Temporal and spatial patterns in perceptual behavior: Implications for dynamical structure

Chaos and Complexity in Psychology: The Theory of Nonlinear Dynamical Systems

Volumn , Issue , 2011, Pages 132-176

  Aks, Deborah J ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77957607338     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781139058544.006     Document Type: Chapter

References (128)

1. Aks, D. J. (2005). 1/f dynamic in complex visual search: Evidence for self-organized criticality in human perception. Retrieved November 29, 2007, from http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
2. Aks, D. J., & Enns, J. T. (1992). Visual search for direction of shading is influenced by apparent depth. Perception and Psychophysics, 52, 63–74.
3. Aks, D. J., & Enns, J. T. (1996). Visual search for size is influenced by a background texture gradient. Journal of Experimental Psychology: Human Perception and Performance, 22, 1467–1481.
4. Aks, D. J., Nokes, T., Sprott, J. C., & Keane, E. (1998, November). Resolving perceptual ambiguity in the Necker cube: A dynamical systems approach. Paper presented at the Psychonomics Society Meeting, Dallas, TX.
5. Aks, D. J., & Sprott, J. C. (2003). The role of depth and 1/f dynamics in perceiving reversible figures. Nonlinear Dynamics, Psychology, and Life Sciences, 7, 161–180.
6. Aks, D. J., Zelinsky, G. J., & Sprott, J. C. (2002). Memory across eye movements: 1/f dynamic in visual search. Nonlinear Dynamics, Psychology, and Life Sciences, 6, 1–25.
7. Bak, P. (1996). How nature works. New York: Springer-Verlag.
8. Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59, 381–384.
9. Barabási, A., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
10. Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006). Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences, 103, 19518–19523.
11. Beer, R.D.(2000). Dynamical approaches to cognitive science.Trends in Cognitive Science, 4, 91–99.
12. Beggs, J. M., & Plenz, D. (2003). Neural avalanches in neocortical circuits. Journal of Neuroscience, 23, 1167–1177.
13. Beggs, J. M. & Plenz D.(2004). Neural avalanches are diverse and precise activitypatterns that are stable for many hours in cortical slice cultures. Journal of Neuroscience, 24, 5216–5229.
14. Beintema, J. A., van Loon, E. M., & van den Berg, A. V. (2005). Manipulating saccadic decision-rate distributions in visual search. Journal of Vision, 5, 150–164.
15. Beltz, B. C., & Kello, C. T. (2004, August). The effect of cue predictability on long-range dependencies in response times and response durations. Paper presented at the Proceedings of the 26th Annual Meeting of Cognitive Science, Chicago, IL.
16. Benichou, O., Coppey, M., Moreau, M., Suet, P. H., & Voituriez, R. (2005). Optimal search strategies for hidden targets. Physical Review Letters,94, 198101–198104.
17. Bergen, J. R., & Julesz, B. (1983). Parallel versus serial processing in rapid pattern discrimination. Nature, 303, 696–698.
18. Brascamp, J. W., van Ee, R., Pestman, W. R., & Van Den Berg, A. V. (2005). Distributions of alternation rates in various forms of bistable perception. Journal of Vision, 5, 287–298.
19. Brecht, M., Singer, W., & Engel, A. E. (1999). Patterns of synchronization in superior solliculus of anaesthetized cats. Journal of Neuroscience, 19, 3567–3579.
20. Buzsáki, G. (2006). Rhythms of the brain. Oxford: Oxford University Press.
21. Carello, C. M., & Moreno, M. A. (2005). Why nonlinear methods?Retrieved November, 29, 2007, from http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
22. Carpenter, R. H. S. (1981). Oculomotor procrastination. In D. F. Fisher, R. A. Monty, & J. W. Senders (Eds.), Eye movements: Cognition and visual perception(pp. 237–246). Hillsdale, NJ: Erlbaum.
23. Chen, Y., Ding, M., & Kelso, J. A. (2001). Origins of timing errors in human sensorimotor coordination. Journal of Motor Behavior, 3, 3–8.
24. Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2007). Power-law distributions in empirical data [Electronic version]. Retrieved November, 29, 2007, from http://arxiv.org/abs/0706.1062v1
25. Clayton, K., & Frey, B. B. (1997). Studies of mental “noise.” Nonlinear Dynamics in Psychology and Life Science, 1, 173–180.
26. Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433, 513–516.
27. Deco, G., Pollatos, O., & Zihl, J. (2002). The time course of selective visual attention: Theory and experiments. Vision Research, 42, 2925–2945.
28. Deco, G., & Zihl, J. (2001). Top-down selective visual attention: A neurodynamical approach. Visual Cognition, 8, 118–139.
29. Delignières, D., Ramdani, S., Lemoine, L., Torre, K., Fortes, M., & Ninot, G. (2006). Fractal analyses for “short” time series: A re-assessment of classical methods. Journal of Mathematical Psychology, 50, 525–544.
30. Delignières, D., Torre, K., & Lemoine, L. (2005). Methodological issues in the application of monofractal analyses in psychological and behavioral analyses. Nonlinear Dynamics, Psychology, and Life Sciences, 9, 435–461.
31. Ding, M., Chen, Y., & Kelso, J. A. (2002). Statistical analysis of timing errors. Brain Cognition, 48, 98–106.
32. Duchowski, A. (2005). Eye-based interaction in graphical systems: Theory and practice [Electronic version]. Retrieved November, 29, 2007, from http://www.vr.clemson.edu/eyetracking/sigcourse
33. Duncan, J. H., & Humphrey, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96, 433–458.
34. Edelman, G. M., & Tononi, G. (2000). Consciousness: How matter becomes imagination. London: Penguin.
35. Engel, A. K., & Singer, W. (2001). Temporal binding and the neural correlates of sensory awareness. Trends in Cognitive Sciences, 5, 16–25.
36. Farid, H., & Adelson, T. (2001). Synchrony does not promote grouping in temporally structured displays. Nature Neuroscience, 4, 875–876.
37. Feder, J. (1988). Fractals. New York: Plenum Press.
38. Findlay, J. M., Brown, V., & Gilchrist, I. D. (2001). Saccade target selection in visual search: The effect of information from the previous fixation. Vision Research, 41, 87–95.
39. Findlay, J. M., & Gilchrist, I. D. (2003). Active vision: The psychology of looking and seeing. Oxford: Oxford University Press.
40. Freeman, W. J. (1991). The physiology of perception. Scientific American, 264, 78–85.
41. Freeman, W. J. (2001). How brains make up their minds. New York: Columbia University Press.
42. Gao, J. B., Billock, V. A., Merk, I., Tung, W. W., White, K. D., et al. (2006). Inertia and memory in ambiguous visual perception. Cognitive Processing, 7, 105–112.
43. Gilden, D. L. (1997). Fluctuations in the time required for elementary decisions. Psychological Science, 8, 296–301.
44. Gilden, D. L. (2001). Cognitive emissions of 1/f noise. Psychological Review, 108, 33–56.
45. Gilden, D. L., Thornton, T., & Mallon, M. W. (1995). 1/f noise in human cognition. Science, 267, 1837–1839.
46. Glimcher, P. (2003). Decisions, uncertainty, and the brain: The science of neuroeconomics. Cambridge, MA: MIT Press.
47. Gong P., Nikolaev, A. R., & van Leeuwen C. (2003). Scale-invariant fluctuations of the dynamical synchronization in human brain electrical activity. Neuroscience Letters, 336, 33–36.
48. Guastello, S. J. (2005). Statistical distributions and self-organizing phenomena: What conclusions should be drawn? Nonlinear Dynamics in Psychology and Life Science, 9, 463–478.
49. Hohle, R. H. (1965). Inferred components of reaction time as functions of fore period duration. Journal of Experimental Psychology, 69, 382–386.
50. Hooge, I. T. C., & Erkelens, C. J. (1998). Adjustment of fixation duration in visual search. Vision Research, 3, 1295–1302.
51. Hooge, I. T. C., Over, E. A. B., van Wezel, R. J. A., & Frens, M. A. (2005). Inhibition of return is not a foraging facilitator in saccadic search and free viewing. Vision Research, 45, 1901–1908.
52. Horowitz, T. S., & Wolfe, J. M. (1998). Visual search has no memory. Nature, 357, 575–577.
53. Horowitz, T. S., & Wolfe, J. M. (2001). Search for multiple targets: Remember the targets, forget the search. Perception and Psychophysics, 63, 272–285.
54. Itti, L., & Koch, C. (2001). Computational modeling of visual attention. Nature Reviews, 2, 1–11.
55. Jensen, H. J. (1998). Self-organized criticality. Cambridge, MA: Cambridge University Press.
56. Kadanoff, L. P., Nagel, S. R., Wu, L., & Zhou, S. (1989). Scaling and universality in avalanches. Physical Review A, 39, 6524–6537.
57. Kapoula, Z., & Robinson, D. A. (1986). Saccade undershoot is not inevitable: Saccades can be accurate. Vision Research, 26, 735–743.
58. Kastner, S., & Pinsk, M. A. (2004). Visual attention as a multilevel selection process. Cognition, Affect and Behavioral Neuroscience, 4, 483–500.
59. Kelso, S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.
60. Klein, R. M., & MacInnes, W. J. (1999). Inhibition of return is a foraging facilitator in visual search. Psychological Science, 10, 346–352.
61. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59–69.
62. Kovacs, I., Papathomas, T. V., Yang, M., & Feher, A. (1996). When the brain changes its mind: Interocular grouping during binocular rivalry. Proceeding of National Academy of Sciences, 93, 15508–15511.
63. Kowler, E., Martins, A. J., & Pavel, M. (1984). The effect of expectations on slow oculomotor control – IV. Anticipatory smooth eye movements depend on prior target motions. Vision Research, 24, 197–210.
64. Kwok, T., & Smith, P. L. (2005). Optimization via intermittency with a self-organizing neural network. Neural Computation, 17, 2454–2481.
65. Liebovitch, L. S. (1998). Fractals and chaos: Simplified for the life sciences. Oxford: Oxford University Press.
66. Lin, M., & Chen, T. (2005). Self-organized criticality in a simple model of neurons based on small-world networks. Physical Review E, 71, 016133–016135.
67. Linkenkaer-Hansen, K., Nikouline, V. V., Palva, J. M., Kaila, K., & Ilmoniemi, R. J. (2004). Stimulus-induced change in long-range temporal correlations and scaling behaviour of sensorimotor oscillations. European Journal ofNeuroscience, 19, 203–211.
68. Lumer, E. D., Edelman, G. M., & Tononi, G. (1997). Neural dynamics in a model of the thalamocortical system: Layers, loops, and the emergence of fast synchronous rhythms. Cerebral Cortex, 7, 207–227.
69. Mazer, J. A., & Gallant, J. L. (2003). Goal-related activity in V4 during free viewing visual search: Evidence for a ventral stream visual salience map. Neuron, 40, 1241–1250.
70. McGill, W. J. (1963). Stochastic latency mechanisms. In R. D. Luce, R. R. Bush, & E. Galanter (Eds.), Handbook of Mathematical Psychology(Vol. I, pp. 309–360). New York: Wiley.
71. McIlwain, J. T. (1991). Distributed spatial coding in the superior colliculus: A review. Visual Neuroscience, 6(1), 3–13.
72. McPeek, R. M., Skavenski, A. A., & Nakayama, K. (2000). Concurrent processing of saccades in visual search. Vision Research, 40, 2499–2516.
73. Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46, 323–351.
74. Ögmen, H., & Breitmeyer, B. G. (2006). The first half second: The microgenesis and temporal dynamics of unconscious and conscious visual processes. Cambridge, MA: MIT Press.
75. Over, E. A. B., Hooge, I. T. C., Vlaskamp, B. N. S., & Erkelens, C. J. (2007). Coarse-to-fine eye movement strategy in visual search. Vision Research, 47, 2272–2280.
76. Palmer, E. M., Wolfe, J. M., & Horowitz, T. S. (2004). Response time distributions constrain models of visual search [Abstract]. Journal of Vision, 4, 674.
77. Paczuski, M., Maslov, S., & Bak, P. (1996). Avalanche dynamics in evolution, growth and depinning models. Physical Review E, 53, 414–443.
78. Peak, D., & Frame, M. (1994). Chaos under control: The art and science of complexity. New York: Freeman.
79. Pfeifer, R., & Bongard, J. (2007). How the body shapes the way we think: A new view of intelligence. Cambridge, MA: Bradford – MIT Press.
80. Plenz, D., & Thiagarajan, T. C. (2006). The organizing principles of neural avalanches: cell assemblies in the cortex. Trends in Neurosciences, 30, 101–110.
81. Posner, M. I. (1986). Chronometric exploration of mind. Oxford, England: Oxford University Press.
82. Posner, M. I. (2005). Timing the brain: Mental chronometry as a tool in neuroscience. Public Library of Science Biology, 3, e51.
83. Posner, M. I., & Cohen, Y. (1984). Components of attention. In H. Bouma & D. G. Bouwhuis (Eds.), Attention and performance X(pp. 55–66). Hillsdale, NJ: Erlbaum.
84. Press, W. H., Flannery, B. P., Teukolsky S. A., & Vetterling, W. T. (1986). Numerical recipes. Cambridge: Cambridge University Press.
85. Rodriguez, E., George, N., Lachaux, J. P., Martinerie, J., Renault, B., & Varela, F. J. (1999). Perception’s shadow: Long-distance synchronization of human brain activity. Nature, 397, 430–433.
86. Shadlen, M. N., & Movshon, J. A. (1999). Synchrony unbound: A critical evaluation of the temporal binding hypothesis. Neuron, 24, 67–77.
87. Shelhamer, M. (2005a). Sequences of predictive saccades are correlated over a span of ~2 s and produce a fractal time series. Journal of Neurophysiology, 93, 2002–2011.
88. Shelhamer, M. (2005b). Sequences of predictive eye movements form a fractional Brownian series – implications for self-organized criticality in the oculomotor system. Biological Cybernetics, 93, 43–53.
89. Shelhamer, M. (2006). Nonlinear dynamics in physiology: A state-space approach. Singapore: World Scientific.
90. Shelhamer, M., & Joiner, W. M. (2003). Saccades exhibit abrupt transition between reactive and predictive; predictive saccade sequences have long-term correlations. Journal of Neurophysiology, 90, 2763–2769.
91. Singer, W. (1993). Synchronization of cortical activity and its putative role in information processing and learning. Human Perception and Performance, 21, 901–913.
92. Singer, W. (1999). Response synchronization: A universal coding strategy for the definition of relations. In M. S. Gazzaniga (Ed.), The cognitive neurosciences(Vol. 21, pp. 901–913). Cambridge, MA: MIT Press.
93. Singer, W., & Gray, C. M. (1995). Visual feature integration and the temporal correlation hypothesis. Annual Review of Neuroscience, 18, 555–586.
94. Skarda, C., & Freeman W. J. (1987). How brains make chaos in order to make sense of the world. Brain and Behavioral Science, 10, 161–195.
95. Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in Neurosciences, 27, 161–168.
96. Sommer, M. A. (1994). Express saccades elicited during visual scan in the monkey. Vision Research, 34, 2023–2038.
97. Sornette, D. (2004). Critical phenomena in natural sciences: Chaos, fractals, self-organization and disorder; concepts and tools. New York: Springer Publishing Company.
98. Spivey, M. (2007). The continuity of mind. Oxford: Oxford University Press.
99. Sprott, J. C. (2003). Chaos and time-series analysis. New York: Oxford University Press.
100. Stam, C. J., & de Bruin, E. A. (2004). Scale-free dynamics of global functional connectivity in the human brain. Human Brain Mapping, 22, 97–109.
101. Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders’ method. Acta Psychologica, 30, 276–315.
102. Stone, A. C. (2006). Stone Works: Starmores (Version 1.0 [2]). Albuquerque, NM: Andrew C. Stone & Stone Design.
103. Sumpter, D. J. T. (2006). Review. The principles of collective animal behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences,361, 5–22.
104. Thornton, T. L., & Gilden, D. L. (2005). Provenance of correlations in psychological data. Psychonomic Bulletin and Review, 12, 409–441.
105. Tononi, G., & Edelman, G. M. (1998). Consciousness: How matter becomes imagination. Science, 282, 1846–1185.
106. Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12, 97–136.
107. Usher, M., Stemmler, M., & Olami, Z. (1995). Dynamic pattern formation leads to 1/f noise in neural populations. Physical Review Letters, 74, 326–329.
108. Van Loon, E. M., Hooge, I. T., & van den Berg, A. V. (2002). The timing of sequences of saccades in visual search. Proceedings of Biological Sciences, 269, 1571–1579.
109. Van Orden, G. C., Holden, J. G., & Turvey, M. T. (2003). Self-organization of cognitive performance. Journal of Experimental Psychology: General, 132, 331–350.
110. Van Orden, G. C., Holden, J. G., & Turvey, M. T. (2005). Human cognition and 1/f scaling. Journal of Experimental Psychology: General, 134, 117–123.
111. Varela, F. (1999). The specious present: A neurophenomenology of time consciousness. In F. J. V. Jean Petitot, B. Pachoud & J. M. Roy (Eds.), Naturalizing phenomenology: Issues in contemporary phenomenology and cognitive science(pp. 266–329). Stanford, CA: Stanford University Press.
112. Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2, 229–239.
113. Varela, F., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.
114. Vaughan, J., & Graefe, T. (1977). Delay of stimulus presentation after the saccade in visual search. Perception and Psychophysics,22, 201–205.
115. Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., et al. (2007). Intrinsic functional architecture in the anaesthetized monkey brain. Nature, 447, 83–86.
116. Wagenmakers, E. J., Farrell, S., & Ratcliff, R. (2004). Estimation and interpretation of 1/f alpha noise in human cognition. Psychonomics Bulletin and Review, 1, 579–615.
117. Wagenmakers, E. J., Farrell, S., & Ratcliff, R. (2005). Human cognition and a pile of sand: A discussion on serial correlations and self-organized criticality. Journal of Experimental Psychology: General, 134, 108–116.
118. Ward, L. M. (2002). Dynamical cognitive science. Cambridge MA: MIT Press.
119. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.
120. Webber, C. L., Jr., & Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Psychology, 76, 965–973.
121. Werner, M. D. G. (2006). Brain dynamics across levels of organization [Electronic version]. Retrieved November 29, 2007, from http://cogprints.org/5275/
122. Werner, M. D. G. (2007). Perspectives on the neuroscience of cognition and consciousness. BioSystems, 87, 82–95.
123. Wolfe, J. M. (1994). Guided search 2.0: A revised model of visual search. Psychonomic Bulletin and Review, 1, 202–238.
124. Wolfe, J. M. (1998a). Visual search. In H. Pashler (Ed.), Attention(pp. 13–71). England, London, UK: University College Press.
125. Wolfe, J. M. (1998b). What can 1,000,000 trials tell us about visual search? Psychological Science, 9, 33–39.
126. Wolfe, J. M., Butcher, S. J., Lee, C., & Hyle, M. (2003). On the contributions of top-down and bottom-up guidance in visual search for feature singletons. Journal of Experimental Psychology: Human Perception and Performance, 29, 483–502.
127. Yates, F. E. (1994). Order and complexity in dynamical systems: Homeodynamics as a generalized mechanics for biology. Mathematical and Computer Modeling, 19, 49–74.
128. Zingale, C. M., & Kowler, E. (1987). Planning sequences of saccades. Vision Research, 27, 1327–1337.