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Cognitive emissions of 1/f noise.
Abstract
The residual fluctuations that naturally arise in experimental inquiry are analyzed in terms of their time histories. Although these fluctuations are generally relegated to a statistical purgatory known as unexplained variance, this article shows that they may harbor a long-term memory process known as 1/f noise. This type of noise has been encountered in a number of biological and physical systems and is theorized to be a signature of dynamic complexity. Its presence in psychological data appears to be associated with the most elementary aspect of cognitive process, the formation of representations.
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“For example, coherent spontaneous activity appears to be an important source of trial-to-trial variability in measured brain responses (42,43) and may relate to the trialto-trial variability commonly observed in human behavior (44).”
From Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems (2006) · cites this paper as [44]
Citations
Cites
Savitzky-Golay Smoothing Filters
Press, W. H., & Teukolsky, S. A. (1990) · Computers in Physics · doi:10.1063/1.4822961 · Cited by 11,851
Self-organized criticality: An explanation of the 1/ f noise
Bak, P., Tang, C., & Wiesenfeld, K. A. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.381 · Cited by 7,644
Bak, P., Tang, C., & Wiesenfeld, K. A. (1988) · Physical Review A · doi:10.1103/physreva.38.364 · Cited by 4,744
(1989) · Long Range Planning · doi:10.1016/0024-6301(89)90186-6 · Cited by 3,449
Bak, P. (1996) · Springer eBooks · doi:10.1007/978-1-4757-5426-1 · Cited by 2,830
Statistics for Long-Memory Processes.
Ray, W. D., & Beran, J. (1996) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983481 · Cited by 2,716
Gleick, J., Goodwin, B. M., Stewart, I., & Hanson, B. E. (1987) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,707
Intermittent transition to turbulence in dissipative dynamical systems
Yves Pomeau, A., & Manneville, P. (1980) · Communications in Mathematical Physics · doi:10.1007/bf01197757 · Cited by 2,032
Stelmach, G. E., & Requin, J. (1980) · Advances in psychology · doi:10.1016/s0166-4115(08)x6078-9 · Cited by 1,705
Mental Images and Their Transformations
D., C. P., Shepard, R. N., & Cooper, L. A. (1983) · The American Journal of Psychology · doi:10.2307/1422825 · Cited by 1,689
Research works printed but not linked: 3
- Hays, 1988, Statistics.
- Luce, 1986, Response times.
- Press, 1992, Numerical recipes.
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Heiby, E. M., Pagano, I., Blaine, D. D., Nelson, K., & Heath, R. A. (2003) · Psychological Assessment · doi:10.1037/1040-3590.15.3.426 · Cited by 34
Madison, G., & Delignières, D. (2008) · Experimental Brain Research · doi:10.1007/s00221-008-1652-x · Cited by 34
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Glaze, C. M., & Troyer, T. W. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1065-07.2007 · Cited by 33
Torre, K., & Balasubramaniam, R. (2010) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0020556 · Cited by 33
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Orden, G. C. V. (2010) · Medicina · doi:10.3390/medicina46090083 · Cited by 30
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Banerjee, J., Verma, M. K., Manna, S. S., & Ghosh, S. (2006) · Europhysics Letters (EPL) · doi:10.1209/epl/i2005-10418-2 · Cited by 28
Rosenbaum, D. Α. (2002) · Wiley eBooks · doi:10.1002/0471214426.pas0108 · Cited by 28
Ruiter, N. M. P. D., Hartigh, R. J. R. D., Cox, R. F. A., van Geert, P. L. C., & Kunnen, E. S. (2014) · Self and Identity · doi:10.1080/15298868.2014.994026 · Cited by 29
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Metadata
- Authors
- Gilden, D. L.
- Affiliations
- The University of Texas at Austin (US)
- Corresponding author
- Gilden, David L.
- ORCID
- Gilden, David L.
- Venue
- Psychological Review
- Published
- 2001
- Volume
- 108
- Issue
- 1
- Pages
- 33-56
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2096018883
- Cited by
- 516
- References
- 72
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 20 · 2022: 14 · 2021: 23 · 2020: 20 · 2019: 52 · 2018: 25 · 2017: 16 · 2016: 13 · 2015: 20 · 2014: 28 · 2013: 23 · 2012: 31
- FWCI
- 6.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (1 RO1-MH58606-01, R01MH058606)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- 1/f noise, long-range dependence, mental representation, residual variance, time series analysis, dynamic complexity, cognitive processes, psychological data
- MeSH terms
- Cognition, Discrimination, Psychological, Humans, Memory, Psychology, Experimental, Reaction Time, Signal Detection, Psychological, Cognitive Science
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