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The new novel, exploring new representations of cause and effect, fate and choice, shifts interest from the Self, the character, to the story, the exemplary life.
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We switched to the method of measuring inferences – a reliable and long-standing indirect measure of mental representations (e.g., Johnson-Laird, 2006) – because our account of differences in the mental representations of causes and counterfactuals commits us to the prediction that there should accordingly be differences in inferences from them.
This work offers an Embodied Construction Grammar (Feldman et al. 2010) representation of caused motion, thereby also providing (a sample of) the computational infrastructure for implementing the information that FrameNet has characterized as Caused_motion1 (Ruppenhofer et al. 2010).
Our findings also provide insight into the dimensions of Leventhal's illness representations of identity, cause, timeline, control/cure, and consequences [ 11, 12].
We need to re-evaluate how we can still deliver nugget-size communication goodies, but giving a stronger representation of the cause.
As Kant (1787), Husserl (1913), and generations of phenomenologists have shown, the phenomenal structure of experience is richly intentional and involves not only sensory ideas and qualities but complex representations of time, space, cause, body, self, world and the organized structure of lived reality in all its conceptual and nonconceptual forms.
Electronic systems and circuits are often represented by block diagrams because they are a shorthand pictorial representation of the cause-and-effect relationship between the input and output in a real system.
We start with the premise that subjects continuously update a probabilistic representation of the causes of their sensory inputs to optimise their behaviour.
It is also known that such mechanism can work at very fine spatial scales, as demonstrated by the cortical representation of 'angioscotomas' caused by cast shadows of retinal blood vessels [102], [103].
There are other factors that may result in an over-representation of viral causes of pneumonia.
By including incomplete VR data, modelers run the risk of a biased representation of the cause-specific mortality fractions for the entire population.
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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