Dynamics of multiple-choice decision making

Neural Computation

Volumn 25, Issue 8, 2013, Pages 2108-2145

  You, Hongzhi ^a,b   Wang, Da Hui ^a  

^a BEIJING NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; DECISION MAKING; HUMAN; LETTER; MOTION; NERVE CELL; NONLINEAR SYSTEM; PHYSIOLOGY; REACTION TIME; TIME;

ACTION POTENTIALS; ANIMALS; CHOICE BEHAVIOR; COMPUTER SIMULATION; DECISION MAKING; HUMANS; MODELS, NEUROLOGICAL; MOTION; NEURONS; NONLINEAR DYNAMICS; REACTION TIME; TIME FACTORS;

EID: 84881003063     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00473     Document Type: Letter

References (42)

1. Abbott, L. F., & Chance, E. S. (2005). Drivers and modulators from push-pull and balanced synaptic input. Progress in Brain Research, 149, 147-155.
2. Albantakis, L., & Deco, G. (2009). The encoding of alternatives in multiple-choice decision making. Proc. Nat. Acad. Sci. USA, 106, 10308-10313.
3. Amit, D. J., Bernacchia, A., & Yakovlev, V. (2003). Multiple-object working memory: A model for behavioral performance. Cerebral Cortex, 13, 435-443.
4. Bogacz, R., Usher, M., Zhang, J., & McClelland, J. L. (2007). Extending a biologically inspired model of choice: Multi-alternatives, nonlinearity and value-based multidimensional choice. Phil. Trans. R. Soc. B, 362, 1655-1670.
5. Bollimunta, A., Totten, D., & Ditterich, J. (2012). Neural dynamics of choice: Singletrial analysis of decision-related activity in parietal cortex. Journal of Neuroscience, 32(37), 12684-12701.
6. Brunel, N., & Wang, X-J. (2001). Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition. J. Comput. Neurosci., 11, 63-85.
7. Churchland, A.K., Kiani, R.,&Shadlen, M. N. (2008).Decision-makingwithmultiple alternatives. Nature Neuroscience, 11, 693-702.
8. Compte, A., Brunel, N., Goldman-Rakic, P. S., & Wang, X.-J. (2000). Synaptic mechanisms and network dynamics underlying visuospatial working memory in a cortical network model. Cerebral Cortex, 10, 910-923.
9. Constantinidis, C., & Steinmetz, M. (1996). Neuronal activity in posterior parietal area 7a during the delay periods of a spatial memory task. Journal of Neurophysiology, 76, 1352-1355.
10. Constantinidis, C., Franowicz, M. N., & Goldman-Rakic, P. S. (2001). Coding specificity in cortical microcircuits:Amultiple-electrode analysis of primate prefrontal cortex. Journal of Neuroscience, 21, 3646-3655.
11. Deco, G.,&Rolls, E. (2006).Decision-making andWeber's law:Aneurophysiological model. Eur. J. Neurosci., 24, 901-916.
12. Deco, G., Scarano, L., & Soto-Faraco, S. (2007). Weber's law in decision making: Integrating behavioral data in humans with a neurophysiological model. Journal of Neuroscience, 27, 11192-11200.
13. Destexhe, A., Rudolph, M., Fellous, J. M., & Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107, 13-24.
14. Ditterich, J. (2010). A comparison between mechanisms of multi-alternative perceptual decision making: Ability to explain human behavior, predictions for neurophysiology, and relationship with decision theory. Frontiers in Neuroscience, 4, 184.
15. Edin, F., Klingberg, T., Johansson, P., McNab, F., Tegner, J., & Compte, A. (2009). Mechanism for top-down control of working memory capacity. Proc. Nat. Acad. Sci. USA, 106, 6802-6807.
16. Fung, C. C., Wong, K. Y., & Wu, S. (2008). Dynamics of neural networks with continous attractors. Europhys. Lett., 84, 18002.
17. Fung, C. C.,Wong, K. Y., &Wu, S. (2010). A moving bump in a continuousmanifold: A comprehensive study of the tracking dynamics of continuous attractor neural networks. Neural Computation, 22, 752-792.
18. Furman, M., & Wang, X.-J. (2008). Similarity effect and optimal control of multiplechoice decision making. Neuron, 60, 1153-1168.
19. Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D. (2008). One-dimensional dynamics of attention and decision making in LIP. Neuron, 58, 15-25.
20. Gillespie, D. T. (1996). The mathematics of Brownian motion and Johnson noise. American Journal of Physics, 64, 225-240.
21. Gold, J., & Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci., 30, 535-574.
22. Kable, J.W., & Glimcher, P.W. (2009). The neurobiology of decision: Consensus and controversy. Neuron., 63, 733-745.
23. Kim, J. N.,&Shadlen, M. N. (1999). Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nature Neuroscience, 2, 176-185.
24. Liu, F., & Wang, X.-J. (2008). A common cortical circuit mechanism for perceptual categorical discrimination and veridical judgment. PLoS Comput Biol, 4, e1000253.
25. McMillen, T., & Holmes, P. (2006). The dynamics of choice among multiple alternatives. Journal of Mathematical Psychology, 50, 30-57.
26. Miller, P., & Katz, D. B. (2010). Stochastic transitions between neural states in taste processing and decision-making. Journal of Neuroscience, 30, 2559-2570.
27. Niwa,M., & Ditterich, J. (2008). Perceptual decisions between multiple directions of visual motion. Journal of Neuroscience, 28, 4435-4445.
28. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1992). Numerical recipes in C: The art of scientific computing (2nd ed.). Cambridge: Cambridge University Press.
29. 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.
30. Romo, R., & Salinas, E. (2001). Touch and go: Decision-making mechanisms in somatosensation. Annu. Rev. Neurosci., 24, 107-137.
31. Shadlen, M. N., & Gold, J. (2004). The neurophysiology of decision-making as a window on cognition. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (3rd ed.). Cambridge, MA: MIT Press.
32. Smith, P. L. (2000). Stochastic dynamic models of response time and accuracy: A foundational primer. Journal of Mathematical Psychology, 44, 408-463.
33. Strogatz, S. (2001). Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering. Boulder, CO: Westview Press.
34. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36, 955-968.
35. Wang, X.-J. (2008). Decision making in recurrent neuronal circuits. Neuron, 60, 215- 234.
36. Wei, Z.,Wang, X.-J.,&Wang, D.-H. (2012). Fromdistributed resources to limited slots in multiple-item working memory:Aspiking network model with normalization. Journal of Neuroscience, 32, 11228-11240.
37. Wilimzig,C., Schneider, S.,&Schoner, G. (2006). The time course of saccadic decision making: Dynamic field theory. Neural Networks, 19, 1059-1074.
38. Wilson, H., & Cowan, J. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1-24.
39. Wilson, H., & Cowan, J. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13, 55-80.
40. Wong,K.-F.,&Wang,X.-J. (2006).Arecurrent networkmechanism of time integration in perceptual decisions. Journal of Neuroscience, 26, 1314-1328.
41. Wong,K.-F.,Huk, A.C., Shadlen, M. N.,&Wang,X.-J. (2007).Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision making. Neuroscience, 1, 1-11.
42. You, H.,Meng,Y.,Huan, D.,&Wang, D.-H. (2011).The neural dynamics for hysteresis in visual perception. Neurocomputing, 74, 3502-3508.