Editing Cognitive science (section)

==Binding problem in cognitive science==
{{main|Binding problem}}

One of the core aims of cognitive science is to achieve an integrated theory of cognition. This requires integrative mechanisms explaining how the information processing that occurs simultaneously in spatially segregated (sub-)cortical areas in the brain is coordinated and bound together to give rise to coherent perceptual and symbolic representations. One approach is to solve this "[[Binding problem]]"<ref>Hardcastle, V.G. (1998). "The binding problem". In W. Bechtel & G. Graham (eds.), A Companion to Cognitive Science (pp. 555-565). Blackwell Publisher, Malden/MA, Oxford/UK.</ref><ref>Hummel, J. (1999). "Binding problem". In: R.A. Wilson & F.C. Keil, The MIT Encyclopedia of the Cognitive Sciences (pp. 85-86). Cambridge, MA, London: The MIT Press.</ref><ref>Malsburg, C. von der (1999). "The what and why of binding: The modeler's perspective". Neuron. 24: 95-104.</ref> (that is, the problem of dynamically representing conjunctions of informational elements, from the most basic perceptual representations ("feature binding") to the most complex cognitive representations, like symbol structures ("variable binding")), by means of integrative synchronization mechanisms. In other words, one of the coordinating mechanisms appears to be the temporal (phase) synchronization of neural activity based on dynamical self-organizing processes in neural networks, described by the [[Binding-by-synchrony]] (BBS) Hypothesis from neurophysiology.<ref>Gray, C. M. & Singer, W. (1989). "Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex". Proceedings of the National Academy of Sciences of the United States of America. 86: 1698-1702.</ref><ref>Singer, W. (1999b). "Neuronal synchrony: A versatile code for the definition of relations". Neuron. 24: 49-65.</ref><ref>Singer, W. & A. Lazar. (2016). "Does the cerebral cortex exploit high-dimensional, non-linear dynamics for information processing?" Frontiers in Computational Neuroscience.10: 99.</ref><ref>Singer, W. (2018). "Neuronal oscillations: unavoidable and useful?" European Journal of Neuroscience. 48: 2389-2399.</ref> Connectionist cognitive neuroarchitectures have been developed that use integrative synchronization mechanisms to solve this binding problem in perceptual cognition and in language cognition.<ref>Maurer, H. (2021). "Cognitive science: Integrative synchronization mechanisms in cognitive neuroarchitectures of the modern connectionism". CRC Press, Boca Raton/FL, ISBN 978-1-351-04352-6. https://doi.org/10.1201/9781351043526 {{Webarchive|url=https://web.archive.org/web/20230205120509/https://www.taylorfrancis.com/books/mono/10.1201/9781351043526/cognitive-science-harald-maurer |date=5 February 2023 }}</ref><ref>Maurer, H. (2016). "Integrative synchronization mechanisms in connectionist cognitive Neuroarchitectures". Computational Cognitive Science. 2: 3. https://doi.org/10.1186/s40469-016-0010-8 {{Webarchive|url=https://web.archive.org/web/20230205120509/https://computationalcognitivescience.springeropen.com/articles/10.1186/s40469-016-0010-8 |date=5 February 2023 }}</ref><ref>Marcus, G.F. (2001). "The algebraic mind. Integrating connectionism and cognitive science". Bradford Book, The MIT Press, Cambridge, ISBN 0-262-13379-2.</ref> In perceptual cognition the problem is to explain how elementary object properties and object relations, like the object color or the object form, can be dynamically bound together or can be integrated to a representation of this perceptual object by means of a synchronization mechanism ("feature binding", "feature linking"). In language cognition the problem is to explain how semantic concepts and syntactic roles can be dynamically bound together or can be integrated to complex cognitive representations like systematic and compositional symbol structures and propositions by means of a synchronization mechanism ("variable binding") (see also the "Symbolism vs. connectionism debate" in [[connectionism]]).

However, despite significant advances in understanding the integrated theory of cognition (specifically the [[Binding problem]]), the debate on this issue of beginning cognition is still in progress. From the different perspectives noted above, this problem can be reduced to the issue of how organisms at the simple reflexes stage of development overcome the threshold of the environmental chaos of sensory stimuli: electromagnetic waves, chemical interactions, and pressure fluctuations.<ref>Treisman A. (1999). "Solutions to the binding problem: progress through controversy and convergence." ''Neuron,'' 1999, 24(1):105-125.</ref> The so-called Primary Data Entry (PDE) thesis poses doubts about the ability of such an organism to overcome this cue threshold on its own.<ref name="Val Danilov 2023 Theoretical Grounds">{{cite journal |title=Theoretical Grounds of Shared Intentionality for Neuroscience in Developing Bioengineering Systems |first=Igor |last=Val Danilov |journal=OBM Neurobiology |url=https://www.lidsen.com/journals/neurobiology/neurobiology-07-01-156 |date=2023-02-17 |volume=7 |issue=1 |page=156 |doi=10.21926/obm.neurobiol.2301156|doi-access=free }}</ref> In terms of mathematical tools, the PDE thesis underlines the insuperable high threshold of the cacophony of environmental stimuli (the stimuli noise) for young organisms at the onset of life.<ref name="Val Danilov 2023 Theoretical Grounds" /> It argues that the temporal (phase) synchronization of neural activity based on dynamical self-organizing processes in neural networks, any dynamical bound together or integration to a representation of the perceptual object by means of a synchronization mechanism can not help organisms in distinguishing relevant cue (informative stimulus) for overcome this noise threshold.<ref name="Val Danilov 2023 Theoretical Grounds" />