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Subjects were asked to indicate which stimulus matched the cue via button presses as quickly and accurately as possible.
After a 500ms pause, the subject was then presented with two images (left and right), one that matched the cue image with regard to the current task and another that did not (Figure 1).
Moreover, both distracters were unrelated to the cue, that is, neither global shape nor local letter features matched the cue in this condition (see Fig. 2).
In the easy condition, participants had to decide which compound letter matched the cue in "global" shape, irrespective of the letters that appeared as smaller elements inside the compounds.
The task was to count the stimuli that matched the cue and to report this number, i.e., "0", "1" or "2" at the end of each block by pressing the appropriate response buttons within 2000 ms.
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This task is demonstrated graphically in Figures 3 and 4. In the 2-back task (more generally the n-back task; [21] [23]), subjects are given a continuous stream of cues and must respond to a cue only when it matches the cue from two items earlier.
In contrast, the more difficult Navon task required participants to match the cue letter to the "local" elements of one of the compounds and, hence, to disregard the dominant, global shape of the stimuli.
If the location of the target in the streams matches (resp. doesn't match) the cued instruction, then the target is called a valid target (resp. invalid target).
Measures of learning performance were assessed by calculating the percentages of choices that matched the "better" cue (i.e., had the higher probability of an advantageous outcome; Knutson et al., 2011; Samanez-Larkin et al., 2012).
Subjects were trained prior to scanning to produce speech in a tone of voice that matched the presented visual cue.
DOI: http://dx.doi.org/10.7554/eLife.04478.007 Crucially, we next asked whether the spatial selectivity for the reward cue matched the spatial selectivity for punishment cues by examining the linear relationship between spatial-reward and spatial-punishment selectivity indices.
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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