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Results: Significant differences across task and across sections were determined.
Neuronal activity across task states converges onto a low-dimensional manifold.
In all three experiments exclusion led to larger set sizes, across task type and experimental design.
To measure activation related to proactive inhibition, it was necessary to compare variations in sustained activation across task contexts.
Peak prosaccade velocity collapsed across task condition for pre and post exercise categories in placebo, caffeine and NDRI treatments.
However, if this was the case, we might expect to find this effect consistently across task pairings.
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This allows for more valid across-task generalizations.
To further validate these observations, we then interrogated these regions using an across-task adaptation technique, and found adaptation across reading and spelling in the left dorsal IFG BAA 44/9).
The across-task patterns of performance correlated significantly between the three mouse strains (Spearman, 0.93≤ rs ≤0.96, p<0.01) indicating that tasks that were relatively difficult for one of the mouse strains were also relatively difficult for the other two mouse strains.
Importantly, the GP classifier directly fits subjects' stimulus-to-response mappings without extracting underlying subjective distributions and thus has no way to provide across-task predictions.
In contrast, in the OOO task, even our across-task predictions are as good as (even marginally better, p = 0.092, than) the within-task predictions of the GP classifier algorithm.
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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