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A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism.
In this work, we focus in an essential kind of task that is performed in many visual domains: the visual interpretation task.
A crucial problem in this interpretation task is the presence of uncertainty in the origins of measurements (data association or correspondence uncertainty) as well as in the values of measurements (noise uncertainty).
The brains of the trainee interpreters had changed, particularly parts of the right caudate, but not in the way you might expect – activity there lessened during the interpretation task.
The inherently subjective nature of the test complicates the test interpretation task [ 7].
Together, these findings should stimulate further research into primary cognitive functions involved in the odor interpretation task.
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Such research indicates that gaze behaviours characterise expertise in medical image interpretation tasks.
This proposed open architecture allows the host to deal with final high-level interpretation tasks.
For example, Novick and Catley (2013) concluded that students performed significantly better with bracket phylogenetic trees on a variety of interpretation tasks, regardless of background in biology.
Eye-tracking is commonly used to examine observers' gaze behaviour during such medical image interpretation tasks, but analysis of eye movement sequences is limited.
We propose a top-down approach for solving visual interpretation tasks, which is based on symbolic pattern matching supported by well-founded ontologies.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com