Dynamical analysis of bayesian inferencemodels for the eriksen task

Neural Computation

Volumn 21, Issue 6, 2009, Pages 1520-1553

  Liu, Yuan Sophie ^a,b   Yu, Angela ^a   Holmes, Philip ^c  

^a PRINCETON UNIVERSITY   (United States)

^b SALK INSTITUTE FOR BIOLOGICAL STUDIES   (United States)

^c Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; NONLINEAR SYSTEM; PHYSIOLOGY; PSYCHOMETRY; REACTION TIME; SENSORY RECEPTOR;

ALGORITHMS; ANIMALS; BAYES THEOREM; COMPUTER SIMULATION; HUMANS; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PSYCHOMETRICS; REACTION TIME; SENSORY RECEPTOR CELLS;

EID: 67651028080     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2009.03-07-495     Document Type: Article

References (35)

1. Bogacz, R., Brown, E., Moehlis, J., Holmes, P., & Cohen, J. (2006). The physics of optimal decision making: A formal analysis of models of performance in two alternative forced choice tasks. Psychological Review, 113(4), 700-765.
2. Bogacz, R., & Gurney, K. (2007). The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Computation, 19, 442-477.
3. Bogacz, R., Hu, P., Cohen, J., & Holmes, P. (2007). Do humans select the speed-accuracy tradeoff maximizing reward rate? Manuscript submitted for publication.
4. Cohen, J., Dunbar, K., & McClelland, J. (1990). On the control of automatic processes: A parallel distributed processing model of the Stroop effect. Psychological Review, 97(3), 332-361.
5. Cohen, J., Servan-Schreiber, D., & McClelland, J. (1992). A parallel distributed processing approach to automaticity. American Journal of Psychology, 105, 239-269.
6. Eckhoff, P., Holmes, P., Law, C., Connolly, P., & Gold, J. (2008). On diffusion processes with variable drift rates as models for decision making during learning. New Journal of Physics, doi 1367-2360/10/1/015006.
7. Eriksen, B., & Eriksen, C. (1974). Effects of noise letters upon the identification of a target letter in a non-search task. Perception and Psychophysics, 16, 143-149.
8. Gardiner, C. (1985). Handbook of stochastic methods (2nd ed.). New York: Springer.
9. Gold, J., & Shadlen, M. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Science, 5(1), 10-16.
10. Gold, J., & Shadlen, M. (2002). Banburismus and the brain: Decoding the relationship between sensory stimuli, decisions, and reward. Neuron, 36, 299-308.
11. Gratton, G., Coles, M. G. H., & Donchin, E. (1992). Optimizing the use of information: The strategic control of the activation of responses. J. Exp. Psych. General, 121, 480-506.
12. Gratton, G., Coles, M., Sirevaag, E., Eriksen, C., & Donchin, E. (1988). Preand poststimulus activation of response channels: A psychophysiological analysis. J. Exp. Psychol. Hum. Percept. Perform., 14, 331-344.
13. Higham, D. (2001). An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Rev., 43(3), 525-546.
14. Holmes, P., Shea-Brown, E., Moehlis, J., Bogacz, R., Gao, J., Aston-Jones, G., et al. (2005). Optimal decisions: From neural spikes, through stochastic differential equations, to behavior. IEICE Transactions on Fundamentals on Electronics, Communications and Computer Science, E88A(10), 2496-2503.
15. Laming, D. (1968). Information theory of choice-reaction times. Orlando, FL: Academic Press.
16. Liu, Y., & Blostein, S. (1992). Optimality of the sequential probability ratio test for nonstationary observations. IEEE Transactions on Information Theory, 38(1), 177-182.
17. Liu, Y., Holmes, P., & Cohen, J. (2008). A neural network model of the Eriksen task: Reduction, analysis, and data fitting. Neural Computation, 20, 345-373.
18. McMillen, T., & Holmes, P. (2006). The dynamics of choice among multiple alternatives. J. Math. Psych., 50, 30-57.
19. Oksendal, B. (2002). Stochastic differential equations. Berlin: Springer.
20. Platt, M., & Glimcher, P. (2001). Neural correlates of decision variable in parietal cortex. Nature, 400, 233-238.
21. Ratcliff, R. (1978). A theory of memory retrieval. Psych. Rev., 85, 59-108.
22. Ratcliff, R., & Smith, P. (2004). A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev., 111, 333-346.
23. Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psych. Rev., 106(2), 261-300.
24. Roitman, J., & Shadlen, M. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci., 22, 9475-9489.
25. Schall, J. (2001). Neural basis of deciding, choosing and acting. Nature Reviews in Neuroscience, 2, 33-42.
26. Schall, J., Stuphorn, V., & Brown, J. (2002). Monitoring and control of action by the frontal lobes. Neuron, 36, 309-322.
27. Servan-Schreiber, D., Bruno, R., Carter, C., & Cohen, J. (1998). Dopamine and the mechanisms of cognition: Part I. A neural network model predicting dopamine effects on selective attention. Biological Psychiatry, 43, 713-722.
28. Shadlen, M., & Newsome, W. (2001). Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiology, 86, 1916-1936.
29. Usher, M., & McClelland, J. (2001). On the time course of perceptual choice: The leaky competing accumulator model. Psych. Rev., 108, 550-592.
30. Wald, A. (1947). Sequential analysis. Hoboken, NJ: Wiley.
31. Wald, A., & Wolfowitz, J. (1948). Optimum character of the sequential probability ratio test. Ann. Math. Statist., 19, 326-339.
32. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36, 955-968.
33. Wong, K.-F., & Wang, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci., 26, 1314-1328.
34. Yu, A., Dayan, P., & Cohen, J. (in press). Dynamics of attentional selection under conflict: Toward a rational Bayesian account. J. Exp. Psych. Human Perception and Performance.
35. Yu, A., & Dayan, P. (2005). Inference, attention and decision in a Bayesian neural architecture. In L. Saul, W. Yair, & L. Bottou (Eds.), Advances in neural information processing systems, 17 (pp. 179-196). Cambridge, MA: MIT Press.