Your English writing platform
Discover LudwigExact(1)
The difference when comparing an interactive to a passive context of assessment could be the loss of discrimination (reduced MOS range) and potentially an asymmetrical assessment depending on the modality that interactants dedicate most of their shared attentional resources to.
Similar(59)
There is a growing body of evidence for a shared attentional capacity between the modalities of vision and hearing.
In support of this position we have previously shown that perceptual VSE judgements and eye movements share common attentional resources [54], and that pointing movements and perceptual extrapolation judgements are susceptible to a common geometric illusion [27], [75].
Under the assumption that attentional resources are shared between vision and hearing, the visual interface design may also impact the ability to process these auditory stimuli.
The multiple limited resource model dictates that each hemisphere has a finite supply of attentional resources which cannot be shared.
These results have been interpreted under the perspective that we possess a limited pool of attentional resources that must be shared in case other cognitive processes are required to overlap at some point in time with the time estimation process (e.g. [ 21]).
An interpretation of our results so far is that spatial and feature-based attentions operate under the restriction of separate pools of attentional resources: that WMC is critically associated with feature-based attention via the shared recruitment of an attentional template.
This type of allocation of attentional resources is commonly called "divided attention".
Scientists are starting to learn more about how the brain allocates its attentional resources -- and how our attention gets away from us.
The presence of this overlap also suggests that spatial attention and working memory share common cognitive features related to the dynamic shifting of attentional resources.
Participants may be able to detect when the mask and sample share the same spatial frequency (MSR = 1), and thereby devote more attentional resources to the processing of the sample stimulus.
Write better and faster with AI suggestions while staying true to your unique style.
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