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Planned t tests showed that words preceded by morphological primes during the 250-ms SOA were recognized 110 ms faster than those preceded by non-related primes [t 31) = 3.25, p < 0.01] and were recognized 125 ms faster than those preceded by orthographic primes [t 31) = 3,9, p < 0,01].
The words preceded by morphological primes were recognized 86 ms faster than those preceded by non-related primes, and were recognized 109 ms faster than those preceded by orthographic primes.
The words preceded by morphological primes were recognized 119 ms faster than those preceded by non-related primes.
A preliminary analysis that included gender as a factor found that males responded, on average, 45 ms faster than females (p < 0.001); however, gender did not interact with any of the other factors (all p values >0.200); hence we collapsed across this variable for all additional analyses.
HV mismatches (1580 ms; 22% errors) were 2134 ms faster than VH mismatches (3714 ms; 25% errors).
Unlike in Experiment 1a, semantic priming was now also obtained: responses in the semantic condition were on average 32 ms faster than in the unrelated condition (p<0.05).
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Pimenta et al. [21] examined 200 Brazilian professional soccer players and found that 577RR carriers ran 10, 20, and 30 m faster than 577XX carriers.
Guriel et al. [ 19] showed M-FAST and TSI detected 90% of malingering to the PTSD and Guy et al. [ 15] suggested that malingering people scored higher in the M-FAST than clinical participants (with Schizophrenia, major depression, bipolar and acute traumatic stress disorders).
This implies that since M-M is faster than G-M, MI-GRAAL's NCF is less computationally intensive than GHOST's NCF.
The results show that one aspect of the acquired skill was sequence-specific: the left-hand MTs were 237 ms (SE: ±42 ms) faster for trained than for untrained sequences.
In both the BLP and the DLP, participants' responses to words were 80 ms faster, on average, than in the ELP (the RTs of which, in general, are longer than in published studies; see below).
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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