Reciprocal relations between cognitive neuroscience and formal cognitive models: Opposites attract?

Trends in Cognitive Sciences

Volumn 15, Issue 6, 2011, Pages 272-279

  Forstmann, Birte U ^a   Wagenmakers, Eric Jan ^a   Eichele, Tom ^b   Brown, Scott ^c   Serences, John T ^d  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b UNIVERSITY OF BERGEN   (Norway)

^c UNIVERSITY OF NEWCASTLE   (Australia)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL DATA; BRAIN MEASUREMENT; COGNITIVE MODEL; COGNITIVE NEUROSCIENCES; COGNITIVE PROCESS; FORMAL MODEL; NEURAL MECHANISMS; RECIPROCAL RELATIONS;

COGNITIVE SYSTEMS; DATA REDUCTION; DECISION MAKING; NEUROPHYSIOLOGY;

BRAIN MODELS;

BEHAVIOR; BRAIN FUNCTION; COGNITION; DECISION MAKING; HUMAN; INTROSPECTION; LEARNING; NEUROSCIENCE; PERCEPTION; REVIEW;

ANIMALS; BRAIN; COGNITION; HUMANS; MODELS, BIOLOGICAL; NEUROSCIENCES;

EID: 79957858166     PISSN: 13646613     EISSN: 1879307X     Source Type: Journal    
DOI: 10.1016/j.tics.2011.04.002     Document Type: Review

References (76)

1. Corrado G., Doya K. Understanding neural coding through the model-based analysis of decision making. J. Neurosci. 2007, 27:8178-8180.
2. Dolan R.J. Neuroimaging of cognition: past, present, and future. Neuron 2008, 60:496-502.
3. Friston K.J. Modalities, modes, and models in functional neuroimaging. Science 2009, 326:399-403.
4. Gold J.I., Shadlen M.N. Neural computations that underlie decisions about sensory stimuli. Trends Cogn. Sci. 2001, 5:10-16.
5. Mars R.B., et al. Model-based analyses: promises, pitfalls, and example applications to the study of cognitive control. Q. J. Exp. Psychol. (Colchester) 2010, 1-16.
6. O'Doherty J.P., et al. Model-based fMRI and its application to reward learning and decision making. Ann. N.Y. Acad. Sci. 2007, 1104:35-53.
7. Hanes D.P., Schall J.D. Neural control of voluntary movement initiation. Science 1996, 274:427-430.
8. Celebrini S., Newsome W.T. Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. J. Neurosci. 1994, 14:4109-4124.
9. Geisler W.S. Ideal-observer analysis of visual discrimination. In Frontiers of Visual Science: Proceedings of the 1985 Symposium 1987, 17-31. National Academy Press. Committee on Vision (Ed.).
10. Ress D., Heeger D.J. Neuronal correlates of perception in early visual cortex. Nat. Neurosci. 2003, 6:414-420.
11. Ditterich J. A comparison between mechanisms of multi-alternative perceptual decision making: ability to explain human behavior, predictions for neurophysiology, and relationship with decision theory. Front. Neurosci. 2010, 4:184.
12. Schall J.D. On building a bridge between brain and behavior. Annu. Rev. Psychol. 2004, 55:23-50.
13. Green D.M., Swets J.A. Signal detection theory and psychophysics 1966, Wiley.
14. Anderson J.R., et al. An integrated theory of the mind. Psychol. Rev. 2004, 111:1036-1060.
15. Anderson J.R. How can the human mind occur in the physical universe? 2007, Oxford University Press.
16. Donkin C., et al. Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes?. Psychon. Bull. Rev. 2011, 18:61-69.
17. Grafton S.T., Tunik E. Human basal ganglia and the dynamic control of force during on-line corrections. J. Neurosci. 2011, 31:1600-1605.
18. Brown S.D., Heathcote A. The simplest complete model of choice response time: linear ballistic accumulation. Cogn. Psychol. 2008, 57:153-178.
19. Forstmann B.U., et al. The neural substrate of prior information in perceptual decision making: a model-based analysis. Front. Hum. Neurosci. 2010, 4:40.
20. Lauwereyns J. The Anatomy of Bias: How Neural Circuits Weigh the Options 2010, MIT Press.
21. Gold J.I., Shadlen M.N. The neural basis of decision making. Annu. Rev. Neurosci. 2007, 30:535-574.
22. Sevy S., et al. Emotion-based decision-making in healthy subjects: short-term effects of reducing dopamine levels. Psychopharmacology (Berl.) 2006, 188:228-235.
23. Yechiam E., et al. Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychol. Sci. 2005, 16:973-978.
24. Forstmann B.U., et al. Striatum and pre-SMA facilitate decision-making under time pressure. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:17538-17542.
25. Forstmann B.U., et al. Cortico-striatal connections predict control over speed and accuracy in perceptual decision making. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:15916-15920.
26. Behrens T.E., et al. Associative learning of social value. Nature 2008, 456:245-249.
27. Dayan P., Daw N.D. Decision theory, reinforcement learning, and the brain. Cogn. Affect. Behav. Neurosci. 2008, 8:429-453.
28. Daw N.D., et al. Cortical substrates for exploratory decisions in humans. Nature 2006, 441:876-879.
29. Noppeney U., et al. Perceptual decisions formed by accumulation of audiovisual evidence in prefrontal cortex. J. Neurosci. 2010, 30:7434-7446.
30. Yu A.J., et al. Dynamics of attentional selection under conflict: toward a rational Bayesian account. J. Exp. Psychol. Hum. Percept. Perform. 2009, 35:700-717.
31. Borst J.P., et al. The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking. PLoS ONE 2010, 5:e12966.
32. Anderson J.R., Qin Y. Using brain imaging to extract the structure of complex events at the rational time band. J. Cogn. Neurosci. 2008, 20:1624-1636.
33. Anderson J.R., et al. Using fMRI to test models of complex cognition. Cogn. Sci. 2008, 32:1323-1348.
34. Ratcliff R., et al. Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:6539-6544.
35. Ashby F.G., et al. Cortical and basal ganglia contributions to habit learning and automaticity. Trends Cogn. Sci. 2010, 14:208-215.
36. Stocco A., et al. Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination. Psychol. Rev. 2010, 117:541-574.
37. Churchland A.K., et al. Decision-making with multiple alternatives. Nat. Neurosci. 2008, 11:693-702.
38. Usher M., et al. Hick's law in a stochastic race model with speed-accuracy tradeoff. J. Math. Psychol. 2002, 46:704-715.
39. Purcell B.A., et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 2010, 117:1113-1143.
40. Thomas N.W., Pare M. Temporal processing of saccade targets in parietal cortex area LIP during visual search. J. Neurophysiol. 2007, 97:942-947.
41. Balan P.F., et al. Neuronal correlates of the set-size effect in monkey lateral intraparietal area. PLoS Biol. 2008, 6:e158.
42. Ipata A.E., et al. Activity in the lateral intraparietal area predicts the goal and latency of saccades in a free-viewing visual search task. J. Neurosci. 2006, 26:3656-3661.
43. Ratcliff R., et al. Dual diffusion model for single-cell recording data from the superior colliculus in a brightness-discrimination task. J. Neurophysiol. 2007, 97:1756-1774.
44. Teller D.Y. Linking propositions. Vision Res. 1984, 24:1233-1246.
45. Lo C.C., et al. Proactive inhibitory control and attractor dynamics in countermanding action: a spiking neural circuit model. J. Neurosci. 2009, 29:9059-9071.
46. Smith P.L., Ratcliff R. Psychology and neurobiology of simple decisions. Trends Neurosci. 2004, 27:161-168.
47. Siegel M., et al. Phase-dependent neuronal coding of objects in short-term memory. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:21341-21346.
48. Pesaran B., et al. Free choice activates a decision circuit between frontal and parietal cortex. Nature 2008, 453:406-409.
49. de Lange F.P., et al. Accumulation of evidence during sequential decision making: the importance of top-down factors. J. Neurosci. 2010, 30:731-738.
50. Krajbich I., et al. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 2010, 13:1292-1298.
51. Resulaj A., et al. Changes of mind in decision-making. Nature 2009, 461:263-266.
52. Wetzels R., et al. Bayesian inference using WBDev: a tutorial for social scientists. Behav. Res. Methods 2010, 42:884-897.
53. Vanderkerckhove J., et al. Hierarchical diffusion models for two-choice response times. Psychol. Methods 2011, 16:44-62.
54. Sugrue L.P., et al. Matching behavior and the representation of value in the parietal cortex. Science 2004, 304:1782-1787.
55. Corrado G.S., et al. Linear-nonlinear-Poisson models of primate choice dynamics. J. Exp. Anal. Behav. 2005, 84:581-617.
56. Serences J.T. Value-based modulations in human visual cortex. Neuron 2008, 60:1169-1181.
57. Lau B., Glimcher P.W. Dynamic response-by-response models of matching behavior in rhesus monkeys. J. Exp. Anal. Behav. 2005, 84:555-579.
58. Serences J.T., Saproo S. Population response profiles in early visual cortex are biased in favor of more valuable stimuli. J. Neurophysiol. 2010, 104:76-87.
59. Maia T.V., Frank M.J. From reinforcement learning models to psychiatric and neurological disorders. Nat. Neurosci. 2011, 14:154-162.
60. Waldorp L., et al. Effective connectivity of fMRI data using ancestral graph theory: dealing with missing regions. Neuroimage 2011, 54:2695-2705.
61. Eichele T., et al. Prediction of human errors by maladaptive changes in event-related brain networks. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:6173-6178.
62. Wallsten T.S., et al. Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol. Rev. 2005, 112:862-880.
63. Ratcliff R., McKoon G. The diffusion decision model: theory and data for two-choice decision tasks. Neural. Comput. 2008, 20:873-922.
64. Nosofsky R.M. Attention, similarity, and the identification-categorization relationship. J. Exp. Psychol. Gen. 1986, 115:39-61.
65. Batchelder W.H., Riefer D.M. Separation of storage and retrieval factors in free recall of clusterable pairs. Psychol. Rev. 1980, 87:375-397.
66. Klauer K.C., et al. The abstract selection task: new data and an almost comprehensive model. J. Exp. Psychol. Learn. Mem. Cogn. 2007, 33:680-703.
67. Jacoby L.L. A process dissociation framework: separating automatic from intentional uses of memory. J. Mem. Lang. 1991, 30:513-541.
68. Rouder J.N., et al. A hierarchical approach for fitting curves to response time measurements. Psychon. Bull. Rev. 2008, 15:1201-1208.
69. Tversky A., Kahneman D. Advances in prospect theory: cumulative respresentations of uncertainty. J. Risk Uncertain. 1992, 5:297-323.
70. Kahneman D., Tversky A. Prospect theory: an analysis of decision under risk. Econometrica 1979, 47:263-291.
71. Wakker P.P. Prospect Theory: For Risk and Ambiguity 2010, Cambridge University Press.
72. Shiffrin R.M. Modeling memory and perception. Cogn. Sci. 2003, 27:341-378.
73. Shiffrin R.M., Steyvers M. A model for recognition memory: REM-retrieving effectively from memory. Psychon. Bull. Rev. 1997, 4:145-166.
74. Van Ravenzwaaij D., et al. Cognitive model decomposition of the BART: assessment and application. J. Math. Psychol. 2011, 55:94-105.
75. Shuler M.G., Bear M.F. Reward timing in the primary visual cortex. Science 2006, 311:1606-1609.
76. Wittmann M., Paulus M.P. Decision making, impulsivity and time perception. Trends Cogn. Sci. 2008, 12:7-12.