ArticleCognitive neurodynamics2017
The feeling of understanding: an exploration with neural models.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Aperiodic stochastic resonance in neural information processing with Gaussian colored noise.Cognitive neurodynamics · 2021Article
- Probabilistically segregated neural circuits and subcritical linguistics.Cognitive neurodynamics · 2020Article
- Effects of network topologies on stochastic resonance in feedforward neural network.Cognitive neurodynamics · 2020Article
- Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns.Cognitive neurodynamics · 2020Article
- Points and lines inside human brains.Cognitive neurodynamics · 2019Review
- Random pulse induced synchronization and resonance in uncoupled non-identical neuron models.Cognitive neurodynamics · 2019Article
- Neurodynamic analysis of Merkel cell-neurite complex transduction mechanism during tactile sensing.Cognitive neurodynamics · 2019Article
- Simulation of retinal ganglion cell response using fast independent component analysis.Cognitive neurodynamics · 2018Article
- Analyzing text recognition from tactually evoked EEG.Cognitive neurodynamics · 2017Article
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2 authors.
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Abstract
There exists a dynamic interaction between the world of information and the world of concepts, which is seen as a quintessential byproduct of the cultural evolution of individuals as well as of human communities. The feeling of understanding (FU) is that subjective experience that encompasses all the emotional and intellectual processes we undergo in the process of gathering evidence to achieve an understanding of an event. This experience is part of every person that has dedicated substantial efforts in scientific areas under constant research progress. The FU may have an initial growth followed by a quasi-stable regime and a possible decay when accumulated data exceeds the capacity of an individual to integrate them into an appropriate conceptual scheme. We propose a neural representation of FU based on the postulate that all cognitive activities are mapped onto dynamic neural vectors. Two models are presented that incorporate the mutual interactions among data and concepts. The first one shows how in the short time scale, FU can rise, reach a temporary steady state and subsequently decline. The second model, operating over longer scales of time, shows how a reorganization and compactification of data into global categories initiated by conceptual syntheses can yield random cycles of growth, decline and recovery of FU.
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