peruggia, M., Van Zandt, T., & Chen, M. (2002). Was it a car or a cat I saw? An analysis of response times for word recognition. Case Studies in Bayesian Statistics, 6, 319-334.
62. Philiastides, M. G., Ratcliff, R., & Sajda, P. (2006). Neural representation of task difficult and decision making during perceptual categorization: A timing diagram. Journal of Neuroscience, 26, 8965-8975.
63. Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence diagnosis and output analysis for MCMC. R News, 6, 7-11. Retrieved from http://CRAN.R-project.org/doc/Rnews/
64. Pollack, R., & Devlin, J. T. (2007). On the fundamental role of anatomy in functional imaging: Reply to commentaries on 'In praise of tedious anatomy'. NeuroImage, 37, 1073-1082.
65. Polyn, S. M., Norman, K. A., & Kahana, M. J. (2009). A context maintenance and retrieval model of organizational processes in free recall. Psychological Review, 116, 129-156.
66. Pratte, M. S., & Rouder, J. N. (2011). Hierarchical single- and dual-process models of recognition memory. Journal of Mathematical Psychology, 55, 36-46.
67. Province, J. M., & Rouder, J. N. (2012). Evidence for discrete-state processing in recognition memory. Proceedings of the National Academy of Sciences, 109, 14357-14362.
68. Purcell, B., Heitz, R., Cohen, J., Schall, J., Logan, G., & Palmeri, T. (2010). Neurally-constrained modeling of perceptual decision making. Psychological Review, 117, 1113-1143.
69. Purcell, B., Schall, J., Logan, G., & Palmeri, T. (2012). Gated stochastic accumulator model of visual search decisions in FEF. Journal of Neuroscience, 32, 3433-3446.
70. Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98, 676-682.
71. Raichle, M. E., & Snyder, A. Z. (2007). A default mode of brain function:A brief history of an evolving idea. NeuroImage, 37, 1083-1090.
72. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108.
73. Ratcliff, R., Philiastides, M. G., & Sajda, P. (2009). Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proceedings of the National Academy of Sciences, 106, 6539-6544.
74. Ratcliff, R., & Smith, P. L. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychological Review, 111, 333-367.
75. Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Comparing connectionist and diffusion models of reaction time. Psychological Review, 106, 261-300.
76. Robert, C. P., & Casella, G. (2004). Monte Carlo statistical methods. New York, NY: Springer.
77. Sederberg, P. B., Howard, M. W., & Kahana, M. J. (2008). A contextbased theory of recency and contiguity in free recall. Psychological Review, 115, 893-912.
78. 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.
79. ter Braak, C. J. F. (2006). A Markov chain Monte Carlo version of the genetic algorithm Differential Evolution: Easy Bayesian computing for real parameter spaces. Statistics and Computing, 16, 239-249.
80. Todd, M. T., Nystrom, L. E., & Cohen, J. D. (2013). Confounds in multivariate pattern analysis: Theory and rule representation case study. NeuroImage, 77, 157-165.
81. Tosoni, A., Galati, G., Romani, G. L., & Corbetta, M. (2008). Sensorymotor mechanisms in human parietal cortex underlie arbitrary visual decisions. Nature Neuroscience, 11, 1446-1453.
82. Treisman, M., & Williams, T. (1984). A theory of criterion setting with an application to sequential dependencies. Psychological Review, 91, 68-111.
83. Turner, B. M. (in press). Constraining cognitive abstractions through Bayesian modeling. In B. U. Forstmann & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience. New York, NY:Springer.
84. Turner, B. M., Forstmann, B. U., Wagenmakers, E. J., Brown, S. D., Sederberg, P. B., & Steyvers, M. (2013). A Bayesian framework for simultaneously modeling neural and behavioral data. NeuroImage, 72, 193-206.
85. Turner, B. M., & Sederberg, P. B. (2012). Approximate Bayesian computation with Differential Evolution. Journal of Mathematical Psychology, 56, 375-385.
86. Turner, B. M., Sederberg, P. B., Brown, S., & Steyvers, M. (2013). A method for efficiently sampling from distributions with correlated dimensions. Psychological Methods, 18, 368-384.
87. Turner, B. M., Van Zandt, T., & Brown, S. D. (2011). A dynamic, stimulus-driven model of signal detection. Psychological Review, 118, 583-613.
88. Usher, M., & McClelland, J. L. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psychological Review, 108, 550-592.
89. Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2008). A Bayesian approach to diffusion process models of decision-making. In V. M. Sloutsky, B. C. Love, & K. McRae (Eds.), Proceedings of the 30rd annual conference of the cognitive science society (pp. 1429-1434). Austin, TX: Cognitive Science Society.
90. Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011). Hierarchical diffusion models for two-choice response time. Psychological Methods, 16, 44-62.
91. van Maanen, L., Brown, S. D., Eichele, T., Wagenmakers, E. J., Ho, T., & Serences, J. (2011). Neural correlates of trial-to-trial fluctuations in response caution. Journal of Neuroscience, 31, 17488-17495.
92. 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 Neuroscience, 6, 1-13.
93. Vickers, D., & Lee, M. (1998). Dynamic models of simple judgments: I. Properties of a self-regulating accumulator module. Nonlinear Dynamics, Psychology, and Life Sciences, 2, 169-194.
94. Vickers, D., & Lee, M. (2000). Dynamic models of simple judgements: II. Properties of a self-organizing PAGAN (Parallel, Adaptive, Generalized Accumulator Network) model for multi-choice tasks. Nonlinear Dynamics, Psychology, and Life Sciences, 4, 1-31.
95. Waldorp, L., Christoffels, I., & van de Ven, V. (2011). Effective connectivity of fMRI data using ancestral graph theory: Dealing with missing regions. NeuroImage, 54, 2695-2705.
96. Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9, 971-978.
97. Wiecki, T. V., Sofer, I., & Frank, M. J. (2013). HDDM: Hierarchical Bayesian estimation of the drift-diffusion model in Python. Frontiers in Neuroinformatics, 7, 14.
98. Winkel, J., van Maanen, L., Ratcliff, R., van der Schaaf, M., Van Schouwenburg, M., Cools, R., & Forstmann, B. U. (2012). Bromocriptine does not alter speed-accuracy tradeoff. Frontiers in Decision Neuroscience, 6, 126.
99. Zandbelt, B. B., Purcell, B. A., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2014). Response times from ensembles of accumulators. Proceedings of the National Academy of Sciences, 111, 2848-2853.