Measuring neural representations with fMRI: Practices and pitfalls

Annals of the New York Academy of Sciences

Volumn 1296, Issue 1, 2013, Pages 108-134

  Davis, Tyler ^a   Poldrack, Russell A ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Adaptation; FMRI; MVPA; Representation

Indexed keywords

ADAPTATION; ARTICLE; BOLD SIGNAL; BRAIN REGION; CLASSIFICATION; COGNITION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; MACHINE LEARNING; NEUROIMAGING; STIMULUS RESPONSE; TASK PERFORMANCE; TEMPORAL LOBE; VOXEL BASED MORPHOMETRY;

EID: 84883170234     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1111/nyas.12156     Document Type: Article

References (138)

1. Marr, D. 1982. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: Henry Holt and Co.
2. Simons, J.S. & H.J. Spiers . 2003. Prefrontal and medial temporal lobe interactions in long-term memory. Nat. Rev. Neurosci. 4: 637-648.
3. Eichenbaum, H., A.R. Yonelinas & C. Ranganath . 2007. The medial temporal lobe and recognition memory. Annu. Rev. Neuro. 30: 123.
4. Corbetta, M. & G.L. Shulman . 2002. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3: 215-229.
5. Kastner, S. & L.G. Ungerleider . 2000. Mechanisms of visual attention in the human cortex. Annu. Rev. Neuro. 23: 315-341.
6. Poldrack, R.A. & K. Foerde . 2008. Category learning and the memory systems debate. Neurosci. Biobehav. Rev. 32: 197-205.
7. Smith, E.E. & M. Grossman . 2008. Multiple systems of category learning. Neurosci. Biobehav. Rev. 32: 249-264.
8. Seger, C.A. & E.K. Miller . 2010. Category learning in the brain. Annu. Rev. Neuro. 33: 203-219.
9. Rissman, J. & A.D. Wagner . 2012. Distributed representations in memory: insights from functional brain imaging. Annu. Rev. Psychol. 63: 101-128.
10. Ashby, F.G. & R.E. Gott . 1988. Decision rules in the perception and categorization of multidimensional stimuli. J. Exp. Psychol. Learn Mem. Cogn. 14: 33-53.
11. Rosch, E. & C.B. Mervis . 1975. Family resemblances: studies in the internal structure of categories. J. Cogn. Psychol. 7: 573-605.
12. Nosofsky, R.M. 1986. Attention, similarity, and the identification-categorization relationship. J. Exp. Psychol. Gen. 115: 39.
13. Love, B.C., D.L. Medin & T.M. Gureckis . 2004. SUSTAIN: a network model of category learning. Psychol. Rev. 111: 309-322.
14. Ashby, F.G. & L.A. Alfonso-Reese . 1995. Categorization as probability density estimation. J. Math. Psychol. 39: 216-233.
15. Rosseel, Y. 2002. Mixture models of categorization. J. Math. Psychol. 46: 178-210.
16. Davachi, L. 2006. Item, context and relational episodic encoding in humans. Curr. Opin. Neurobiol. 16: 693-700.
17. Diana, R.A., A.P. Yonelinas & C. Ranganath . 2007. Imaging recollection and familiarity in the medial temporal lobe: a three-component model. Trends Cogn. Sci. 11: 379-386.
18. Squire, L.R., J.T. Wixted & R.E. Clark . 2007. Recognition memory and the medial temporal lobe: a new perspective. Nat. Rev. Neurosci. 8: 872-883.
19. Norman, K.A. & R.C. O'Reilly . 2003. Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol. Rev. 110: 611-645.
20. Barsalou, L.W. 1999. Perceptual symbol systems. Behav. Brain Sci. 22: 577-660.
21. Love, B.C. 2005. Environment and goals jointly direct category acquisition. Curr. Dir. Psychol. Sci. 14: 195-199.
22. Quiroga, R.Q., L. Reddy, G. Kreiman, et al. 2005. Invariant visual representation by single neurons in the human brain. Nature 435: 1102-1107.
23. Edelman, S., K. Grill-Spector, T. Kushnir & R. Malach . 1998. Toward direct visualization of the internal shape representation space by fMRI. Psychobiology 26: 309-321.
24. Drucker, D.M. & G.K. Aguirre . 2009. Different spatial scales of shape similarity representation in lateral and ventral LOC. Cereb. Cortex 19: 2269-2280.
25. Kriegeskorte, N., M. Mur, D.A. Ruff, et al. 2008. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60: 1126-1141.
26. Huth, A.G., S. Nishimoto, A.T. Vu & J.L. Gallant . 2012. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron 76: 1210-1224.
27. Edelman, S. 1998. Representation is representation of similarities. Behav. Brain Sci. 21: 449-467.
28. Gärdenfors, P. 2004. Conceptual Spaces: The Geometry of Thought. MIT Press. Cambridge, MA.
29. Shepard, R.N. 1987. Toward a universal law of generalization for psychological science. Science 237: 1317-1323.
30. Nosofsky, R.M. 1988. Exemplar-based accounts of relations between classification, recognition, and typicality. J. Exp. Psychol. Learn Mem. Cogn. 14: 700-708.
31. Tversky, A. 1977. Features of similarity. Psychol. Rev. 84: 327-352.
32. Smith, E.E., E.J. Shoben & L.J. Rips . 1974. Structure and process in semantic memory: a featural model for semantic decisions. Psychol. Rev. 81: 214-241.
33. Friston, K.J., P. Jezzard & R. Turner . 1994. Analysis of functional MRI time-series. Hum. Brain Mapp. 1: 153-171.
34. Worsley, K.J., C.H. Liao, J. Aston, et al. 2002. A general statistical analysis for fMRI data. Neuroimage 15: 1-15.
35. Kanwisher, N., J. McDermott & M.M. Chun . 1997. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J. Neuro. 17: 4302-4311.
36. Epstein, R., A. Harris, D. Stanley & N. Kanwisher . 1999. The parahippocampal place area: recognition, navigation, or encoding? Neuron 23: 115-125.
37. Lotze, M., M. Erb, H. Flor, et al. 2000. fMRI evaluation of somatotopic representation in human primary motor cortex. Neuroimage 11: 473-481.
38. Shmuelof, L. & E. Zohary . 2005. Dissociation between ventral and dorsal fMRI activation during object and action recognition. Neuron 47: 457-470.
39. O'Doherty, J.P. 2004. Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr. Opin. Neurobiol. 14: 769-776.
40. Forstmann, B.U., E.J. Wagenmakers, T. Eichele, et al. 2011. Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? Trends Cogn. Sci. 15: 272-279.
41. Davis, T., B.C. Love & A.R. Preston . 2012. Learning the exception to the rule: model-based fMRI reveals specialized representations for surprising category members. Cereb. Cortex. 22: 260-273.
42. Davis, T., B.C. Love & A.R. Preston . 2012. Striatal and hippocampal entropy and recognition signals in category learning: simultaneous processes revealed by model-based fMRI. J. Exp. Psychol. Learn Mem. Cogn. 38: 821-839.
43. White, C.N. & R.A. Poldrack . 2013. Using fMRI to constrain theories of cognition. Perspect. Psychol. Sci. 8: 79-83.
44. Daw, N.D. & K. Doya . 2006. The computational neurobiology of learning and reward. Curr. Opin. Neurobiol. 16: 199-204.
45. Kourtzi, Z. & N. Kanwisher . 2000. Cortical regions involved in perceiving object shape. J. Neuro. 20: 3310-3318.
46. Grill-Spector, K. & R. Malach . 2001. fMR-adaptation: a tool for studying the functional properties of human cortical neurons. Acta Psychol. 107: 293-321.
47. Kourtzi, Z. & N. Kanwisher . 2001. Representation of perceived object shape by the human lateral occipital complex. Science 293: 1506-1509.
48. Malach, R. 2012. Targeting the functional properties of cortical neurons using fMR-adaptation. Neuroimage 62: 1163-1169.
49. Desimone, R. 1996. Neural mechanisms for visual memory and their role in attention. Proc. Natl. Acad. Sci. U. S. A. 93: 13494-13499.
50. Aguirre, G.K. 2007. Continuous carry-over designs for fMRI. Neuroimage 35: 1480-1494.
51. Tolias, A.S., Z. Kourtzi & N.K. Logothetis . 2003. Functional magnetic resonance imaging adaptation: a technique for studying the properties of neuronal networks. In Exploratory analysis and data modeling in functional neuroimaging, F.T. Somner and A. Wichert (eds). pp. 109-125. MIT Press Cambridge MA.
52. Sawamura, H., G.A. Orban & R. Vogels . 2006. Selectivity of neuronal adaptation does not match response selectivity: a single-cell study of the FMRI adaptation paradigm. Neuron 49: 307-318.
53. Summerfield, C., E.H. Trittschuh, J.M. Monti, et al. 2008. Neural repetition suppression reflects fulfilled perceptual expectations. Nat. Neurosci. 11: 1004-1006.
54. Mur, M., D.A. Ruff, J. Bodurka, et al. 2010. Face-identity change activation outside the face system: "release from adaptation" may not always indicate neuronal selectivity. Cereb. Cortex 20: 2027-2042.
55. Larsson, J. & A.T. Smith . 2012. FMRI repetition suppression: neuronal adaptation or stimulus expectation? Cereb. Cortex 22: 567-576.
56. Norman, K.A., S.M. Polyn, G.J. Detre & J.V. Haxby . 2006. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10: 424-430.
57. Cox, D.D. & R.L. Savoy . 2003. Functional magnetic resonance imaging (fMRI)"brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19: 261-270.
58. Mur, M., P.A. Bandettini & N. Kriegeskorte . 2009. Revealing representational content with pattern-information fMRI-an introductory guide. Soc. Cogn. Affect Neurosci. 4: 101-109.
59. Raizada, R.D. & N. Kriegeskorte . 2010. Pattern information fMRI: new questions which it opens up and challenges which face it. Int. J. Imaging Syst. Technol. 20: 31-41.
60. Landauer, T.K. & S.T. Dumais . 1997. A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104: 211-240.
61. Kriegeskorte, N., M. Mur & P. Bandettini . 2008. Representational similarity analysis-connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2: 4.
62. Weber, M., S.L. Thompson-Schill, D. Osherson, et al. 2009. Predicting judged similarity of natural categories from their neural representations. Neuropsychologia 47: 859-868.
63. Davis, T. & R.A. Poldrack . 2013. Quantifying the internal structure of categories using a neural typicality measure. Cereb. Cortex. doi: 10.1093/cercor/bht014.
64. Haxby, J.V., M.I. Gobbini, M.L. Furey, et al. 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293: 2425-2430.
65. Formisano, E., F. De Martino & G. Valente . 2008. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn. Reson. Imaging 26: 921-934.
66. Yamashita, O., M.A. Sato, T. Yoshioka, et al. 2008. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage 42: 1414-1429.
67. Ng, B., A. Vahdat, G. Hamarneh & R. Abugharbieh . 2010. Generalized sparse classifiers for decoding cognitive states in fMRI. Mach. Learn. Med. Imag. 6357: 108-115.
68. Ryali, S., K. Supekar, D.A. Abrams & V. Menon . 2010. Sparse logistic regression for whole brain classification of fMRI data. Neuroimage 51: 752.
69. Pereira, F., T. Mitchell & M. Botvinick . 2009. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45: S199-S209.
70. Hanson, S.J. & Y.O. Halchenko . 2008. Brain reading using full brain support vector machines for object recognition: there is no "face" identification area. Neural Comput. 20: 486-503.
71. Mukherjee, S. & V. Vapnik . 1999. Support vector method for multivariate density estimation. Center for Biological and Computational Learning. Department of Brain and Cognitive Sciences, MIT. CBCL, 170. http://cbcl.csail.mit.edu.ezproxy.lib.utexas.edu/projects/cbcl/publications/ps/dense_memo.ps.
72. Diedrichsen, J., G.R. Ridgway, K.J. Friston & T. Wiestler . 2011. Comparing the similarity and spatial structure of neural representations: a pattern-component model. Neuroimage 55: 1665-1678.
73. Kay, K.N., T. Naselaris, R.J. Prenger & J.L. Gallant . 2008. Identifying natural images from human brain activity. Nature 452: 352-355.
74. Mitchell, T.M., S.V. Shinkareva, A. Carlson, et al. 2008. Predicting human brain activity associated with the meanings of nouns. Science 320: 1191-1195.
75. Naselaris, T., K.N. Kay, S. Nishimoto & J.L. Gallant . 2011. Encoding and decoding in fMRI. Neuroimage 56: 400-410.
76. Naselaris, T., R.J. Prenger, K.N. Kay, et al. 2009. Bayesian reconstruction of natural images from human brain activity. Neuron 63: 902-915.
77. Jimura, K. & R.A. Poldrack . 2011. Analyses of regional-average activation and multivoxel pattern information tell complementary stories. Neuropsychologia 50: 544-552.
78. Sapountzis, P., D. Schluppeck, R. Bowtell & J.W. Peirce . 2010. A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex. Neuroimage 49: 1632-1640.
79. Mourao-Miranda, J., K.J. Friston & M. Brammer . 2007. Dynamic discrimination analysis: a spatial-temporal SVM. Neuroimage 36: 88-99.
80. Soon, C.S., M. Brass, H.J. Heinze & J.D. Haynes . 2008. Unconscious determinants of free decisions in the human brain. Nat. Neurosci. 11: 543-545.
81. Harrison, S.A. & F. Tong . 2009. Decoding reveals the contents of visual working memory in early visual areas. Nature 458: 632-635.
82. Turner, B.O., J.A. Mumford, R.A. Poldrack & F.G. Ashby . 2012. Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs. Neuroimage 62: 1429-1438.
83. Kriegeskorte, N., R. Goebel & P. Bandettini . 2006. Information-based functional brain mapping. Proc. Natl. Acad. Sci. U. S. A. 103: 3863-3868.
84. Raizada, R.D. & R.A. Poldrack . 2007. Selective amplification of stimulus differences during categorical processing of speech. Neuron 56: 726-740.
85. Haxby, J.V. 2012. Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage 62: 852-855.
86. Liang, J.C., A.D. Wagner & A.R. Preston . 2012. Content representation in the human medial temporal lobe. Cereb. Cortex 23: 80-96.
87. Miller, G.A., R. Beckwith, C. Fellbaum, D. Gross, & K.J. Miller . 1990. Introduction to wordnet: An on-line lexical database. Int. J. Lexicography 3: 235-244.
88. Morgan, L.K., S.P. MacEvoy, G.K. Aguirre & R.A. Epstein . 2011. Distances between real-world locations are represented in the human hippocampus. J. Neurosci. 31: 1238-1245.
89. Connolly, A.C., J.S. Guntupalli, J. Gors, et al. 2012. The representation of biological classes in the human brain. J. Neurosci. 32: 2608-2618.
90. Op de Beeck, H.P., K. Torfs & J. Wagemans . 2008. Perceived shape similarity among unfamiliar objects and the organization of the human object vision pathway. J. Neurosci. 2840: 10111-10123.
91. Braet, W., J. Wagemans & H.P. Op de Beeck . 2011. The visual word form area is organized according to orthography. Neuroimage 59: 2751-2759.
92. Panis, S., J. Wagemans & H.P. Op de Beeck . 2011. Dynamic norm-based encoding for unfamiliar shapes in human visual cortex. J. Cogn. Neurosci. 23: 1829-1843.
93. Kahn, D.A. & G.K. Aguirre . 2012. Confounding of norm-based and adaptation effects in brain responses. Neuroimage 60: 2294-2299.
94. Sakamoto, Y., T. Matsuka & B.C. Love . 2004. Dimension-wide vs. exemplar-specific attention in category learning and recognition. In Proceedings of the 6th International Conference of Cognitive Modeling. 261-266. Mahwah, NJ, US: Lawrence Erlbaum Associates Publisher.
95. Rodrigues, P.M. & J.M. Murre . 2007. Rules-plus-exception tasks: a problem for exemplar models? Psychon. Bull. Rev. 14: 640-646.
96. Kastner, S., P. De Weerd, R. Desimone & L.G. Ungerleider . 1998. Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. Science 282: 108-111.
97. Brefczynski, J.A. & E.A. DeYoe . 1999. A physiological correlate of the 'spotlight' of visual attention. Nat. Neurosci. 2: 370-374.
98. Folstein, J.R., T.J. Palmeri & I. Gauthier . 2012. Category learning increases discriminability of relevant object dimensions in visual cortex. Cereb. Cortex 23: 814-823.
99. Kamitani, Y. & F. Tong . 2005. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8: 679-685.
100. Haynes, J.D. & G. Rees . 2005. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat. Neurosci. 8: 686-691.
101. MacEvoy, S.P. & R.A. Epstein . 2009. Decoding the representation of multiple simultaneous objects in human occipitotemporal cortex. Curr. Bio. 19: 943-947.
102. Serences, J.T., S. Saproo, M. Scolari, et al. 2009. Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions. Neuroimage 44: 223-231.
103. Ester, E.F., J.T. Serences & E. Awh . 2009. Spatially global representations in human primary visual cortex during working memory maintenance. J. Neurosci. 29: 15258-15265.
104. Reddy, L., N. Tsuchiya & T. Serre . 2010. Reading the mind's eye: decoding category information during mental imagery. Neuroimage 50: 818-825.
105. Serences, J.T., E.F. Ester, E.K. Vogel & E. Awh . 2009. Stimulus-specific delay activity in human primary visual cortex. Psychol. Sci. 20: 207-214.
106. Lewis-Peacock, J.A. & B.R. Postle . 2008. Temporary activation of long-term memory supports working memory. J. Neurosci. 28: 8765-8771.
107. Kuhl, B.A., J. Rissman, M.M. Chun & A.D. Wagner . 2011. Fidelity of neural reactivation reveals competition between memories. Proc. Natl. Acad. Sci. U. S. A. 108: 5903-5908.
108. Zeithamova, D., A.L. Dominick & A.R. Preston . 2012. Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference. Neuron 75: 168-179.
109. Xue, G., Q. Dong, C. Chen, et al. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science 330: 97-101.
110. Nosofsky, R.M. 1991. Tests of an exemplar model for relating perceptual classification and recognition memory. J. Exp. Psychol. Hum. Percept. Perform. 17: 3-27.
111. Gillund, G. & R.M. Shiffrin . 1984. A retrieval model for both recognition and recall. Psychol. Rev. 91: 1.
112. Hintzman, D.L. 1988. Judgments of frequency and recognition memory in a multiple-trace memory model. Psychol. Rev. 95: 528-551.
113. Shiffrin, R.M. & M. Steyvers . 1997. A model for recognition memory: REM-retrieving effectively from memory. Psychon. Bull. Rev. 4: 145-166.
114. Sakamoto, Y. & B.C. Love . 2004. Schematic influences on category learning and recognition memory. J. Exp. Psychol. Gen. 133: 534-553.
115. Kuhl, B.A., J. Rissman & A.D. Wagner . 2012. Multi-voxel patterns of visual category representation during episodic encoding are predictive of subsequent memory. Neuropsychologia 50: 458-469.
116. LaRocque, K.F., M.E. Smith, V.A. Carr, et al. 2013. Global similarity and pattern separation in the human medial temporal lobe predict subsequent memory. J. Neurosci. 33: 5466-5474.
117. Weisskoff, R.M., J. Baker, J. Belliveau, et al. 1993. Power spectrum analysis of functionally-weighted MR data: what's in the noise. Proc. Soc. Magn. Reson. Med. 1: 7.
118. Purdon, P.L. & R.M. Weisskoff . 1998. Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI. Hum. Brain Mapp. 6: 239-249.
119. Woolrich, M.W., B.D. Ripley, M. Brady & S.M. Smith . 2001. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 14: 1370-1386.
120. Rissman, J., A. Gazzaley & M. D'Esposito . 2004. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage 23: 752-763.
121. Mumford, J.A., B.O. Turner, F.G. Ashby & R.A. Poldrack . 2012. Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. Neuroimage 59: 2636-2643.
122. Mumford, J.A., T. Davis & R.A. Poldrack . In prep. Bias in representational similarity analyses using single trial parameter estimates.
123. Aguirre, G.K., M.G. Mattar & L. Magis-Weinberg . 2011. de Bruijn cycles for neural decoding. Neuroimage 56: 1293-1300.
124. Hanke, M., Y.O. Halchenko, P.B. Sederberg, et al. 2009. PyMVPA: a Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics 7: 37-53.
125. Coutanche, M.N. & S.L. Thompson-Schill . 2012. The advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs. Neuroimage 61: 1113-1119.
126. Fodor, J.A. 1983. The Modularity of Mind: An Essay on Faculty Psychology. The MIT Press. Cambridge MA.
127. Poldrack, R.A. 2006. Can cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10: 59-63.
128. Palmer, S.E. & K.B. Schloss . 2010. An ecological valence theory of human color preference. Proc. Natl. Acad. Sci. U. S. A. 107: 8877-8882.
129. Posner, M.I. & S.W. Keele . 1968. On the genesis of abstract ideas. J. Exp. Psychol. 77: 353-363.
130. Love, B.C. 2003. The multifaceted nature of unsupervised category learning. Psychon. Bull. Rev. 10: 190-197.
131. Pothos, E.M. & N. Chater . 2002. A simplicity principle in unsupervised human categorization. Cogn. Sci. 26: 303-343.
132. Carp, J. 2012. On the plurality of (methodological) worlds: estimating the analytic flexibility of fMRI experiments. Front. Neurosci. 6: 149.
133. Carp, J. 2012. The secret lives of experiments: methods reporting in the fMRI literature. Neuroimage 63: 289-300.
134. Poldrack, R.A. 2012. The future of fMRI in cognitive neuroscience. Neuroimage 62: 1216-1220.
135. Harrell, F.E. 2010. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer-Verlag.
136. Markman, A.B. 1998. Knowledge Representation. Lawrence Erlbaum.
137. Cilibrasi, R.L. & P.M. Vitanyi . 2007. The google similarity distance. IEEE Trans. Knowl. Data Eng. 19: 370-383.
138. Laurence, S. & E. Margolis . 2001. The poverty of the stimulus argument. Br. J. Philos. Sci. 52: 217-276.