Modeling psychophysical data in R

Modeling Psychophysical Data in R

Volumn , Issue , 2012, Pages 1-367

  Knoblauch, Kenneth ^a   Maloney, Laurence T ^b  

^a STEM CELL AND BRAIN RESEARCH INSTITUTE   (France)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING LANGUAGES; OPEN SOURCE SOFTWARE; STATISTICAL METHODS;

CLASSIFICATION IMAGES; CONJOINT MEASUREMENT; GENERALIZED LINEAR MODEL; GRAPHICAL DISPLAYS; MIXED EFFECTS MODELS; OPEN-SOURCE PROGRAMMING; PSYCHOMETRIC FUNCTION; SIGNAL DETECTION THEORY;

MAXIMUM LIKELIHOOD;

EID: 84860698812     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-4614-4475-6     Document Type: Book

References (208)

1. Abbey, C.K., Eckstein, M.P.: Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments. J. Vis. 2, 66-78 (2002)
2. Ahumada, A.J.: Perceptual classification images from vernier acuity masked by noise. Perception 25 ECVP Abstract Suppl. (1996)
3. Ahumada, A.J.: Classification image weights and internal noise level estimation. J. Vis. 2(1), 121-131 (2002)
4. Ahumada, A.J., Lovell, J.: Stimulus features in signal detection. J. Acoust. Soc. Am. 49, 1751-1756 (1971)
5. Ahumada, A.J., Marken, R., Sandusky, A.: Time and frequency analyses of auditory signal detection. J. Acoust. Soc. Am. 57(2), 385-390 (1975)
6. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csàki, F. (eds.) Second International Symposium on Inference Theory, pp. 267-281. Akadémia Kiadó, Budapest (1973)
7. Baayen, R.H., Davidson, D.J., Bates, D.M.: Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390-412 (2008)
8. Bates, D.: Fitting linear mixed models. R News 5, 27-30 (2005). http://www.r-project.org/ doc/Rnews/Rnews_2005-1.pdf
9. Bates, D., Maechler, M., Bolker, B.: lme4: Linear mixed-effects models using S4 classes (2011). R package version 0.999375-42. http://CRAN.R-project.org/package=lme4. Accessed date on 2nd August 2012
10. Bates, D., Maechler, M., Bolker, B.: lme4.0: Linear mixed-effects models using S4 classes (2012). R package version 0.9999-1/r1692. http://R-Forge.R-project.org/projects/ lme4/. Accessed date on 2nd August 2012
11. Bates, D.M.: lme4: Mixed-Effects Modeling with R. Springer, New York (in preparation). http://lme4.r-forge.r-project.org/book/
12. Bengtsson, H., Riedy, J.: R.matlab: Read and write of MAT files together with R-to-Matlab connectivity (2011). R package version 1.5.1. http://CRAN.R-project.org/package=R.matlab. Accessed date on 2nd August 2012
13. Bernstein, S.N.: Sur l'ordre de la meilleure approximation des fonctions continues par les polynômes de degré donné. Mémoires de l' Académie Royale de Belgique 4, 1-104 (1912)
14. Bishop, Y.M.M., Fienberg, S.E., Holland, P.W.: Discrete Multivariate Analysis: Theory and Practice. MIT, Cambridge (1975)
15. Block, H.D., Marschak, J.: Random orderings and stochastic theories of responses. In: Olkin, I., Ghurye, S., Hoeffding, W., Madow, W., Mann, H. (eds.) Contributions to Probability and Statistics, pp. 38-45. Stanford University Press, Stanford (1960)
16. Boeck, P.D., Wilson, M. (eds.): Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. Springer, New York (2004)
17. Boring, E.G.: Sensation and Perception in the History of Experimental Psychology. Irvington Publishers, Inc., New York (1942)
18. Bouet, R., Knoblauch, K.: Perceptual classification of chromatic modulation. Vis. Neurosci. 21, 283-289 (2004)
19. Bracewell, R.N.: The Fourier Transform and its Applications, 3rd edn. McGraw-Hill, New York (2000)
20. Bratley, P., Fox, B.L., Schrage, L.E.: A Guide to Simulation. Springer, New York (1983)
21. Brindley, G.S.: Two more visual theorems. Q. J. Exp. Psychol. 12, 110-112 (1960)
22. Britten, K.H., Shadlen, M.N., Newsome, W.T.,Movshon, J.A.: The analysis of visual motion: A comparison of neuronal and psychophysical performance. J. Neurosci. 12(12), 4745-4765 (1992)
23. Broström, G., Holmberg, H.: glmmML: Generalized linear models with clustering (2011). R package version 0.82-1. http://CRAN.R-project.org/package=glmmML. Accessed date on 2nd August 2012
24. Burnham, K.P., Anderson, D.R.: Model Selection and Inference: A Practical Information-Theoretic Approach. Springer, New York (1998)
25. Canty, A., Ripley, B.D.: boot: Bootstrap R (S-Plus) Functions (2012). R package version 1.3-5
26. Carney, T., Tyler, C.W., Watson, A.B., Makous, W., Beutte, B., Chen, C.C., Norcia, A.M., Klein, S.A.: Modelfest: Year one results and plans for future years. In: Rogowitz, B.E., Pappas, T.N. (eds.) Proceedings of SPIE: Human vision and electronic imaging V, vol. 3959, pp. 140-151. SPIE, Bellingham (2000)
27. Carroll, L.: Through the Looking Glass and Ahat Alice Found There. Macmillan, Bassingstoke (1871)
28. Chambers, J.M., Hastie, T.J. (eds.): Statistical Models in S. Chapman and Hall/CRC, Boca Raton (1992)
29. Charrier, C., Maloney, L.T., Cherifi, H., Knoblauch, K.: Maximum likelihood difference scaling of image quality in compression-degraded images. J. Opt. Soc. Am. A 24, 3418-3426 (2007)
30. Chauvin, A., Worsley, K., Schyns, P., Arguin, M., Gosselin, F.: Accurate statistical tests for smooth classification images. J. Vis. 5, 659-667 (2005)
31. Christensen, R.H.B.: ordinal-regression models for ordinal data (2010). R package version 2011.09-14. http://www.cran.r-project.org/package=ordinal/. Accessed date on 2nd August 2012
32. Christensen, R.H.B., Brockhoff, P.B.: sensR-an R-package for sensory discrimination (2011). R package version 1.2-13. http://www.cran.r-project.org/package=sensR/. Accessed date on 2nd August 2012
33. Christensen, R.H.B., Hansen, M.K.: binomTools: Performing diagnostics on binomial regression models (2011). R package version 1.0-1. http://CRAN.R-project.org/package= binomTools. Accessed date on 2nd August 2012
34. Chung, K.L., Aitsahlia, F.: Elementary Probability Theory, 4th edn. Springer, New York (2006)
35. Clark, H.H.: The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. J. Verbal Learn. Verbal Behav. 12, 355-359 (1973)
36. Cleveland, W.S., Diaconis, P., McGill, R.: Variables on scatterplots look more highly correlated when the scales are increased. Science 216, 1138-1141 (1982)
37. Cleveland, W.S., McGill, R.: Graphical perception: Theory, experimentation and application to the development of graphical methods. J. Am. Stat. Assoc. 79, 531-554 (1984)
38. Cleveland,W.S.,McGill, R.: The many faces of a scatterplot. J. Am. Stat. Assoc. 79, 807-822 (1984)
39. Conway, J., Eddelbuettel, D., Nishiyama, T., Prayaga, S.K., Tiffin, N.: RPostgreSQL: R interface to the PostgreSQL database system (2010). R package version 0.1-7. http://www. postgresql.org. Accessed date on 2nd August 2012
40. Cook, R.D.,Weisberg, S.: Residuals and Influence in Regression. Chapman and Hall, London (1982)
41. Cornsweet, T.: Visual Perception. Academic, New York (1970)
42. Crozier, W.J.: On the visibility of radiation at the human fovea. J. Gen. Physiol. 34, 87-136 (1950)
43. Crozier, W.J., Wolf, E.: Theory and measurement of visual mechanisms. IV. On flicker with subdivided fields. J. Gen. Physiol. 27, 401-432 (1944)
44. Dakin, S.C., Bex, P.J.: Natural image statistics mediate brightness filling in. Proc. Biol. Sci. 1531, 2341-2348 (2003)
45. Dartnall, H.J.A., Bowmaker, J.K., Mollon, J.D.: Microspectrophotometry of human photoreceptors. In: Mollon, J.D., Sharpe, L.T. (eds.) Colour Vision: Physiology and Psychophysics, pp. 69-80. Academic, London (1983)
46. Davison, A.C., Hinkley, D.V.: Bootstrap Methods and their Applications. Cambridge University Press, Cambridge (1997). http://statwww.epfl.ch/davison/BMA/
47. Debreu, G.: Topological methods in cardinal utility theory. In: Arrow, K.J., Karlin, S., Suppes, P. (eds.) Mathematical Methods in the Social Sciences, pp. 16-26. Stanford University Press, Stanford (1960)
48. DeCarlo, L.T.: Signal detection theory and generalized linear models. Psychol. Meth. 3, 186-205 (1998)
49. DeCarlo, L.T.: On the statistical and theoretical basis of signal detection theory and extensions: Unequal variance, random coefficient and mixture models. J. Math. Psychol. 54, 304-313 (2010)
50. Delord, S., Devinck, F., Knoblauch, K.: Surface and edge in visual detection: Is filling-in necessary? J. Vis. 4, 68a (2004). http://journalofvision.org/4/8/68/
51. Devinck, F.: Les traitements visuels chez l'homme: stratégies de classification de la forme. Ph.D. thesis, Université Lyon 2 (2003)
52. Devroye, L.: Non-uniform Random Variate Generation. Springer, New York (1986)
53. Dobson, A.J.: An Introduction to Generalized Linear Models. Chapman and Hall, London (1990)
54. Edwards, A.W.F.: Likelihood, expanded edn. Johns Hopkins University Press, Baltimore (1992)
55. Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman Hall, New York (1993)
56. Emrith, K., Chantler, M.J., Green, P.R., Maloney, L.T., Clarke, A.D.F.: Measuring perceived differences in surface texture due to changes in higher order statistics. J. Opt. Soc. Am. A 27(5), 1232-1244 (2010)
57. Falmagne, J.C.: Elements of Psychophysical Theory. Oxford University Press, Oxford (1985)
58. Faraway, J.J.: Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman and Hall/CRC, Boca Raton (2006)
59. Fechner, G.T.: Elemente der Psychophysik. Druck und Verlag von Breitkopfs, Leipzig (1860)
60. Finney, D.J.: Probit Analysis, 3rd edn. Cambridge University Press, Cambridge (1971)
61. Firth, D.: Bias reduction of maximum likelihood estimates. Biometrika 80, 27-38 (1993)
62. Fisher, R.A.: The design of experiments. In: Bennett, J.H. (ed.) Statistical Methods, Experimental Design and Scientific Inference, 9th edn. Macmillan, New York (1971)
63. Fleming, R.W., Jäkel, F., Maloney, L.T.: Visual perception of thick transparent materials. Psychol. Sci. 22, 812-820 (2011)
64. Foster, D.H., Bischof, W.F.: Bootstrap estimates of the statistical accuracy of thresholds obtained from psychometric functions. Spatial Vis. 11(1), 135-139 (1997)
65. Foster, D.H., Zychaluk, K.: Nonparametric estimates of biological transducer functions. IEEE Signal Process. Mag. 24, 49-58 (2007)
66. Fründ, I., Haenel, N.V.,Wichmann, F.A.: Inference for psychometric functions in the presence of nonstationary behavior. J. Vis. 11(6), 1-19 (2011)
67. Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., Bornkamp, B., Hothorn, T.: mvtnorm: Multivariate normal and t distributions (2011). R package version 0.9-9991. http:// CRAN.R-project.org/package=mvtnorm. Accessed date on 2nd August 2012
68. Gescheider., G.A.: Psychophysical scaling. Annu. Rev. Psychol. 39, 169-200 (1988)
69. Glass, L.: Moiré effect from random dots. Nature 223, 578-580 (1969)
70. GmbH, M.S.: XLConnect: Excel Connector for R (2011). R package version 0.1-5. http:// CRAN.R-project.org/package=XLConnect. Accessed date on 2nd August 2012
71. Gold, J.M., Murray, R.F., Bennett, P.J., Sekular, A.B.: Deriving behavioural receptive fields for visually completed contours. Curr. Biol. 10, 663-666 (2000)
72. Green, D.M., Swets, J.A.: Signal Detection Theory and Psychophysics. Robert E. Krieger Publishing Company, Huntington (1966/1974)
73. Guilford, J.P.: Psychometric Methods, 2nd edn. McGraw-Hill, New York (1954)
74. Hadfield, J.D.: Mcmc methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Software 33(2), 1-22 (2010). http://www.jstatsoft.org/v33/ i02/
75. Hansen, T., Gegenfurtner, K.R.: Classification images for chromatic signal detection. J. Opt. Soc. Am. A 22, 2081-2089 (2005)
76. Harrell, F.E.: rms: Regression modeling strategies (2011). R package version 3.3-1. http:// CRAN.R-project.org/package=rms. Accessed date on 2nd August 2012
77. Hastie, T., Tibshirani, R.: Generalized Additive Models. Chapman and Hall, London (1990)
78. Hauck, W.W. Jr, Donner, A.: Wald's test as applied to hypotheses in logit analysis. J. Am. Stat. Assoc. 72, 851-853 (1977)
79. Hecht, S., Shlaer, S., Pirenne, M.H.: Energy, quanta and vision. J. Gen. Physiol. 25, 819-840 (1942)
80. Ho, Y.X., Landy, M.S., Maloney, L.T.: Conjoint measurement of gloss and surface texture. Psychol. Sci. 19, 196-204 (2008)
81. James, D.A.: DBI: R Database Interface (2009). R package version 0.2.5. http://CRAN.Rproject. org/package=DBI. Accessed date on 2nd August 2012
82. James, D.A.: RSQLite: SQLite interface for R (2011). R package version 0.10.0. http:// CRAN.R-project.org/package=RSQLite. Accessed date on 2nd August 2012
83. James, D.A., DebRoy, S.: RMySQL: R interface to the MySQL database (2011). R package version 0.8-0. http://biostat.mc.vanderbilt.edu/RMySQL. Accessed date on 2nd August 2012
84. James, D.A., Luciani, J.: ROracle: Oracle database interface for R (2007). R package version 0.5-9. http://www.omegahat.org. Accessed date on 2nd August 2012
85. Johnson, N.L., Kemp, A.W., Kotz, S.: Univariate Discrete Distributions. Wiley, New York (2005)
86. Johnson, N.L., Kotz, S., Balakrishnan, N.: Continuous Univariate Distributions, vol. 1.Wiley, New York (1994)
87. Johnson, N.L., Kotz, S., Balakrishnan, N.: Continuous Univariate Distributions, vol. 2.Wiley, New York (1995)
88. Keppel, G.: Design & Analysis: A Researcher's Handbook, 2nd edn. Prentice-Hall, Englewood Cliffs (1982)
89. Kienzle, W., Franz, M.O., Scholkopf, B., Wichmann, F.A.: Center-surround patterns emerge as optimal predictors for human saccade targets. J. Vis. 9, 1-15 (2009)
90. Kienzle, W., Wichmann, F.A., Schölkopf, B., Franz, M.O.: A nonparametric approach to bottom-up visual saliency. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, pp. 689-696. MIT, Cambridge (2007)
91. Kingdom, F.A.A., Prins, N.: Psychophysics: A Practical Introduction. Academic, New York (2009)
92. Kleiber, C., Zeileis, A.: Applied Econometrics with R. Springer, New York (2008). http:// CRAN.R-project.org/package=AER
93. Klein, S.A.: Measuring, estimating, and understanding the psychometric function: A commentary. Percept. Psychophys. 63, 1421-1455 (2001)
94. Knoblauch, K.: psyphy: Functions for analyzing psychophysical data in R (2012). R package version 0.1-7. http://cran.r-project.org/web/packages/psyphy
95. Knoblauch, K., Maloney, L.T.: Estimating classification images with generalized linear and additive models. J. Vis. 8, 1-19 (2008)
96. Knoblauch, K., Maloney, L.T.: MLDS: Maximum likelihood difference scaling in R. J. Stat. Software 25, 1-26 (2008). http://www.jstatsoft.org/v25/i02
97. Knoblauch, K., Maloney, L.T.: MLCM: Maximum likelihood conjoint measurement (2011). R package version 0.0-8. http://CRAN.R-project.org/package=MLCM. Accessed date on 2nd August 2012
98. Knoblauch, K., Maloney, L.T.: MPDiR: Data sets and scripts for Modeling Psychophysical Data in R (2012). R package version 0.1-11. http://cran.r-project.org/web/packages/MPDiR
99. Knoblauch, K., Vital-Durand, F., Barbur, J.: Variation of chromatic sensitivity across the life span. Vis. Res. 41, 23-36 (2001)
100. Knuth, D.E.: The Art of Computer Programming: Seminumerical Programming, 3rd edn. Addison-Wesley, New York (1997)
101. Komárek, A., Lesaffre, E.: Generalized linear mixed model with a penalized gaussian mixture as a random-effects distribution. Comput. Stat. Data Anal. 52(7), 3441-3458 (2008)
102. Kontsevich, L.L., Tyler, C.W.:What makesMona Lisa smile? Vis. Res. 44, 1493-1498 (2004)
103. Kosmidis, I.: brglm: Bias reduction in binary-response GLMs (2007). R package version 0.5-6. http://www.ucl.ac.uk/~ucakiko/software.html. Accessed date on 2nd August 2012
104. Krantz, D.H., Luce, R.D., Suppes, P., Tversky, A.: Foundations of Measurement (Vol. 1): Additive and Polynomial Representations. Academic, New York (1971)
105. Kuss, M., Jäkel, F., Wichmann, F.A.: Bayesian inference for psychometric functions. J. Vis. 5, 478-492 (2005). http://journalofvision.org/5/5/8/
106. Lang, D.T.: Rcompression: In-memory decompression for GNU zip and bzip2 formats. R package version 0.93-2. http://www.omegahat.org/Rcompression. Accessed date on 2nd August 2012
107. Lazar, N.A.: The Statistical Analysis of Functional MRI Data. Springer, New York (2008)
108. Legge, G.E., Gu, Y.C., Luebker, A.: Efficiency of graphical perception. Percept. Psychophys. 46, 365-374 (1989)
109. Lehmann, E.L., Casella, G.: Theory of Point Estimation, 2nd edn. Springer, New York (1998)
110. Lemon, J: Plotrix: A package in the red light district of R. R-News 6(4), 8-12 (2010)
111. Levi, D.M., Klein, S.A.: Classification images for detection and position discrimination in the fovea and parafovea. J. Vis. 2(1), 46-65 (2002)
112. Li, Y., Baron, J.: Behavioral Research Data Analysis in R, 1st edn. Springer, New York (2011)
113. Lindsey, D., Brown, A.: Color naming and the phototoxic effects of sunlight on the eye. Psychol. Sci. 13, 506-512 (2002)
114. Lindsey, D.T., Brown, A.M., Reijnen, E., Rich, A.N., Kuzmova, Y.I., Wolfe, J.M.: Color channels, not color appearance or color categories, guide visual search for desaturated color targets. Psychol. Sci. 21, 1208-1214 (2010)
115. Link, S.W.: The Wave Theory of Difference and Similarity. Lawrence Erlbaum Associates, Hillsdale (1992)
116. Luce, R.D., Green, D.M.: Parallel psychometric functions from a set of independent detectors. Psychol. Rev. 82, 483-486 (1975)
117. Luce, R.D., Tukey, J.W.: Simultaneous conjoint measurement: A new scale type of fundamental measurement. J. Math. Psychol. 32, 466-473 (1964)
118. Macke, J.H., Wichmann, F.A.: Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces. J. Vis. 10 (2010)
119. MacLeod, D.: Visual sensitivity. Annu. Rev. Psychol. 29, 613-645 (1978)
120. Macmillan, N.A., Creelman, C.D.: Detection Theory: A User's Guide, 2nd edn. Lawrence Erlbaum Associates, New York (2005)
121. Maloney, L.T.: Confidence intervals for the parameters of psychometric functions. Percept. Psychophys. 47(2), 127-134 (1990)
122. Maloney, L.T., Dal Martello, M.F.: Kin recognition and the perceived facial similarity of children. J. Vis. 6, 1047-1056 (2006). http://journalofvision.org/6/10/4/
123. Maloney, L.T., Yang, J.N.: Maximum Likelihood difference scaling. J. Vis. 3(8), 573-585 (2003). http://www.journalofvision.org/3/8/5
124. Mamassian, P., Goutcher, R.: Temporal dynamics in bistable perception. J. Vis. 5, 361-375 (2005)
125. Mangini, M.C., Biederman, I.: Making the ineffable explicit: Estimating the information employed for face classifications. Cognit. Sci. 28, 209-226 (2004)
126. Marin-Franch, I., Zychaluk, K., Foster, D.H.: modelfree: Model-free estimation of a psychometric function (2010). R package version 1.0. http://CRAN.R-project.org/package= modelfree. Accessed date on 2nd August 2012
127. McCullagh, P., Nelder, J.A.: Generalized Linear Models. Chapman and Hall, London (1989)
128. Mineault, P.J., Barthelme, S., Pack, C.C.: Improved classification images with sparse priors in a smooth basis. J. Vis. 9, 1-24 (2009)
129. Mood, A., Graybill, F.A., Boes., D.C.: Introduction to the Theory of Statistics, 3rd edn. McGraw-Hill, New York (1974)
130. Murray, R.F.: Classification images: A review. J. Vis. 11, 1-25 (2011)
131. Murray, R.F., Bennett, P.J., Sekuler, A.B.: Optimal methods for calculating classification images: Weighted sums. J. Vis. 2(1), 79-104 (2002)
132. Murrel, P.: Introduction to Data Technologies. Chapman and Hall/CRC, Boca Raton (2009)
133. Nandy, A.S., Tjan, B.S.: The nature of letter crowding as revealed by first-and second-order classification images. J. Vis. 7(2), 5.1-26 (2007)
134. Neri, P.: Estimation of nonlinear psychophysical kernels. J. Vis. 4, 82-91 (2004). http:// journalofvision.org/4/2/2/
135. Neri, P.: How inherently noisy is human sensory processing? Psychonomic Bull. Rev. 17, 802-808 (2010)
136. Neri, P., Heeger, D.J.: Spatiotemporal mechanisms for detecting and identifying image features in human vision. Nat. Neurosci. 5, 812-816 (2002)
137. Neri, P., Parker, A.J., Blakemore, C.: Probing the human stereoscopic system with reverse correlation. Nature 401, 695-698 (1999)
138. Newsome, W.T., Britten, K.H., Movshon, J.A.: Neuronal correlates of a perceptual decision. Nature 341(6237), 52-54 (1989)
139. Obein, G., Knoblauch, K., Viénot, F.: Difference scaling of gloss: Nonlinearity, binocularity, and constancy. J. Vis. 4(9), 711-720 (2004)
140. Peirce, J.W.: The potential importance of saturating and supersaturating contrast response functions in visual cortex. J. Vis. 7, 13 (2007)
141. Pinheiro, J., Bates, D.: Mixed-Effects Models in S and S-PLUS. Springer, New York (2000)
142. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., the R Development Core Team: nlme: Linear and nonlinear mixed effects models (2012). R package version 3.1-104
143. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes, 3rd ed.: The Art of Scientific Computing. Cambridge University Press, Cambridge (2007)
144. Quick, R.: A vector-magnitude model of contrast detection. Kybernetik 16, 65-67 (1974)
145. R-core members, et al.: Foreign: Read data stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, . . . (2011). R package version 0.8-46. http://CRAN.R-project.org/package=foreign. Accessed date on 2nd August 2012
146. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2011). ISBN 3-900051-07-0. http:// www.R-project.org/. Accessed date on 2nd August 2012
147. Rensink, R.A., Baldridge, G.: The perception of correlation in scatterplots. Comput. Graph. Forum 29, 1203-1210 (2010)
148. Rhodes, G., Maloney, L.T., Turner, J., Ewing, L.: Adaptive face coding and discrimination around the average face. Vis. Res. 47, 974-989 (2007)
149. Ripley, B.: RODBC: ODBC Database Access (2011). R package version 1.3-3. http://CRAN. R-project.org/package=RODBC. Accessed date on 2nd August 2012
150. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
151. Roberts, F.S.: Measurement Theory. Cambridge University Press, Cambridge (1985)
152. Ross, M.G., Cohen, A.L.: Using graphical models to infer multiple visual classification features. J. Vis. 9(3), 23.1-24 (2009)
153. Ross, S.M.: A First Course in Probability Theory, 8th edn. Prentice-Hall, Englewood Cliffs (2009)
154. Rouder, J.N., Lu, J., Sun, D., Speckman, P.,Morey, R., Naveh-Benjamin, M.: Signal detection models with random participant and item effects. Psychometrika 72, 621-642 (2007)
155. Salsberg, D.: The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. W. H. Freeman, New York (2001)
156. Sarkar, D.: Lattice: Multivariate Data Visualization with R. Springer, New York (2008). http:// lmdvr.r-forge.r-project.org
157. Schneider, B.: Individual loudness functions determined from direct comparisons of loudness intervals. Percept. Psychophys. 28, 493-503 (1980)
158. Schneider, B.: A technique for the nonmetric analysis of paired comparisons of psychological intervals. Psychometrika 45, 357-372 (1980)
159. Schneider, B., Parker, S., Stein, D.: The measurement of loudness using direct comparisons of sensory intervals. J. Math. Psychol. 11, 259-273 (1974)
160. Schwartz, M.: WriteXLS: Cross-platform Perl based R function to create Excel 2003 (XLS) files (2010). R package version 2.1.0. http://CRAN.R-project.org/package=WriteXLS. Accessed date on 2nd August 2012
161. Solomon, J.A.: Noise reveals visual mechanisms of detection and discrimination. J. Vis. 2(1), 105-120 (2002)
162. Spector, P.: Data Manipulation with R. Springer, New York (2008)
163. van Steen, G.: dataframes2xls: dataframes2xls writes data frames to xls files (2011). R package version 0.4.5. http://cran.r-project.org/web/packages/dataframes2xls. Accessed date on 2nd August 2012
164. Stevens, S.S.: On the theory of scales of measurement. Science 103, 677-680 (1946)
165. Stevens, S.S.: On the psychophysical law. Psychol. Rev. 64, 153-181 (1957)
166. Strasburger, H.: Converting between measures of slope of the psychometric function. Percept. Psychophys. 63, 1348-1355 (2001)
167. Sun, H., Lee, B., Baraas, R.: Systematic misestimation in a vernier task arising from contrast mismatch. Vis. Neurosci. 25, 365-370 (2008)
168. Suppes, P.: Finite equal-interval measurement structures. Theoria 38, 45-63 (1972)
169. Tanner, W.P., Swets, J.A.: A decision-making theory of visual detection. Psychol. Rev. 61, 401-409 (1954)
170. Teller, D.Y.: The forced-choice preferential looking procedure: A psychophysical technique for use with human infants. Infant Behav. Dev. 2, 135-153 (1979)
171. Thibault, D., Brosseau-Lachaine, O., Faubert, J., Vital-Durand, F.: Maturation of the sensitivity for luminance and contrast modulated patterns during development of normal and pathological human children. Vis. Res. 47, 1561-1569 (2007)
172. Thomas, J.P., Knoblauch, K.: Frequency and phase contributions to the detection of temporal luminance modulation. J. Opt. Soc. Am. A 22(10), 2257-2261 (2005)
173. Thurstone, L.L.: A law of comparative judgement. Psychol. Rev. 34, 273-286 (1927)
174. Tolhurst, D.J., Movshon, J.A., Dean, A.F.: The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vis. Res. 23(8), 775-785 (1983)
175. Treutwein, B., Strasburger, H.: Fitting the psychometric function. Percept. Psychophys. 61(1), 87-106 (1999)
176. Urban, F.M.: The method of constant stimuli and its generalizations. Psychol. Rev. 17, 229-259 (1910)
177. Urbanek, S.: RJDBC: Provides access to databases through the JDBC interface (2011). R package version 0.2-0. http://www.rforge.net/RJDBC/. Accessed date on 2nd August 2012
178. Venables, W.N., Ripley, B.D.:Modern Applied Statistics with S, 4th edn. Springer, New York (2002). http://www.stats.ox.ac.uk/pub/MASS4
179. Victor, J.: Analyzing receptive fields, classification images and functional images: Challenges with opportunities for synergy. Nat. Neurosci. 8, 1651-1656 (2005)
180. Warnes, G.R.: gmodels: Various R programming tools for model fitting (2011). R package version 2.15.1. http://CRAN.R-project.org/package=gmodels. Accessed date on 2nd August 2012
181. Warnes, G.R., et al.: gdata: Various R programming tools for data manipulation (2010). R package version 2.8.1. http://CRAN.R-project.org/package=gdata. Accessed date on 2nd August 2012
182. Watson, A.: The spatial standard observer: A human vision model for display inspection. In: 353 SID Symposium Digest of Technical Papers, 37, pp. 1312-1315 (2006)
183. Watson, A.B., Ahumada, A.J.: A standard model for foveal detection of spatial contrast. J. Vis. 5, 717-740 (2005)
184. Watson, A.B., Pelli, D.G.: QUEST: A Bayesian adaptive psychometric method. Percept. Psychophys. 33, 113-120 (1983)
185. Watson, G.A.: Approximation Theory and Numerical Methods. Wiley, New York (1980)
186. Westheimer, G.: The spatial grain of the perifoveal visual field. Vis. Res. 22(1), 157-162 (1982)
187. Wichmann, F.A., Graf, A.B.A., Simoncelli, E.P., Bülthoff, H.H., Schölkopf, B.: Machine learning applied to perception: Decision-images for gender classification. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems 17, pp. 1489-1496. MIT, Cambridge (2005)
188. Wichmann, F.A., Hill, N.J.: The psychometric function: I. fitting, sampling and goodness of fit. Percept. Psychophys. 63, 1293-1313 (2001)
189. Wickens, T.D.: Elementary Signal Detection Theory. Oxford University Press, New York (2002)
190. Wickham, H.: ggplot2: Elegant Graphics for Data Analysis. Springer, New York (2009). http://had.co.nz/ggplot2/book
191. Wilkinson, G.N., Rogers, C.E.: Symbolic description of factorial models for analysis of variance. Appl. Stat. 22, 392-399 (1973)
192. Wilkinson, L.: The Grammar of Graphics. Springer, New York (2005)
193. Wilks, S.S.: The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60-62 (1938)
194. Williams, J., Ramaswamy, D., Oulhaj, A.: 10 Hz flicker improves recognition memory in older people. BMC Neurosci. 7, 21 (2006)
195. Winer, B.J.: Statistical Principles in Experimental Design, 2nd edn. McGraw-Hill, New York (1971)
196. Winship, C., Mare, R.D.: Regression models with ordinal variables. Am. Soc. Rev. 49, 512-525 (1984)
197. Wood, S.: gamm4: Generalized additive mixed models using mgcv and lme4 (2011). R package version 0.1-3. http://CRAN.R-project.org/package=gamm4. Accessed date on 2nd August 2012
198. Wood, S.N.: Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC, Boca Raton (2006)
199. Xie, Y., Griffin, L.D.: A 'portholes' experiment for probing perception of small patches of natural images. Perception 36, 315 (2007)
200. Yang, J.N., Szeverenyi, N.M., Ts'o, D.: Neural resources associated with perceptual judgment across sensory modalities. Cerebr. Cortex 18, 38-45 (2008)
201. Yee, T.W.: The vgam package for categorical data analysis. J. Stat. Software 32(10), 1-34 (2010). http://www.jstatsoft.org/v32/i10
202. Yeshurun, Y., Carrasco, M., Maloney, L.T.: Bias and sensitivity in two-interval forced choice procedures: Tests of the difference model. Vis. Res. 48, 1837-1851 (2008)
203. Yovel, Y., Franz, M.O., Stilz, P., Schnitzler, H.U.: Plant classification from bat-like echolocation signals. PLoS Comput. Biol. 4, e1000032 (2008)
204. Yssaad-Fesselier, R., Knoblauch, K.: Modeling psychometric functions in R. Behav. Res. Meth. Instrum. Comp. 38, 28-41 (2006)
205. Zhaoping, L., Jingling, L.: Filling-in and suppression of visual perception from context: A Bayesian account of perceptual biases by contextual influences. PLoS Comput. Biol. 4, e14 (2008)
206. Zucchini, W.: An introduction to model selection. J. Math. Psychol. 44, 41-61 (2000)
207. Zuur, A., Ieno, E.N., Walker, N., Saveiliev, A.A., Smith, G.M.: Mixed Effects Lodels and Extensions in Ecology with R. Springer, New York (2009)
208. Zychaluk, K., Foster, D.H.: Model-free estimation of the psychometric function. Attention Percept. Psychophys. 71, 1414-1425 (2009)