Cited by 53Embodied and Extended CognitionNeural dynamics and brain functionPhilosophy and Theoretical Science

Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems

Open access
Yufik, Yan M., Friston, Karl·2016·Frontiers in Systems Neuroscience

Abstract

This article is motivated by the formulation of biotic self-organisation in (Friston, 2013), where the emergence of ‘life’ in coupled material entities (e.g., macromolecules) was predicated on bounded subsets that maintain a degree of statistical independence from the rest of the network. Boundary elements in such systems or units constitute a Markov blanket; separating the internal states of the unit from its surrounding states. In this paper, we ask whether Markov blankets operate in the nervous system and underlie the development of intelligence, enabling a progression from the ability to sense the environment to the ability to understand it. Markov blankets have been previously hypothesized to form in neuronal networks as a result of phase transitions that cause network subsets to fold into bounded assemblies, or packets (Yufik, 1998a). The ensuing neuronal packets hypothesis builds on the notion of neuronal assemblies (Hebb, 1949, 1980), treating such assemblies as flexible but stable biophysical structures capable of withstanding entropic erosion. In other words, structures that maintain their integrity under changing conditions. In this treatment, neuronal packets give rise to perception of ’objects’; i.e., quasi-stable (stimulus bound) groupings that are conserved over multiple presentations (e.g., the experience of perceiving ‘apple’ can be interrupted and resumed many times). Monitoring the variations in such groups enables the apprehension of behaviour; i.e., attributing to objects the ability to undergo changes without loss of self-identity. Ultimately, ‘understanding’ involves self-directed composition and manipulation of the ensuing ‘mental models’ that are constituted by neuronal packets, whose dynamics capture relationships among objects: that is, dependencies in the behaviour of objects under varying conditions. For example, movement is known to involve rotation of population vectors in the motor cortex (Georgopoulos et al, 1993; Georgopoulos et al, 1988). The neuronal packet hypothesis associates ‘understanding’ with the ability to detect and generate coordinated rotation of population vectors – in neuronal packets –in associative cortex and other regions in the brain. The ability to coordinate vector representations in this way is assumed to have developed in conjunction with the ability to postpone overt motor expression of implicit movement, thus creating a mechanism for prediction

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fnsys.2016.00098

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • For self-evidencing generative models, integrating processes underlying SOHM formation would calculate marginal joint posteriors based on specific Bayesian beliefs entailed by patterns of effective connectivity within and between synchronous complexes.

    “Further, for self-evidencing generative models (Hohwy, 2016; Yufik and Friston, 2016; Kirchhoff et al., 2018), integrating processes underlying SOHM formation would calculate marginal joint posteriors based on specific (Bayesian) beliefs entailed by particular patterns of effective connectivity within and between various synchronous complexes.”

    From An Integrated World Modeling Theory (IWMT) of Consciousness (2020) · cites this paper as Yufik and Friston, 2016

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 118

  1. Baillieux H. De Smet H. J. Paquier P. E. De Deyn P. P. Mainën P. (2008). Cerebellar neurocognition: insights into the bottom of the brain. Clin. Neurol. Neurosurg. 110, 763– 773. 10.1016/j.clineuro.2008.05.013 18602745 · DOI · Paper page
  2. Bak P. (1996). How Nature Works: The Science of Self-Organized Criticality. New York, NY: Copernicus Press.
  3. Battaglia P. W. Hamrick J. B. Tenenbaum J. B. (2013). Simulation as an engine of physical scene understanding. Proc. Natl. Acad. Sci. U S A. 110, 18327– 18332. 10.1073/pnas.1306572110 24145417 PMC3831455 · DOI · Paper page
  4. Bressler S. L. Kelso J. A. S. (2001). Cortical coordination dynamics and cognition. Trends Cogn. Sci. 5, 26– 36. 10.1016/s1364-6613(00)01564-3 11164733 · DOI
  5. Bressler S. L. Kelso J. A. (2016). Coordinations dynamics in cognitive neuroscience. Front. Neurosci. 10: 397 10.3389/fnins.2016.00397 27695395 PMC5023665 · DOI
  6. Bruhn A. Weickert J. Shnörr C. (2005). Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int. J. Comput. Vis. 61, 211– 231. 10.1023/B:VISI.0000045324.43199.43 · DOI · Paper page
  7. Cabessa J. Siegelmann H. T. (2011). “ Evolving recurrent neural networks are super-Turing,” in Int. Joint Conf. Neural Networks (San Jose, CA), 3200– 3206.
  8. Carnap R. (1962). Logical Foundations of Probability. Chicago, IL: The University of Chicago Press.
  9. Casti J. L. (1979). Connectivity, Complexity, and Catastrophe in Large-Scale Systems. New York, NY: John Wiley.
  10. Chaitin G. (2006). The limits of reason. Sci. Am. 294, 74– 81. 10.1038/scientificamerican0306-74 16502614 · DOI
  11. Chung W.-S. Welsh C. A. Barres B. A. Stevens B. (2015). Do glia drive synaptic and cognitive impairment in disease? Nat. Neurosci. 18, 1539– 1545. 10.1038/nn.4142 26505565 PMC4739631 · DOI · Paper page
  12. Conant R. C. Ashby W. R. (1970). Every good regulator of a system must be a model of that system. Int. J. Systems Sci. 1, 89– 97. 10.1080/00207727008920220 · DOI · Paper page
  13. Craik K. (1943). The Nature of Explanation. Cambridge, MA: Cambridge University Press.
  14. Cushing J. T. (1994). Quantum Mechanics. Historical Contingency and the Copenhagen Hegemony. Chicago, IL: University of Chicago Press.
  15. Damasio A. Damasio H. (2016). Exploring the concept of homeostasis and considering its implications for economics. J. Econ. Behav. Organ. 126B, 125– 129. 10.1016/j.jebo.2015.12.003 · DOI · Paper page
  16. Davidson D. (1970). Essays on Actions and Events. New York, NY: Clarendon Press.
  17. Davidson D. (1993). “ Thinking causes,” in Mental Causation, eds Heil J. Mele A. (Oxford, NY: Clarendon Press), 3– 17.
  18. Destexhe A. Sejnowski T. J. (2009). The Wilson-Cowan model, 36 years later. Biol. Cybern. 101, 1– 2. 10.1007/s00422-009-0328-3 19662434 PMC2866289 · DOI
  19. Dieks D. de Regt H. W. (1998). Reduction and understanding. Found. Sci. 3, 45– 59. 10.1023/A:1009630119534 · DOI
  20. Doran C. Lasenby A. (2003). Geometric Algebra for Physicists. Cambridge, MA: Cambridge University Press.
  21. Dorny C. N. (1975). A Vector Space Approach to Models and Optimization. New York, NY: John Wiley & Sons.
  22. Edelman G. M. (1993). Neural Darwinism: selection and reentrant signaling in higher brain function. Neuron 10, 115– 125. 10.1016/0896-6273(93)90304-a 8094962 · DOI · Paper page
  23. Elhilali M. Fritz J. B. Chi T.-S. Shamma S. A. (2007). Auditory cortical receptive fields: stable entities with plastic abilities. J. Neurosci. 27, 10372– 10382. 10.1523/jneurosci.1462-07.2007 17898209 PMC6673154 · DOI
  24. England J. L. (2013). Statistical physics of self-replication. J. Chem. Phys. 139: 121923. 10.1063/1.4818538 24089735 · DOI · Paper page
  25. Eysenck M. W. Keane M. T. (1995). Cognitive Psychology. A Student’s Handbook, 3rd Edn. (Hove, UK: Psychology Press).
  26. Fitelson B. Hitchcock C. (2011). “ Probabilistic measures of causal strength,” in Causality in the Sciences, eds Illari P. M. Russo F. Williamson J. (Oxford: Oxford University Press), 600– 627.
  27. Feynman R. P. Leighton R. B. Sands M. (1964). The Feynman Lectures on Physics Volume II., Reading, MA: Addison-Wesley.
  28. Freeman W. J. Holmes M. D. (2005). Metastability, instability, and state transition in neocortex. Neural Netw. 18, 497– 504. 10.1016/j.neunet.2005.06.014 16095879 · DOI
  29. Freeman W. J. Kozma R. Vitiello G. (2012). Adaptation of the generalized Carnot cycle to describe thermodynamics of cerebral cortex. Proc. IEEE WCAI, IJCNN, Available Online at: http://escholarship.org/uc/item/4087h3bs#page-1
  30. Friston K. (2013). Life as we know it. J. Royal Society, INTERFACE, Available Online at: http://rsif.royalsocietypublishing.org/ PMC3730701 23825119
  31. Friston K. Kilner J. Harrison L. (2006). A free energy principle for the brain. J. Physiol. Paris. 100, 70– 87. 10.1016/j.jphysparis.2006.10.001 17097864 · DOI · Paper page
  32. Friston K. Sengupta B. Auletta G. (2014). Cognitive dynamics: from attractors to active inference. Proc. IEEE. 102, 427– 445. 10.1109/jproc.2014.2306251 · DOI
  33. Fritz J. B. Elhilali M. David S. V. Shamma S. A. (2007). Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1? Hear. Res. 229, 186– 203. 10.1016/j.heares.2007.01.009 17329048 PMC2077083 · DOI
  34. Fritz J. Shamma S. Elhilali M. Klein D. (2003). Rapid task-dependent plasticity of spectrotemporal receptive fields in primary auditory cortex. Nat. Neurosci. 6, 1216– 1223. 10.1038/nn1141 14583754 · DOI
  35. Fuchs A. Kelso J. A. S. Haken H. (1992). Phase transitions in the human brain: Spatial mode dynamics. Int. J. Bifurcation Chaos. 2, 917– 939. 10.1142/s0218127492000537 · DOI
  36. Georgopoulos A. P. Kettner R. E. Schwartz A. B. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci. 8, 2928– 2937. 3411362 PMC6569382
  37. Georgopoulos A. P. Taira M. Lukashin A. (1993). Cognitive neurophysiology of the motor cortex. Science. 260, 47– 52. 10.1126/science.8465199 8465199 · DOI
  38. Gepshtein S. Lesmes L. A. Albright T. D. (2013). Sensory adaptation as optimal resource allocation. Proc. Natl. Acad. Sci. U S A. 110, 4368– 4373. 10.1073/pnas.1204109110 23431202 PMC3600488 · DOI
  39. Glansdorff P. Prigogine I. (1971). Thermodynamic Theory of Structure, Stability and Fluctuations. New York, NY: John Wiley & Sons, Inc.
  40. Haken H. (1983). Synergetics, An Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry and Biology. New York, NY: Springer-Verlag.
  41. Haken H. (1993). Advanced Synergetics: Instability Hierarchies of Self-Organizing Systems and Devices. New York, NY: Springer-Verlag.
  42. Halpern J. Y. Hitchcock C. (2015). Graded causality and defaults. Br. J. Philos. Sci. 66, 413– 457. 10.1093/bjps/jaxt050 · DOI
  43. Hebb D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: John Wiley & Sons.
  44. Hempel C. G. (1962). “ Deductive-Nomological vs. Statistical Explanation,” in Minnesota Studies in the Philosophy of Science, (Vol. III), eds Feigel H. Maxwell G. (Minneapolis, MN: University of Minnesota Press), 98– 131.
  45. Hempel C. G. (1965). Aspects of Scientific Explanation and Other Essays in the Philosophy of Science. New York, NY: Free Press.
  46. Horn B. K. P. Schunck B. G. (1981). Determining optical flow. Artif. Intell. 17, 185– 203. 10.1016/0004-3702(81)90024-2 · DOI · Paper page
  47. Humphrey N. (2006). Seeing Red: A Study in Consciousness. Cambridge, MA: The Belknap Press of Harvard University Press.
  48. Ito M. (1993). Movement and thought: Identical control mechanisms by the cerebellum. Trends Neurosci. 16, 448– 450. 10.1016/0166-2236(93)90073-u 7507615 · DOI
  49. Jazayeri M. Movshon J. A. (2006). Optimal representation of sensory information by neural populations. Nat. Neurosci. 9, 690– 696. 10.1038/nn1691 16617339 · DOI
  50. Jensen H. J. (1998). Self-Organizing Criticality. Emergent Complex Behavior in Physical and Biological Systems. Cambridge, MA: Cambridge University Press.
  51. Johnson-Laird P. N. (1983). Mental Models. Towards a Cognitive Science of Language, Inference and Consciousness. Cambridge, MA: Harvard University Press.
  52. Johnson-Laird P. N. (1989). “ Mental models,” in Foundations of Cognitive Science, ed. Posner M. I. (Cambridge, MA: MIT Press), 469– 499.
  53. Kasparov G. (2007). How Life Imitates Chess. New York, NY: Bloomsbury.
  54. Kauffman S. (2000). Investigations. New York, NY: Oxford University Press.
  55. Kelso J. A. S. (1995). The Dynamic Patterns. The Self-Organization of Brain and Behavior. Cambridge, MA: The MIT Press.
  56. Koestler A. (1964). The Act of Creation. London: Penguin Group.
  57. Köhler W. (1948). The Mentality of Apes. London, UK: Routledge and Kegan.
  58. Kohn A. Movshon A. (2004). Adaptation changes the direction tuning of macaque MT neurons. Nat. Neurosci. 7, 764– 772. 10.1038/nn1267 15195097 · DOI
  59. Kozma R. Puljic M. Balister P. Bollobás B. Freeman W. (2005). Phase transitions in the neuropercolation model of neural populations with mixed local and non-local interactions. Biol. Cybern. 92, 367– 379. 10.1007/s00422-005-0565-z 15920663 · DOI
  60. Lear J. (1988). Aristotle: the Desire to Understand. New York, NY: Cambridge University Press.
  61. Lehar S. (2003a). Gestalt isomorphism and the primacy of subjective conscious experience: A Gestalt bubble model. Behav. Brain Sci. 26, 375– 408; discussion 408– 443. 10.1017/s0140525x03000098 15067950 · DOI
  62. Lin L. Osan R. Shoham S. Jin W. Zuo W. Tsien J. Z. (2005). Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. Proc. Natl. Acad. Sci. U S A. 102, 6125– 6130. 10.1073/pnas.0408233102 15833817 PMC1087910 · DOI
  63. Lin L. Osan R. Tsien J. Z. (2006). Organizing principle of real-time memory encoding: neural clique assemblies and universal neural codes. Trends Neurosci. 29, 48– 57. 10.1016/j.tins.2005.11.004 16325278 · DOI
  64. Lipowsky R. (1984). Surface-induced order and disorder: critical phenomena at first-order phase transitions. J. Appl. Phys. 55, 2485– 2490. 10.1063/1.333703 · DOI
  65. Lucas B. Kanade T. (1981). “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (Vancouver, BC), 674– 679
  66. Marder M. (2013). Plant intelligence and attention. Plant Signal. Behav. 8: e23902. 10.4161/psb.23902 23425923 PMC3906434 · DOI
  67. Margenau H. (1959). The Nature of Physical Reality. New York, NY: McGraw-Hill Education.
  68. Moran J. Desimone R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science. 229, 782– 784. 10.1126/science.4023713 4023713 · DOI · Paper page
  69. Morowitz H. J. (1979). Energy Flow in Biology: Biological Organization As a Problem in Thermal Physics. Woodbridge, CO: Ox Bow Press.
  70. Morowitz H. J. (1978). Foundations of Bioenergetics. Woodbridge, CO: Ox Bow Press.
  71. Mumford D. Shah J. (1985). “ Boundary detection by minimizing functionals,” in Proc. IEEE CS Conference Computer Vision and Pattern Recognition (CVPR), (San Francisco, CA), 22– 26.
  72. Mumford D. Shah J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42, 577– 685. 10.1002/cpa.3160420503 · DOI
  73. Noe A. (2004). Action in Perception. Cambridge, MA: The MIT Press.
  74. Palatnik S. Khodarkovsky M. (2014). The Chess GPS: Improvement of Your Position. Washington DC: Wildside Press.
  75. Penrose R. (1997). The Large, the Small and the Human Mind. Boston, MA: Cambridge University Press.
  76. Penrose R. (1989). The Emperor’s New Mind. Oxford: Oxford University Press.
  77. Penrose R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness. New York, NY: Oxford University Press.
  78. Piaget J. (1976). The Grasp of Consciousness: Action and Concept in the Young Child. Cambridge, MA: Harvard Univ. Press.
  79. Piaget J. (1977). The Development of Thought: Equilibration of Cognitive Structures. New York, NY: The Viking Press.
  80. Piaget J. (1978). Success and Understanding. Cambridge, MA: Harvard Univ. Press.
  81. Prokopenko M. Polani D. Ay N. (2014). “ On the cross-disciplinary nature of guided self-organization,” in Guided Self-Organization: Inception, ed. Prokopenko M. (Berlin: Springer), 3– 18.
  82. Purushotaman G. Bradley D. C. (2005). Neural population code for fine perceptual decisions in area MT. Nat. Neurosci. 8, 99– 106. 10.1038/nn1373 15608633 · DOI · Paper page
  83. Razi A. Friston K. J. (2016). The connected brain: causality, models, and intrinsic dynamics. IEEE Signal Proc. Mag. 33, 14– 35. 10.1109/msp.2015.2482121 · DOI
  84. Rosenbloom M. H. Schmahmann J. D. Price B. H. (2012). The functional neuroanatomy of decision-making. J. Neuropsychiatry Clin. Neurosci. 24, 266– 277. 10.1176/appi.neuropsych.11060139 23037641 · DOI
  85. Salman M. S. (2002). The cerebellum: new insights into the role of the cerebellum in timing motor and cognitive tasks. J. Child Neurol. 17, 1– 9. 10.1177/088307380201700101 11913561 · DOI
  86. Salmon W. C. (1970). Statistical Explanation and Statistical Relevance. Pittsburgh, PA: University of Pittsburgh Press.
  87. Salmon W. C. (1984). Scientific Explanation and the Causal Structure of the World. Chicago, IL: Princeton University Press.
  88. Salmon W. C. (1989). Four Decades of Scientific Explanation. Pittsburgh, PA: University of Pittsburgh Press.
  89. Sanford A. J. (1987). The Mind of Man: Models of Human Understanding. New Haven, CT: Yale University Press.
  90. Sarti A. Citti G. (2015). The constitution of visual perceptual units in the functional architecture of V1. J. Comp. Neurosci. 38, 285– 300. 10.1007/s10827-014-0540-6 25529294 · DOI
  91. Schooler J. W. Ohlsson S. Brook K. (1993). Thoughts beyond words: when language overshadows insight. J. Exp. Psychol. Gen. 2, 166– 1843. 10.1037/0096-3445.122.2.166 · DOI · Paper page
  92. Schrodinger W. (2006). What is Life? New York, NY: Cambridge University Press.
  93. Sejnowski T. Poizner H. Lynch G. Gepshtein S. Greenspan R. J. (2014). Prospective optimization. Proc. IEEE Inst. Electr. Electron. Eng. 102, 799– 811. 10.1109/JPROC.2014.2314297 25328167 PMC4201124 · DOI
  94. Sengupta B. Stemmler M. B. Friston K. J. (2013). Information and efficiency in the nervous system-a synthesis. PLoS Comput. Biol. 9: e1003157. 10.1371/journal.pcbi.1003157 23935475 PMC3723496 · DOI · Paper page
  95. Shah J. (1992). Properties of energy-minimizing segmentations. SIAM J. Control Optim. 30, 99– 111. 10.1137/0330007 · DOI
  96. Shannon C. E. (1941). Mathematical theory of the differential analyzer. J. Math. Phys. 20, 337– 354. 10.1002/sapm1941201337 · DOI
  97. Shastri L. (2001). “ Biological grounding of recruitment learning and vicinal algorithms in long-term potentiation,” in Emergent Neural Computational Architectures Based on Neuroscience–Towards Neuroscience-Inspired Computing, eds Austin J. Wermter S. Wilshaw D. (Berlin: Lecture Notes in Computer Science, Springer-Verlag), 348– 367.
  98. Siebeck U. E. Litherland L. Wallis G. M. (2009). Shape learning and discrimination in reef fish. J. Exp. Biol. 212, 2113– 2119. 10.1242/jeb.028936 19525438 · DOI
  99. Siegelmann H. T. (1999). Neural Networks and Analog Computation: Beyond the Turing Limit. Cambridge, MA: Birkhauser Boston Inc.
  100. Sierpinska A. (1994). Understanding in Mathematics. London: The Falmer Press.
  101. Singer W. (2009). The brain, a complex self-organizing system. Eur. Rev. 17, 321– 329. 10.1017/s1062798709000751 · DOI
  102. Sloman S. (2005). Causal Models. How People Think About the World and the Alternatives. New York, NY: Oxford University Press.
  103. Sperry R. W. (1969). A modified concept of consciousness. Psychol. Rev. 76, 532– 536. 10.1037/h0028156 5366411 · DOI
  104. Syms R. R. A. Yeatman E. M. Bright V. M. Whitesides G. M. (2003). Surface-tension powered self-assembly of microstructures–state-of-the-art. J. Microelectromech. Syst. 12, 387– 417. 10.1109/jmems.2003.811724 · DOI
  105. Thompson E. Varela F. (2001). Radical embodiment: Neural dynamics and consciousness. Trends Cogn. Sci. 5, 418– 425. 10.1016/s1364-6613(00)01750-2 11707380 · DOI · Paper page
  106. Trevawas A. (2002). Mindless mastery. Nature. 415: 841. 10.1038/415841a 11859344 · DOI
  107. Tsien J. Z. (2007). The memory code. Sci. Am. 297, 52– 57. 10.1038/scientificamerican0707-52 17695842 · DOI
  108. Tschacher W. Haken H. (2007). Intentionality in non-equilibrium systems? The functional aspects of self-organised pattern formation. New Ideas Psychol. 25, 1– 15. 10.1016/j.newideapsych.2006.09.002 · DOI · Paper page
  109. van Fraasen B. (1980). The Scientific Image. Oxford: Clarendon Press.
  110. von Wright G. H. (1971). Explanation and Understanding. Ithaca, NY: Cornell University Press.
  111. Wilson H. R. Cowan J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1– 24. 10.1016/s0006-3495(72)86068-5 4332108 PMC1484078 · DOI
  112. Wilson H. R. Cowan J. D. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik. 13, 55– 80. 10.1007/bf00288786 4767470 · DOI
  113. Yantis S. (1992). Multi-element visual tracking: attention and perceptual organization. Cogn. Psychol. 24, 295– 340. 10.1016/0010-0285(92)90010-y 1516359 · DOI
  114. Yufik Y. M. (2002). ”How the mind works: An exercise in pragmatism”, in Proceedings of the 2002 International Joint Conference Neural Networks, 2002. IJCNN 02 (Honolulu: HI), 2265– 2269.
  115. Yufik Y. M. (2013). Understanding, consciousness and thermodynamics of cognition. Chaos Solitons Fractals 55, 44– 59. 10.1016/j.chaos.2013.04.010 · DOI
  116. Yufik Y. M. Malhotra R. (1999). Information blending in virtual associative networks: a new paradigm for sensor integration. Int. J. Artif. Intell. Tools 8, 275– 290. 10.1142/s0218213099000191 · DOI
  117. Yufik Y. M. Sheridan T. B. (1997). Virtual networks: new framework for operator modelling in complex systems. Annu. Rev. Control. 20, 179– 195. 10.1016/s1367-5788(97)00016-3 · DOI
  118. Yufik Y. M. Sheridan T. B. (2002). Swiss army knife and Ockham’s razor: modelling operator’s comprehension in complex dynamic tasks. IEEE Trans. Syst. Man Cyber. A Syst. Hum. 32, 185– 199. 10.1109/tsmca.2002.1021107 · DOI

Cited by

Sorted by citations

Authors
Yufik, Y. M., & Friston, K.
Affiliations
Wellcome Centre for Human Neuroimaging (GB)
Corresponding author
Yufik, Yan M.
Venue
Frontiers in Systems Neuroscience
Published
2016-12-09
Volume
10
Pages
98-98
ISSN
1662-5137 (ISSN-L)
OpenAlex
W2559897504
Cited by
53
References
11
Citations by year
2026: 5 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 11 · 2020: 7 · 2019: 7 · 2018: 1 · 2017: 5
FWCI
0.46
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Systems Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Wellcome Trust (088130, 088130/Z/09/Z)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
Keywords
understanding, Markov blanket, cell assemblies, origin of life, prediction, mental models, object perception, association cortex

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review