We suggest an enhancement to structural coding through the use of (a) causally bound codes, (b) basic constructs of graph theory and (c) statistics. As is the norm with structural coding, the codes are collected into ...
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We suggest an enhancement to structural coding through the use of (a) causally bound codes, (b) basic constructs of graph theory and (c) statistics. As is the norm with structural coding, the codes are collected into categories. The categories are represented by nodes (graph theory). The causality is illustrated through links (graph theory) between the nodes and the entire set of linked nodes is collected into a single directed acyclic graph. The number of occurrences of the nodes and the links provide the input required to analyze relative frequency of occurrence, as well as opening a scope for further statistical analysis. While our raw data was a corpus of literature from a specific discipline, this enhancement is accessible to any qualitative analysis that recognizes causality in its structural codes. Through our work, we claim: To extend the semantic potential of structural coding, where the structural codes are causally related, and To extend the methodological scope of systematic review. (C) 2022 The Author(s). Published by Elsevier B.V.
Focusing on visual perceptual organization, this article contrasts the free-energy (FE) version of predictive coding (a recent Bayesian approach) to structural coding (a long-standing representational approach). Both ...
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Focusing on visual perceptual organization, this article contrasts the free-energy (FE) version of predictive coding (a recent Bayesian approach) to structural coding (a long-standing representational approach). Both use free-energy minimization as metaphor for processing in the brain, but their formal elaborations of this metaphor are fundamentally different. FE predictive coding formalizes it by minimization of prediction errors, whereas structural coding formalizes it by minimization of the descriptive complexity of predictions. Here, both sides are evaluated. A conclusion regarding competence is that FE predictive coding uses a powerful modeling technique, but that structural coding has more explanatory power. A conclusion regarding performance is that FE predictive coding-though more detailed in its account of neurophysiological data-provides a less compelling cognitive architecture than that of structural coding, which, for instance, supplies formal support for the computationally powerful role it attributes to neuronal synchronization.
Research investigating the effect of lighting and viewpoint changes on unfamiliar and newly learnt faces has revealed that such recognition is highly image dependent and that changes in either of these leads to poor r...
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Research investigating the effect of lighting and viewpoint changes on unfamiliar and newly learnt faces has revealed that such recognition is highly image dependent and that changes in either of these leads to poor recognition accuracy. Three experiments are reported to extend these findings by examining the effect of apparent age on the recognition of newly learnt faces. Experiment 1 investigated the ability to generalize to novel ages of a face after learning a single image. It was found that recognition was best for the learnt image with performance falling the greater the dissimilarity between the study and test images. Experiments 2 and 3 examined whether learning two images aids subsequent recognition of a novel image. The results indicated that interpolation between two studied images (Experiment 2) provided some additional benefit over learning a single view, but that this did not extend to extrapolation (Experiment 3). The results from all studies suggest that recognition was driven primarily by pictorial codes and that the recognition of faces learnt from a limited number of sources operates on stored images of faces as opposed to more abstract, structural, representations.
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