Perspectives on Modeling in Cognitive Science

Topics in Cognitive Science

Volumn 2, Issue 4, 2010, Pages 736-750

  Shiffrin, Richard M ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

Cognitive science; Mathematical; Models; Perspectives

Indexed keywords

HISTORY; HUMAN; PSYCHOLOGY; THEORETICAL MODEL; TRENDS;

COGNITIVE SCIENCE; HISTORY, 20TH CENTURY; HISTORY, 21ST CENTURY; HUMANS; MODELS, THEORETICAL;

EID: 79955121217     PISSN: 17568757     EISSN: 17568765     Source Type: Journal    
DOI: 10.1111/j.1756-8765.2010.01092.x     Document Type: Article

References (65)

1. Anderson, J. A. (1973). A theory for the recognition of items from short memorized lists. Psychological Review, 1973, 80.
2. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum.
3. Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Erlbaum.
4. Anderson, J. R., & Bower, G. H. (1973). Human associative memory. Washington, DC: Winston and Sons.
5. Anderson, J. R., Carter, C. S., Fincham, J. M., Qin, Y., Ravizza, S. M., & Rosenberg-Lee, M. (2008). Using fMRI to test models of complex cognition. Cognitive Science, 32, 1323-1348.
6. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89-195). New York: Academic Press.
7. Batchelder, W. H., & Riefer, D. M. (1999). Theoretical and empirical review of multinomial processing tree modeling. Psychonomic Bulletin & Review, 6, 57-86.
8. Beer, R. D. (in press). Dynamical systems and embedded cognition. To appear in K. Frankish & W. Ramsey (Eds.), The Cambridge handbook of artificial intelligence. Cambridge, England: Cambridge University Press.
9. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
10. Bjork, R. A. (1968). All-or-none subprocesses in the learning of complex sequences. Journal of Mathematical Psychology, 5, 182-195.
11. Bower, G. (1961). Application of a model to paired-associate learning. Psychometrica, 26, 3.
12. Bullmore, E. T., & Sporns, O. (2009). Complex brain networks: Graph-theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186-198.
13. Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006). Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50, 220-241.
14. Carroll, J., & Chang, J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of the "Eckhard-Young" composition. Psychometrika, 35, 283-319.
15. Dzhafarov, E. N. (2008). Dissimilarity cumulation theory in smoothly-connected spaces. Journal of Mathematical Psychology, 52, 93-115.
16. Estes, W. K. (1950). Toward a statistical theory of learning. Psychological Review, 57, 2.
17. Geisler, W. S. (1984). The physical limits of acuity and hyperacuity. Journal of the Optical Society of America A, 1, 775-782.
18. Geisler, W. S., & Albrecht, D. G. (2000). Spatial vision: Single neurons and perception. Handbook of cognition and perception. New York: Academic Press.
19. Geisler, W. S., Perry, J. S., Super, B. J., & Gallogly, D. P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41, 711-724.
20. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: John Wiley and Sons.
21. Griffiths, T., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl. 1), 5228-5235.
22. Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2), 211-244.
23. Grossberg, S. (1968). Some nonlinear networks capable of learning a spatial pattern of arbitrary complexity. Proceedings of the National Academy of Sciences of the United States of America, 59, 368-372.
24. Grossberg, S. (1969). Embedding fields: A theory of learning with physiological implications. Journal of Mathematical Psychology, 6, 209-239.
25. Grünwald, P. D. (2007). The minimum description length principle. Cambridge, MA: MIT Press.
26. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Frund, I., Rieger, J. W., Herrmann, C. S., Hanson, S. J., Haxby, J. V., & Pollmann, S. (2009). PyMVPA: A unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3, 3.
27. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
28. Honey, C. J., Kötter, R., Breakspear, M., & Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104, 10240-10245.
29. Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37.
30. Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307-321.
31. Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences of the United States of America, 105(31), 10687-10692.
32. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211-240.
33. Link, S. W., & Heath, R. A. (1975). A sequential theory of psychological discrimination. Psychometrika, 40, 77-105.
34. Lu, Z.-L., & Dosher, B. (2008). Characterizing observers using external noise and observer models: Assessing internal representations with external noise. Psychological Review, 115(1), 44-82.
35. Luce, D. (1959). Individual choice behavior: A theoretical analysis. New York: Wiley.
36. McClelland, J. L., & Elman, J. L. (1986). The TRACE model of speech perception. Cognitive Psychology, 18, 1-86.
37. McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: Part 1. An account of basic findings. Psychological Review, 88, 375-407.
38. McClelland, J. L., Rumelhart, D. E., & The PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. II). Cambridge, MA: MIT Press.
39. Medin, D. L., & Schaffer, M. M. (1978). A context theory of classification learning. Psychological Review, 85, 207-238.
40. Meyer, D. E., & Kieras, D. E. (1997a). A computational theory of executive cognitive processes and multi-ple-task performance: Part 1. Basic mechanisms. Psychological Review, 104, 3-65.
41. Meyer, D. E., & Kieras, D. E. (1997b). A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena. Psychological Review, 104, 749-791.
42. Narens, L. (2007). Introduction to the theories of measurement and meaningfulness and the use of invariance in science. Mahwah, NJ: Lawrence Erlbaum Associates.
43. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Cambridge University Press.
44. Nosofsky, R. M. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 104-114.
45. Parra, L. C., Christoforou, C., Gerson, A. D., Dyrholm, M., Luo, A., Wagner, M., Philiastides, M. G., & Sajda, P. (2008). Spatio-temporal linear decoding of brain state: Application to performance augmentation in high-throughput tasks. IEEE Signal Processing Magazine, 25(1), 95-115.
46. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo: Morgan Kaufmann.
47. Pitt, M. A., Kim, W., Navarro, D. J., & Myung, J. I. (2006). Global model analysis by parameter space partitioning. Psychological Review, 113, 57-83.
48. Pitt, M. A., Myung, J. I., Montenegro, M., & Pooley, J. (2008). Measuring model flexibility with parameter space partitioning: An introduction and application example. Cogntive Science, 32, 1285-1303.
49. Pothos, E. M., & Busemeyer, J. R. (2009). Quantum probability explanation for violations of 'rational' decision theory. Proceedings of the Royal Society of London. Series B: Biological Sciences, 276(1165), 2171-2178.
50. Raaijmakers, J. G. W., & Shiffrin, R. M. (1980). SAM: A theory of probabilistic search of associative memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 14, pp. 207-262). New York: Academic Press.
51. Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93-134.
52. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108.
53. Ratcliff, R., Philiastides, M. G., & Sajda, P. (2009). Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proceedings of the National Academy of Sciences of the United States of America, 106(16), 6539-6544.
54. 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: Current theory and research (pp. 64-99). New York: Appleton-Century-Crofts.
55. Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408.
56. Rumelhart, D. E., & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: Part 2. The context enhancement effect and some tests and extensions of the model. Psychological Review, 89, 60-94.
57. Rumelhart, D. E., McClelland, J. L., & the PDP research group (1986). Parallel distributed processing: Explorations in the microstructure of cognition (Vol. I). Cambridge, MA: MIT Press.
58. Sederberg, P. B., Schulze-Bonhage, A., Madsen, J. R., Bromfield, E. B., McCarthy, D. C., Brandt, A., Tully, M. S., & Kahana, M. J. (2007). Hippocampal and neocortical gamma oscillations predict memory formation in humans. Cerebral Cortex, 17, 1190-1196.
59. Shepard, R. N. (1962a). The analysis of proximities: Multidimensional scaling with an unknown distance function. I. Psychometrika, 27, 125-140.
60. Shepard, R. N. (1962b). The analysis of proximities: Multidimensional scaling with an unknown distance function II. Psychometrika, 27, 219-246.
61. Shiffrin, R. M. (1970). Memory search. In D. A. Norman (Ed.), Models of memory (pp. 375-447). New York: Academic Press.
62. Shiffrin, R. M., Lee, M. D., Wagenmakers, E. J., & Kim, W. J. (2008). A survey of model evaluation approaches with a focus on hierarchical Bayesian methods. Cognitive Science, 32(8), 1248-1284.
63. Torgerson, W. S. (1958). Theory & methods of scaling. New York: Wiley.
64. Townsend, J. T., & Wenger, M. J. (2004). A theory of interactive parallel processing: New capacity measures and predictions for a response time inequality series. Psychological Review, 111, 1003-1035.
65. Yuille, A. L., & Kersten, D. (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Neuroscience, 10(7), 301-308.