Interactions dominate the dynamics of visual cognition
暂无分享,去创建一个
[1] M. Tanenhaus,et al. The time course of spoken word learning and recognition: studies with artificial lexicons. , 2003, Journal of experimental psychology. General.
[2] R. Duncan Luce,et al. Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .
[3] Cees van Leeuwen,et al. A pragmatic approach to multi-modality and non-normality in fixation duration studies of cognitive processes , 2008 .
[4] Alan D. Baddeley,et al. Attention: Selection, Awareness, and Control , 1993 .
[5] Deborah J. Aks,et al. Memory Across Eye-Movements: 1/f Dynamic in Visual Search , 2010 .
[6] Julie C. Sedivy,et al. Subject Terms: Linguistics Language Eyes & eyesight Cognition & reasoning , 1995 .
[7] John R. Anderson,et al. Reflections of the Environment in Memory Form of the Memory Functions , 2022 .
[8] John G. Holden,et al. Fractal Characteristics of Response Time Variability , 2002 .
[9] R. Mantegna,et al. Quantitative analysis of senile plaques in Alzheimer disease: observation of log-normal size distribution and molecular epidemiology of differences associated with apolipoprotein E genotype and trisomy 21 (Down syndrome). , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[10] Delignières Didier,et al. Testing for the Presence of 1/f Noise in Continuation Tapping Data , 2022 .
[11] Mark S. Seidenberg,et al. Semantic feature production norms for a large set of living and nonliving things , 2005, Behavior research methods.
[12] D. O. Hebb,et al. The organization of behavior , 1988 .
[13] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[14] Ralf Engbert,et al. An iterative algorithm for the estimation of the distribution of mislocated fixations during reading. , 2007 .
[15] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[16] James L. McClelland. Toward a theory of information processing in graded, random, and interactive networks , 1993 .
[17] D. Gilden. Cognitive emissions of 1/f noise. , 2001, Psychological review.
[18] A. Baddeley,et al. Attention : selection, awareness, and control : a tribute to Donald Broadbent , 1996 .
[19] B. Kay. The dimensionality of movement trajectories and the degrees of freedom problem: A tutorial , 1988 .
[20] A. Treisman,et al. A feature-integration theory of attention , 1980, Cognitive Psychology.
[21] T. S. Lee,et al. Dynamics of subjective contour formation in the early visual cortex. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[22] James L. McClelland,et al. Developing a domain-general framework for cognition: What is the best approach? , 2003, Behavioral and Brain Sciences.
[23] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[24] Daniel Mirman,et al. Lévy-like diffusion in eye movements during spoken-language comprehension. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[25] A. M. Edwards,et al. Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer , 2007, Nature.
[26] G. V. van Orden,et al. Self-organization of cognitive performance. , 2003, Journal of experimental psychology. General.
[27] David R. Anderson,et al. Model selection and multimodel inference : a practical information-theoretic approach , 2003 .
[28] Reinhold Kliegl,et al. Mathematical models of eye movements in reading: a possible role for autonomous saccades , 2001, Biological Cybernetics.
[29] W. Kendal,et al. A stochastic model for the self-similar heterogeneity of regional organ blood flow. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[30] E. Montroll,et al. On 1/f noise and other distributions with long tails. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[31] W. Stahel,et al. Log-normal Distributions across the Sciences: Keys and Clues , 2001 .
[32] J. Doyne Farmer,et al. A Rosetta stone for connectionism , 1990 .
[33] Mark S. Seidenberg,et al. When does irregular spelling or pronunciation influence word recognition , 1984 .
[34] N. Breslow,et al. Approximate inference in generalized linear mixed models , 1993 .
[35] Arthur B. Markman,et al. Discrete thoughts: Why cognition must use discrete representations , 2003 .
[36] G. V. van Orden,et al. Dispersion of response times reveals cognitive dynamics. , 2009, Psychological review.
[37] Bruce J. West,et al. THE LURE OF MODERN SCIENCE , 1995 .
[38] Anthony Morse,et al. Dynamic liquid association: Complex learning without implausible guidance , 2009, Neural Networks.
[39] James L. McClelland,et al. Semantic Cognition: A Parallel Distributed Processing Approach , 2004 .
[40] Nicolas E. Humphries,et al. Scaling laws of marine predator search behaviour , 2008, Nature.
[41] J. Fodor,et al. The Modularity of Mind: An Essay on Faculty Psychology , 1984 .
[42] James L. McClelland,et al. Conspiracy effects in word pronunciation. , 1987 .
[43] Max Coltheart,et al. Modularity and cognition , 1999, Trends in Cognitive Sciences.
[44] A. Opstal. Dynamic Patterns: The Self-Organization of Brain and Behavior , 1995 .
[45] M. Bar. Visual objects in context , 2004, Nature Reviews Neuroscience.
[46] Christopher T. Kello,et al. The Pervasiveness of 1/f Scaling in Speech Reflects the Metastable Basis of Cognition , 2008, Cogn. Sci..
[47] R. Ratcliff,et al. Estimation and interpretation of 1/fα noise in human cognition , 2004 .
[48] J. Elman,et al. Rethinking Innateness: A Connectionist Perspective on Development , 1996 .
[49] Rolf Ulrich,et al. Simple reaction time and statistical facilitation: A parallel grains model , 2003, Cognitive Psychology.
[50] J. Singer,et al. Applied Longitudinal Data Analysis , 2003 .
[51] C. Schunn,et al. Evaluating Goodness-of-Fit in Comparison of Models to Data , 2005 .
[52] J. B. Levitt,et al. Circuits for Local and Global Signal Integration in Primary Visual Cortex , 2002, The Journal of Neuroscience.
[53] H. Qian,et al. A class of flow bifurcation models with lognormal distribution and fractal dispersion. , 2000, Journal of theoretical biology.
[54] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[55] P. Cariani. Emergence of new signal-primitives in neural systems , 1997 .
[56] Hans V. Westerhoff,et al. Emergence and Its Place in Nature: A Case Study of Biochemical Networks , 2005, Synthese.
[57] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[58] M. Genton,et al. A unified view on skewed distributions arising from selections , 2006 .
[59] S. Andrews,et al. Distinguishing common and task-specific processes in word identification: a matter of some moment? , 2001, Journal of experimental psychology. Learning, memory, and cognition.