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Psycholinguistic research has documented a range of variables that influence visual word recognition performance.
The encoding of semantic frames into SELMs improves the word recognition performance and especially the recognition performance of the target words, the meaning bearing elements of semantic frames.
This is not surprising since as mentioned above the acoustic adaptation alone already resulted in a worse word recognition performance.
For example, some researchers have used overt naming to assess word recognition performance in studies of hemispheric asymmetry using Latinate languages [25], [75].
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Further, using partial correlations functional connectivity analyses on each trial, the authors demonstrated that increased cingulo-opercular activity was predictive of better word-recognition performance on the next trial.
The association between left temporal WMH and spot-the-word and recognition performance was in line with, respectively, work implicating that lobe in the processing of nouns [34], and lesion studies showing that damage to the amygdala impairs face and emotion recognition [e.g., 35].
The results were selective in that left frontal lobe lesions were associated with within-person variability, while left temporal WMH were associated with spot-the-word and face recognition performance.
In the present paper, we propose a novel approach to factor definition and backoff path selection for FLMs, in which the model considers only specific factors from specific words that can improve recognition performance.
For negatively worded items, the biased recognition performance in favour of salient (negative) symptoms could conflict with mechanisms pointing in the opposite direction, such as social desirability.
In Experiment 3a, single presentations of masked words led to chance recognition performance (d' = 0.06, t(17) = 0.95, n.s.; subjective ratings, t(17) = 1.51, n.s).. Therefore, we modified the study protocol based on prior evidence that spaced repetition increases the likelihood that an event will be perceived as previously encountered [4].
The 60 g dose speeded word recognition and improved serial threes performance following an overnight (but not 2 h) fast.
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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