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Expected Float Entropy Minimisation (view source)
Revision as of 19:56, 10 December 2021
, 19:56, 10 December 2021no edit summary
In 2021 the theory was shown to easily extend to include the concept and mathematical formulation of Model Unity<ref name=Mason2021>Mason, J. W. (2021), Model Unity and the Unity of Consciousness: Developments in Expected Float Entropy Minimisation. Entropy, 23, 11. doi:10.3390/e23111444</ref>, which is closely related to the unity (and disunity) of consciousness and provides a different notion of integration to that of IIT. The intention behind Model Unity is to answer questions such as, why different individuals do not have shared perception, why individual visual perception is unified and why visual perception is phenomenally very different to auditory perception, for example.
Due to properties such as learning, the brain is very biased toward certain system states and therefore determines typical system states and, in theory, a probability distribution over the set of all system states. This opens up the possibility of applying [https://en.wikipedia.org/wiki/Information_theory information theory] type approaches and EFE is a form of expected conditional entropy where the condition involves relationship parameters. EFE is a measure of the expected amount of information required to specify the state of a system (such as an artificial or [https://en.wikipedia.org/wiki/Neural_circuit biological neural network]) beyond what is already known about the system from the relationship parameters. For certain non-uniformly random systems, particular choices of the relationship parameters are isolated from other choices in the sense that they give much lower Expected Float Entropy values and, therefore, the system defines relationships. According to the theory, in the context of these relationships a brain state acquires meaning in the form of the relational content of the corresponding experience. The principle article (Quasi-Conscious Multivariate Systems<ref name=Mason2016>Mason, J. W. (2016), Quasi-conscious multivariate systems. Complexity, 21: 125-147. doi:10.1002/cplx.21720</ref>) on this mathematical theory was published in 2015 and was followed by the article (From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation<ref name=Mason2019>Mason, J. W. (2019), From Learning to Consciousness: An Example Using Expected Float Entropy Minimisation. Entropy, 21, 60. doi:10.3390/e21010060</ref>) in 2019. The extension to Model Unity was introduced in the article (Model Unity and the Unity of Consciousness: Developments in Expected Float Entropy Minimisation<ref name="Mason2021"/>) published in 2021. EFE first appeared in a publication in 2012<ref name=Mason2012>Mason, J. W. (2013), Consciousness and the structuring property of typical data. Complexity, 18: 28-37. doi:10.1002/cplx.21431</ref>.
The nomenclature “Float Entropy” comes from the notion of floating a choice of relationship parameters over a state of a system, similar to the idiom “to float an idea”. Optimisation methods are used in order to obtain the relationship parameters that minimise Expected Float Entropy. A process that performs this minimisation is itself a type of learning method.
==Overview==
Relationships are ubiquitous among mathematical structures. In particular, weighted relations (also called weighted graphs and [https://en.wikipedia.org/wiki/Weighted_network weighted networks]) are very general mathematical objects and, in the finite case, are often handled as adjacency matrices. They are a generalisation of graphs and include all [https://en.wikipedia.org/wiki/Function_(mathematics) functions] since functions are a rather constrained type of [https://en.wikipedia.org/wiki/Graph_(discrete_mathematics) graph]. It is also the case that [[consciousness]] is awash with relationships; for example, red has a stronger relationship to orange than to green, relationships between points in our field of view give rise to geometry, some smells are similar whilst others are very different, and there’s an enormity of other relationships involving many senses such as between the sound of someone’s name, their visual appearance and the timbre of their voice. Expected Float Entropy includes weighted relations as parameters and, for certain non-uniformly random systems, certain choices of weighted relations are isolated from other choices in the sense that they give much lower Expected Float Entropy values. Therefore, systems such as the brain define relationships and, according to the theory, in the context of these relationships a brain state acquires meaning in the form of the relational content of the corresponding experience.
The theory involves a hierarchy of relational models and at the lowest level the primary models involve pairs of weighted relations.
Expected Float Entropy minimisation is very general in scope. For example, the theory has been successfully applied in the context to image processing<ref name="Mason2016" /> but also applies to waveform recovery from audio data<ref name="Mason2012" />.
