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In the 50- to 200-ms period, ERPs were more positive for the natural stimuli in the categorization task, but more positive for the artifactual stimuli in the gender decision task.
In one task (semantic categorization), the subjects were asked to categorize the stimuli as corresponding either to the natural or the artifactual class, and in the other (gender decision), the subjects had to decide if the names of the stimuli corresponded to either the masculine or the feminine gender.
Object identification was assessed through a grammatical gender decision task, which required the participants to make a syntactic judgment on the object's name (for trial outlook, see Figure 2).
We observed no significant RT effects in the gender decision task.
As SCRs, RT data from the gender decision task were z-transformed.
Similar to the present spelling task, the gender decision task also required attention to the auditory stimulus.
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Subjects were shown diagnostic and anti-diagnostic face images for both expression and gender decisions (created using Gosselin and Schyns' Bubbles technique), and asked to perform both tasks on all stimuli.
Initially, images showing objects with natural gender (e.g., a rooster or a hen) were removed, as natural gender could selectively facilitate grammatical gender decisions for these items.
This is in line with the subject's reaction times for which we observed an inverted U-shaped function where the 50% ambiguous stimuli led to slower voice gender decisions (Fig. 1 c ).
Women are seen as "naturally" closer to children, both physically and as a result of their gender: Decisions concerning how newborns are cared for are mostly taken by women since they are closer to the babies than men (Male, 37).
There is no comparable semantic representation associated with grammatical gender of nouns referring to inanimate objects; hence, it may be speculated that the automatic activation of semantic representations facilitated grammatical number decisions and caused higher efficiency compared to grammatical gender decisions.
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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