Bayesian just-so stories in psychology and neuroscience

Psychological Bulletin

Volumn 138, Issue 3, 2012, Pages 389-414

  Bowers, Jeffrey S ^a   Davis, Colin J ^b  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Bayes; Bayesian; Heuristics; Just so stories; Optimal

Indexed keywords

ARTICLE; BAYES THEOREM; BRAIN; EVOLUTION; HUMAN; MENTAL FUNCTION; MOTOR PERFORMANCE; NEUROSCIENCE; PHYSIOLOGY; PSYCHOLOGICAL MODEL; PSYCHOLOGICAL THEORY; PSYCHOLOGY;

BAYES THEOREM; BIOLOGICAL EVOLUTION; BRAIN; HUMANS; MENTAL PROCESSES; MODELS, PSYCHOLOGICAL; MOTOR SKILLS; NEUROSCIENCES; PSYCHOLOGICAL THEORY; PSYCHOLOGY;

EID: 84864523394     PISSN: 00332909     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0026450     Document Type: Article

References (187)

1. Alais, D., & Burr, D. (2004). The ventriloquist effect results from nearoptimal bimodal integration. Current Biology, 14, 257-262. doi: 10.1016/j.cub.2004.01.029
2. Amarasingham, A., Chen, T.-L., Geman, S., Harrison, M. T., & Sheinberg, D. L. (2006). Spike count reliability and the Poisson hypothesis. Journal of Neuroscience, 26, 801-809. doi:10.1523/JNEUROSCI.2948-05.2006
3. Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471-485. doi:10.1017/S0140525X00070801
4. Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2, 396-408. doi:10.1111/j.14679280.1991.tb00174.x
5. Balota, D. A., & Neely, J. H. (1980). Test-expectancy and word-frequency effects in recall and recognition. Journal of Experimental Psychology: Human Learning and Memory, 6, 576-587. doi:10.1037/02787393.6.5.576
6. Bar-Eli, M., Azar, O. H., & Lurie, Y. (2009). (Ir)rationality in action: Do soccer players and goalkeepers fail to learn how to best perform during a penalty kick? Progress in Brain Research, 174, 97-108. doi:10.1016/ S0079-6123(09)01309-0
7. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge, England: Cambridge University Press.
8. 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, 1142-1152. doi:10.1016/j.neuron.2008.09.021
9. Becker, G. M. (1991). The nonoptimality of Anderson's memory fits. Behavioral and Brain Sciences, 14, 487-488. doi:10.1017/ S0140525X00070837
10. Bogacz, R. (2007). Optimal decision-making theories: Linking neurobiology with behaviour. Trends in Cognitive Sciences, 11, 118-125. doi: 10.1016/j.tics.2006.12.006
11. Bogacz, R., Hu, P. T., Holmes, P. J., & Cohen, J. D. (2010). Do humans produce the speed-accuracy trade-off that maximizes reward rate? Quarterly Journal of Experimental Psychology, 63, 863-891. doi: 10.1080/17470210903091643
12. Bowers, J. S. (2009). On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience. Psychological Review, 116, 220-251. doi:10.1037/a0014462
13. Bowers, J. S. (2010a). Does masked and unmasked priming reflect Bayesian inference as implemented in the Bayesian reader? European Journal of Cognitive Psychology, 22, 779-797. doi:10.1080/ 09541440902955900
14. Bowers, J. S. (2010b). What are the Bayesian constraints in the Bayesian reader? Reply to Norris and Kinoshita (2010). European Journal of Cognitive Psychology, 22, 1270-1273. doi:10.1080/09541446 .2010.532120
15. Bowers, J. S., Damian, M. F., & Davis, C. J. (2009). A fundamental limitation of the conjunctive codes learned in PDP models of cognition: Comment on Botvinick and Plaut (2006). Psychological Review, 116, 986-997. doi:10.1037/a0017097
16. Bowers, J. S., & Davis, C. J. (2009). Learning representations of wordforms with recurrent networks: Comment on Sibley, Kello, Plaut, & Elman (2008). Cognitive Science, 33, 1183-1186. doi:10.1111/j.15516709.2009.01062.x
17. Bowers, J. S., & Davis, C. J. (2011). More varieties of Bayesian theories, but no enlightenment. Behavioral and Brain Sciences, 34, 193-194. doi:10.1017/S0140525X11000227
18. Bowers, J. S., Davis, C. J., & Hanley, D. A. (2005). Automatic semantic activation of embedded words: Is there a "hat" in "that"? Journal of Memory and Language, 52, 131-143. doi:10.1016/j.jml.2004.09.003
19. Bowers, J. S., Mattys, S. L., & Gage, S. H. (2009). Preserved implicit knowledge of a forgotten childhood language. Psychological Science, 20, 1064-1069. doi:10.1111/j.1467-9280.2009.02407.x
20. Brighton, H., & Gigerenzer, G. (2008). Bayesian brains and cognitive mechanisms: Harmony or dissonance? In N. Chater & M. Oaksford (Eds.), The probabilistic mind: Prospects for Bayesian cognitive science (pp. 189-208). New York, NY: Oxford University Press.
21. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996). A relationship between behavioral choice and the visual responses of neurons in macaque MT. Visual Neuroscience, 13, 87-100. doi:10.1017/S095252380000715X
22. Brysbaert, M., Lange, M., & Van Wijnendaele, I. (2000). The effects of age-of-acquisition and frequency-of-occurrence in visual word recognition: Further evidence from the Dutch language. European Journal of Cognitive Psychology, 12, 65-85. doi:10.1080/095414400382208
23. Burr, D., Banks, M. S., & Morrone, M. C. (2009). Auditory dominance over vision in the perception of interval duration. Experimental Brain Research, 198, 49-57. doi:10.1007/s00221-009-1933-z
24. Chater, N. (2000). How smart can simple heuristics be? Behavioral and Brain Sciences, 23, 745-746. doi:10.1017/S0140525X00263449
25. Chater, N., & Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in Cognitive Sciences, 3, 57-65. doi:10.1016/S13646613(98)01273-X
26. Chater, N., Oaksford, M., Hahn, U., & Heit, E. (2010). Bayesian models of cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 1, 811- 823. doi:10.1002/wcs.79
27. Chater, N., Tenenbaum, J. B., & Yuille, A. (2006). Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences, 10, 287-291. doi:10.1016/j.tics.2006.05.007
28. Chen, Y., Geisler, W. S., & Seidemann, E. (2006). Optimal decoding of correlated neural population responses in the primate visual cortex. Nature Neuroscience, 9, 1412-1420. doi:10.1038/nn1792
29. Chklovskii, D. B., & Koulakov, A. A. (2004). Maps in the brain: What can we learn from them? Annual Review of Neuroscience, 27, 369-392. doi:10.1146/annurev.neuro.27.070203.144226
30. Davis, C. J. (2006). Orthographic input coding: A review of behavioural evidence and current models. In S. Andrews (Ed.), From inkmarks to ideas: Current issues in lexical processing (pp. 180-206). New York, NY: Psychology Press.
31. Davis, C. J. (2010). The spatial coding model of visual word identification. Psychological Review, 117, 713-758. doi:10.1037/a0019738
32. Davis, C. J., Perea, M., & Acha, J. (2009). Re(de)fining the orthographic neighborhood: The role of addition and deletion neighbors in lexical decision and reading. Journal of Experimental Psychology: Human Perception and Performance, 35, 1550-1570. doi:10.1037/a0014253
33. Dawkins, R. (1982). The extended phenotype: The gene as the unit of selection. San Francisco, CA: Freeman.
34. Dennett, D. (1987). The international stance. Cambridge, MA: MIT Press.
35. DeWeese, M. R., Wehr, M., & Zador, A. M. (2003). Binary spiking in auditory cortex. Journal of Neuroscience, 23, 7940-7949.
36. Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 249-267). New York, NY: Cambridge University Press.
37. Ellis, A. W., & Brysbaert, M. (2010). Split fovea theory and the role of the two cerebral hemispheres in reading: A review of the evidence. Neuropsychologia, 48, 353-365. doi:10.1016/j.neuropsychologia.2009.08.021
38. Ellis, A. W., & Lambon Ralph, M. A. (2000). Age of acquisition effects in adult lexical processing reflect loss of plasticity in maturing systems: Insights from connectionist networks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1103-1123. doi:10.1037/ 0278-7393.26.5.1103
39. Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415, 429-433. doi:10.1038/415429a
40. Estes, W. K. (1964). Probability learning. In A. W. Melton (Ed.), Categories of human learning (pp. 89-128). New York, NY: Academic Press.
41. Evans, J. St. B. T, & Over, D. E. (2004). If. Oxford, England: Oxford University Press. doi:10.1093/acprof:oso/9780198525134.001.0001
42. Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9, 292-303. doi:10.1038/ nrn2258
43. Feldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009). The influence of categories on perception: Explaining the perceptual magnet effect as optimal statistical inference. Psychological Review, 116, 752-782. doi: 10.1037/a0017196
44. Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3-71. doi:10.1016/00100277(88)90031-5
45. Gaissmaier, W., & Schooler, L. J. (2008). The smart potential behind probability matching. Cognition, 109, 416-422. doi:10.1016/ j.cognition.2008.09.007
46. Geisler, W. S. (2011). Contributions of ideal observer theory to vision research. Vision Research, 51, 771-781. doi:10.1016/ j.visres.2010.09.027
47. Geisler, W. S., & Diehl, R. L. (2003). A Bayesian approach to the evolution of perceptual and cognitive systems. Cognitive Science, 27, 379-402. doi:10.1207/s15516709cog2703_3
48. Geisler, W. S., & Ringach, D. (2009). Natural systems analysis. Visual Neuroscience, 26, 1-3. doi:10.1017/S0952523808081005
49. Gibson, J. J. (1950). The perception of the visual world. Cambridge, MA: Riverside Press.
50. Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1, 107-143. doi:10.1111/j.1756-8765.2008.01006.x
51. Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8, 53-96. doi: 10.1111/j.1539-6053.2008.00033.x
52. Gigerenzer, G., & Selten, R. (2001). Rethinking rationality. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality: The adaptive toolbox (pp. 1-12). Cambridge, MA: MIT Press.
53. Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple heuristics that make us smart. New York, NY: Oxford University Press.
54. Godfrey-Smith, P. (2001). Three kinds of adaptationism. In S. H. Orzack & E. Sober (Eds.), Adaptationism and optimality (pp. 335-357). Cambridge, England: Cambridge University Press. doi:10.1017/ CBO9780511609084.012
55. Gold, J. M., Tadin, D., Cook, S. C., & Blake, R. (2008). The efficiency of biological motion perception. Perception & Psychophysics, 70, 88-95. doi:10.3758/PP.70.1.88
56. Goldstein, D. G., & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90. doi: 10.1037/0033-295X.109.1.75
57. Gomez, P., Ratcliff, R., & Perea, M. (2008). A model of letter position coding: The overlap model. Psychological Review, 115, 577-600. doi: 10.1037/a0012667
58. Grainger, J., Granier, J.-P., Farioli, F., Van Assche, E., & van Heuven, W. J. B. (2006). Letter position information and printed word perception: The relative-position priming constraint. Journal of Experimental Psychology: Human Perception and Performance, 32, 865-884. doi: 10.1037/0096-1523.32.4.865
59. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. doi: 10.1016/j.tics.2010.05.004
60. Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2008). Bayesian models of cognition. In Ron Sun (Ed.), Cambridge handbook of computational cognitive modeling (pp. 59-100). Cambridge, England: Cambridge University Press.
61. Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17, 767-773. doi:10.1111/ j.1467-9280.2006.01780.x
62. Grossberg, S. (1980). How does the brain build a cognitive code? Psychological Review, 87, 1-51. doi:10.1037/0033-295X.87.1.1
63. Grossberg, S. (1987a). Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11, 23-63. doi:10.1016/S03640213(87)80025-3
64. Grossberg, S. (1987b). Cortical dynamics of three-dimensional form, color, and brightness perception: I. Monocular theory. Perception & Psychophysics, 41, 87-116. doi:10.3758/BF03204874
65. Grossberg, S., & Pilly, P. K. (2008). Temporal dynamics of decisionmaking during motion perception in the visual cortex. Vision Research, 48, 1345-1373. doi:10.1016/j.visres.2008.02.019
66. Gu, Y., DeAngelis, G. C., & Angelaki, D. E. (2007). A functional link between area MSTd and heading perception based on vestibular signals. Nature Neuroscience, 10, 1038-1047. doi:10.1038/nn1935
67. Guenther, F. H., & Gjaja, M. N. (1996). The perceptual magnet effect as an emergent property of neural map formation. Journal of the Acoustical Society of America, 100, 1111-1121. doi:10.1121/1.416296
68. Gur, M., Beylin, A., & Snodderly, D. M. (1997). Response variability of neurons in primary visual cortex (V1) of alert monkeys. Journal of Neuroscience, 17, 2914-2920.
69. Gur, M., & Snodderly, D. M. (2006). High response reliability of neurons in primary visual cortex (V1) of alert, trained monkeys. Cerebral Cortex, 16, 888-895. doi:10.1093/cercor/bhj032
70. Hahn, U., & Warren, P. A. (2009). Perceptions of randomness: Why three heads are better than four. Psychological Review, 116, 454-461. doi: 10.1037/a0015241
71. Hammett, S. T., Champion, R. A., Thompson, P. G., & Morland, A. B. (2007). Perceptual distortions of speed at low luminance: Evidence inconsistent with a Bayesian account of speed encoding. Vision Research, 47, 564-568. doi:10.1016/j.visres.2006.08.013
72. Hemmer, P., & Steyvers, M. (2009). Integrating episodic memories and prior knowledge at multiple levels of abstraction. Psychonomic Bulletin & Review, 16, 80-87. doi:10.3758/PBR.16.1.80
73. Holyoak, K. J., & Hummel, J. E. (2000). The proper treatment of symbols in a connectionist architecture. In E. Deitrich & A. B. Markman (Eds.), Cognitive dynamics: Conceptual and representational change in humans and machines (pp. 229-263). Mahwah, NJ: Erlbaum.
74. Hummel, J. E., & Holyoak, K. J. (1997). Distributed representations of structure: A theory of analogical access and mapping. Psychological Review, 104, 427-466. doi:10.1037/0033-295X.104.3.427
75. Huttenlocher, J., Hedges, L. V., & Vevea, J. L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129, 220-241. doi:10.1037/0096-3445.129.2.220
76. Jazayeri, M. (2008). Probabilistic sensory recoding. Current Opinion in Neurobiology, 18, 431-437. doi:10.1016/j.conb.2008.09.00
77. Johnson, J. S., & Newport, E. L. (1989). Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology, 21, 60-99. doi:10.1016/0010-0285(89)90003-0
78. Jones, M., & Love, B. C. (2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34, 169-188. doi:10.1017/S0140525X10003134
79. Juhasz, B. J., & Rayner, K. (2006). The role of age of acquisition and word frequency in reading: Evidence from eye fixation durations. Visual Cognition, 13, 846-863. doi:10.1080/13506280544000075
80. Juslin, P., Nilsson, H., & Winman, A. (2009). Probability theory, not the very guide of life. Psychological Review, 116, 856-874. doi:10.1037/ a0016979
81. Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93, 1449-14475.
82. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. New York, NY: Cambridge University Press.
83. Kahneman, D., & Tversky, A. (1982). The simulation heuristic. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (pp. 201-208). New York, NY: Cambridge University Press.
84. Kahneman, D., & Tversky, A. (1996). On the reality of cognitive illusions. Psychological Review, 103, 582-591. doi:10.1037/0033295X.103.3.582
85. Kara, P., Reinagel, P., & Reid, R. C. (2000). Low response variability in simultaneously recorded retinal, thalamic and cortical neurons. Neuron, 27, 635-646. doi:10.1016/S0896-6273(00)00072-6
86. Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 20-58. doi:10.1037/ a0014282
87. Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27, 712-719. doi:10.1016/j.tins.2004.10.007
88. Koehler, D. J., & James, G. (2009). Probability matching in choice under uncertainty: Intuition versus deliberation. Cognition, 113, 123-127. doi: 10.1016/j.cognition.2009.07.00
89. Körding, K. P., & Wolpert, D. M. (2004a). Bayesian integration in sensorimotor learning. Nature, 427, 244-247. doi:10.1038/nature02169
90. Körding, K. P., & Wolpert, D. M. (2004b). The loss function of sensorimotor learning. Proceedings of the National Academy of Sciences of the United States of America, 101, 9839-9842. doi:10.1073/ pnas.0308394101
91. Körding, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10, 319-326. doi: 10.1016/j.tics.2006.05.003
92. Lee, M. D. (2008). Three case studies in the Bayesian analysis of cognitive models. Psychonomic Bulletin & Review, 15, 1-15. doi:10.3758/ PBR.15.1.1
93. Lennie, P. (2003). The cost of cortical computation. Current Biology, 13, 493-497. doi:10.1016/S0960-9822(03)00135-0
94. Lewandowsky, S., Griffiths, T. L., & Kalish, M. L. (2009). The wisdom of individuals: Exploring people's knowledge about everyday events using iterated learning. Cognitive Science, 33, 969-998. doi:10.1111/j.15516709.2009.01045.x
95. Lieberman, D., Tooby, J., & Cosmides, L. (2007). The architecture of human kin detection. Nature, 445, 727-731. doi:10.1038/nature05510
96. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9, 1432-1438. doi:10.1038/nn1790
97. Ma, W. J., & Pouget, A. (2008). Linking neurons to behavior in multisensory perception: A computational review. Brain Research, 1242, 4-12. doi:10.1016/j.brainres.2008.04.082
98. Maimon, G., & Assad, J. A. (2009). Beyond Poisson: Increased spike-time regularity across primate parietal cortex. Neuron, 62, 426-440. doi: 10.1016/j.neuron.2009.03.021
99. Maloney, L. T., & Mamassian, P. (2009). Bayesian decision theory as a model of human visual perception: Testing Bayesian transfer. Visual Neuroscience, 26, 147-155. doi:10.1017/S0952523808080905
100. Mamassian, P. (2008). Overconfidence in an objective anticipatory motor task. Psychological Science, 19, 601-606. doi:10.1111/j.14679280.2008.02129.x
101. Marcus, G. F. (2006). Cognitive architecture and descent with modification. Cognition, 101, 443-465. doi:10.1016/j.cognition.2006.04.009
102. Marcus, G. (2008). Kluge: The haphazard construction of the human mind. Boston, MA: Houghton Mifflin.
103. Marr, D. (1982). Vision. San Francisco, CA: Freeman.
104. Marteau, T. M. (1989). Framing of information: Its influence upon decisions of doctors and patients. British Journal of Social Psychology, 28, 89-94. doi:10.1111/j.2044-8309.1989.tb00849.x
105. Masse, N. Y., & Cook, E. P. (2008). The effect of middle temporal spike phase on sensory encoding and correlates with behavior during a motion-detection task. Journal of Neuroscience, 28, 1343-1355. doi: 10.1523/JNEUROSCI.2775-07.2008
106. McClelland, J. L. (2006). How far can you go with Hebbian learning, and when does it lead you astray? In Y. Munakata & M. H. Johnson (Eds.), Attention and Performance XXI: Processes of change in brain and cognitive development (pp. 33-69). Oxford, England: Oxford University Press.
107. McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B. (2010). Letting structure emerge: Connectionist and dynamical systems approaches to understanding cognition. Trends in Cognitive Sciences, 14, 348-356. doi: 10.1016/j.tics.2010.06.002
108. McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419-457. doi:10.1037/0033-295X.102.3.419
109. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological Review, 88, 375-407. doi:10.1037/0033295X.88.5.375
110. McKenzie, C. R. M. (2003). Rational models as theories-not standards-of behavior. Trends in Cognitive Sciences, 7, 403-406. doi: 10.1016/S1364-6613(03)00196-7
111. McNamara, J. M., & Houston, A. I. (2009). Integrating function and mechanism. Trends in Ecology & Evolution, 24, 670-675. doi:10.1016/ j.tree.2009.05.011
112. Monaghan, P., & Shillcock, R. (2008). Hemispheric dissociation and dyslexia in a computational model of reading. Brain and Language, 107, 185-193. doi:10.1016/j.bandl.2007.12.005
113. Morrison, C. M., & Ellis, A. W. (1995). The roles of word frequency and age of acquisition in word naming and lexical decision. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21, 116- 133. doi:10.1037/0278-7393.21.1.116
114. Movellan, J. R., & Nelson, J. D. (2001). Probabilistic functionalism: A unifying paradigm for the cognitive sciences. Behavioral and Brain Sciences, 24, 690-691. doi:10.1017/S0140525X0154008X
115. Mozer, M. C., Pashler, H., & Homaei, H. (2008). Optimal predictions in everyday cognition: The wisdom of individuals or crowds? Cognitive Science, 32, 1133-1147. doi:10.1080/03640210802353016
116. Najemnik, J., & Geisler, W. S. (2009). Simple summation rule for optimal fixation selection in visual search. Vision Research, 49, 1286-1294. doi:10.1016/j.visres.2008.12.005
117. Nelson, J. D. (2009). Naïve optimality: Subjects' heuristics can be bettermotivated than experimenters' optimal models. Behavioral and Brain Sciences, 32, 94-95. doi:10.1017/S0140525X09000405
118. Norris, D. (2006). The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process. Psychological Review, 113, 327- 357. doi:10.1037/0033-295X.113.2.327
119. Norris, D. (2009). Putting it all together: A unified account of word recognition and reaction-time distributions. Psychological Review, 116, 207-219. doi:10.1037/a0014259
120. Norris, D., & Kinoshita, S. (2007, August). Slow slots with slops: Evidence for "slot-coding" of letter positions with positional noise. Paper presented at the meeting of the European Society for Cognitive Psychology, Marseille, France.
121. Norris, D., & Kinoshita, S. (2008). Perception as evidence accumulation and Bayesian inference: Insights from masked priming. Journal of Experimental Psychology: General, 137, 434-455. doi:10.1037/ a0012799
122. Norris, D., & Kinoshita, S. (2010). Explanation versus accommodation: Reply to Bowers (2010). European Journal of Cognitive Psychology, 22, 1261-1269. doi:10.1080/09541446.2010.524201
123. Norris, D., Kinoshita, S., & van Casteren, M. (2010). A stimulus sampling theory of letter identity and order. Journal of Memory and Language, 62, 254-271. doi:10.1016/j.jml.2009.11.002
124. Norris, D., & McQueen, J. M. (2008). Shortlist B: A Bayesian model of continuous speech recognition. Psychological Review, 115, 357-395. doi:10.1037/0033-295X.115.2.357
125. Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608-631. doi: 10.1037/0033-295X.101.4.608
126. Oaksford, M., & Chater, N. (2003). Computational levels and conditional inference: Reply to Schroyens and Schaeken (2003). Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 150-156. doi:10.1037/0278-7393.29.1.150
127. Oaksford, M., & Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning. Oxford, England: Oxford University Press.
128. Oaksford, M., & Chater, N. (2009). Précis of Bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences, 32, 69-84. doi:10.1017/S0140525X0900051X
129. Palmer, C. R., Cheng, S.-Y., & Seidemann, E. (2007). Linking neuronal and behavioral performance in a reaction-time visual detection task. Journal of Neuroscience, 27, 8122-8137. doi:10.1523/ JNEUROSCI.1940-07.2007
130. Pelli, D. G., Farell, B., & Moore, D. C. (2003). The remarkable inefficiency of word recognition. Nature, 423, 752-756. doi:10.1038/nature01516
131. Perfors, A., Tenenbaum, J. B., & Regier, T. (2011). The learnability of abstract syntactic principles. Cognition, 118, 306-333. doi:10.1016/ j.cognition.2010.11.001
132. Pinker, S., & Prince, A. (1988). On language and connectionism: Analysis of a parallel distributed processing model of language acquisition. Cognition, 28, 73-193. doi:10.1016/0010-0277(88)90032-7
133. Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472-491. doi:10.1037/0033-295X.109.3.472
134. Purcell, B. A., Heitz, R. P., Cohen, J. Y., Schall, J. D., Logan, G. D., & Palmeri, T. J. (2010). Neurally constrained modeling of perceptual decision making. Psychological Review, 117, 1113-1143. doi:10.1037/ a0020311
135. Purushothaman, G., & Bradley, D. C. (2004). Neural population code for fine perceptual decisions in area MT. Nature Neuroscience, 8, 99-106. doi:10.1038/nn1373
136. Ramachandran, V. (1990). Interactions between motion, depth, color and form: The utilitarian theory of perception. In C. Blakemore (Ed.), Vision: Coding and efficiency (pp. 346-360). Cambridge, England: Cambridge University Press.
137. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59-108. doi:10.1037/0033-295X.85.2.59
138. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical Conditioning II (pp. 64-99). New York, NY: Appleton-Century-Crofts.
139. Robbins, H. (1952). Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, 58, 527-535.
140. Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107, 358-367. doi: 10.1037/0033-295X.107.2.358
141. Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. New York, NY: Spartan Books.
142. Rouder, J. N., & Lu, J. (2005). An introduction to Bayesian hierarchical models with an application in the theory of signal detection. Psychonomic Bulletin & Review, 12, 573-604. doi:10.3758/BF03196750
143. Rubin, D. C., & Wenzel, A. E. (1996). One hundred years of forgetting: A quantitative description of retention. Psychological Review, 103, 734- 760. doi:10.1037/0033-295X.103.4.734
144. Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117, 1144-1167. doi:10.1037/a0020511
145. Schacter, D. L. (2001). The seven sins of memory: How the mind forgets and remembers. Boston, MA: Houghton Mifflin.
146. Scherag, A., Demuth, L. Rösler, F., Neville, H. J., & Röder, B. (2004). The effects of late acquisition of L2 and the consequences of immigration on L1 for semantic and morpho-syntactic language aspects. Cognition, 93, B97-B108. doi:10.1016/j.cognition.2004.02.003
147. Schroyens, W., & Schaeken, W. (2003). A critique of Oaksford, Chater, and Larkin's (2000) conditional probability model of conditional reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 140-149. doi:10.1037/0278-7393.29.1.140
148. Seydell, A., Knill, D. C., & Trommershäuser, J. (2010). Adapting internal statistical models for interpreting visual cues to depth. Journal of Vision, 10, Article 1. doi:10.1167/10.4.1
149. Shackleton, T. M., Skottun, B. C., Arnott, R. H., & Palmer, A. R. (2003). Interaural time difference discrimination thresholds for single neurons in the inferior colliculus of guinea pigs. Journal of Neuroscience, 23, 716-724.
150. Shadlen, M. N., & Newsome, W. T. (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. Journal of Neuroscience, 18, 3870-3896.
151. 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.
152. Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17, 443-464. doi:10.3758/ PBR.17.4.443
153. Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM-Retrieving effectively from memory. Psychonomic Bulletin & Review, 4, 145-166. doi:10.3758/BF03209391
154. Shillcock, R., Ellison, T. M., & Monaghan, P. (2000). Eye-fixation behavior, lexical storage and visual word recognition in a split processing model. Psychological Review, 107, 824-851. doi:10.1037/0033295X.107.4.824
155. Shultz, T. R. (2007). The Bayesian revolution approaches psychological development. Developmental Science, 10, 357-364. doi:10.1111/j.14677687.2007.00588.x
156. Sibley, D. E., Kello, C. T., Plaut, D. C., & Elman, J. L. (2009). Sequence encoders enable large-scale lexical modeling: Reply to Bowers and Davis (2009). Cognitive Science, 33, 1187-1191. doi:10.1111/j.15516709.2009.01064.x
157. Sikström, S. (1999). Power function forgetting curves as an emergent property of biologically plausible neural network models. International Journal of Psychology, 34, 460-464. doi:10.1080/002075999399828
158. Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129-138. doi:10.1037/h0042769
159. Sloman, S. A., & Fernbach, P. M. (2008). The value of rational analysis: An assessment of causal reasoning and learning. In N. Chater & M. Oaksford (Eds.), The probabilistic mind: Prospects for Bayesian cognitive science (pp. 485-500). New York, NY: Oxford University Press.
160. Stadthagen-Gonzalez, H., Bowers, J. S., & Damian, M. F. (2004). Age-ofacquisition effects in visual word recognition: Evidence from expert vocabularies. Cognition, 93, B11-B26. doi:10.1016/j.cognition .2003.10.009
161. Stanovich, K. E. (1999). Who is rational? Studies of individual differences in reasoning. Mahwah, NJ: Erlbaum.
162. Steurer, J., Fischer, J. E., Bachmann, L. M., Koller, M., & ter Riet, G. (2002). Communicating accuracy of tests to general practitioners: A controlled study. British Medical Journal, 324, 824-826. doi:10.1136/ bmj.324.7341.824
163. Steyvers, M., Lee, M. D., & Wagenmakers, E.-J. (2009). A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53, 168-179. doi:10.1016/j.jmp.2008.11.002
164. Stocker, A. A., & Simoncelli, E. P. (2006). Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, 9, 578-585. doi:10.1038/nn1669
165. Thompson, P., Brooks, K., & Hammett, S. T. (2006). Speed can go up as well as down at low contrast: Implications for models of motion perception. Vision Research, 46, 782-786. doi:10.1016/ j.visres.2005.08.005
166. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520-522. doi:10.1038/381520a0
167. Thorpe, S., & Imbert, M. (1989). Biological constraints on connectionist modelling. In R. Pfeifer, F. Fogeiman-Soule, S. Steels, & Z. Schreter (Eds.), Connectionism in perspective (pp. 63-93). Amsterdam, the Netherlands: Elsevier/North-Holland.
168. Trommershäuser, J., Maloney, L. T., & Landy, M. S. (2003). The consistency of bisection judgments in visual grasp space. Journal of Vision, 3, 795-807. doi:10.1167/3.11.13
169. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453-458. doi:10.1126/science .7455683
170. Usher, M., & McClelland, J. L. (2001). The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review, 108, 550-592. doi:10.1037/0033-295X.108.3.550
171. Wagenmakers, E.-J. (2009). How do individuals reason in the Wason card selection task? Behavioral and Brain Sciences, 32, 104. doi:10.1017/ S0140525X09000508
172. Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage- Dickey method. Cognitive Psychology, 60, 158-189. doi:10.1016/ j.cogpsych.2009.12.001
173. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron, 36, 955-968. doi:10.1016/S08966273(02)01092-9
174. Weiss, Y., Simoncelli, E. P., & Adelson, E. H. (2002). Motion illusions as optimal percepts. Nature Neuroscience, 5, 598-604. doi:10.1038/nn85
175. West, R. F., & Stanovich, K. E. (2003). Is probability matching smart? Associations between probabilistic choices and cognitive ability. Memory & Cognition, 31, 243-251. doi:10.3758/BF03194383
176. Whitney, C. (2001). How the brain encodes the order of letters in a printed word: The SERIOL model and selective literature review. Psychonomic Bulletin & Review, 8, 221-243. doi:10.3758/BF03196158
177. Widrow, B., & Hoff, M. E. (1960). Adaptive switching circuits. In Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record (Vol. 4, pp. 96-104). New York, NY: Institute of Radio Engineers.
178. Wilson, D. K., Kaplan, R. M., & Schneiderman, L. J. (1987). Framing of decisions and selections of alternatives in health care. Social Behaviour, 2, 51-59.
179. Wixted, J. T., & Carpenter, S. K. (2007). The Wickelgren power law and the Ebbinghaus savings function. Psychological Science, 18, 133-134. doi:10.1111/j.1467-9280.2007.01862.x
180. Wixted, J. T., & Ebbesen, E. B. (1991). On the form of forgetting. Psychological Science, 2, 409-415. doi:10.1111/j.14679280.1991.tb00175.x
181. Wolpert, D. M. (2007). Probabilistic models in human sensorimotor control. Human Movement Science, 26, 511-524. doi:10.1016/j.humov .2007.05.005
182. Wozny, D. R., Beierholm, U. R., & Shams, L. (2010). Probability matching as a computational strategy used in perception. PLoS Computational Biology, 6, e1000871. doi:10.1371/journal.pcbi.1000871
183. Wu, S.-W., Dal Martello, M. F., & Maloney, L. T. (2009), Sub-optimal allocation of time in sequential movements. PLoS ONE, 4, e8228. doi:10.1371/journal.pone.0008228
184. Wu, S.-W., Trommershäuser, J., Maloney, L. T., & Landy, M. S. (2006). Limits to human movement planning in tasks with asymmetric gain landscapes. Journal of Vision, 6, 53-63. doi:10.1167/6.1.5
185. Xu, J., & Griffiths, T. L. (2010). A rational analysis of the effects of memory biases on serial reproduction. Cognitive Psychology, 60, 107- 126. doi:10.1016/j.cogpsych.2009.09.002
186. Zacksenhouse, M., Bogacz, R., & Holmes, P. (2010). Robust versus optimal strategies for two-alternative forced choice tasks. Journal of Mathematical Psychology, 54, 230-246. doi:10.1016/j.jmp.2009.12.004
187. Zhang, H., Wu, S.-W., & Maloney, L. T. (2010). Planning multiple movements within a fixed time limit: The cost of constrained time allocation in a visuo-motor task. Journal of Vision, 10, 1-17. doi: 10.1167/10.6.1