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In the second experiment (incidental self processing), subjects categorised words according to whether they described physical versus psychological attributes, while unaware that the words had been arranged in blocks according to self descriptiveness.
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The monkeys learned quickly, and could even categorise words they had never seen before.
By categorising words into eight emotions - Joy, Anticipation, Anger, Disgust, Sadness, Surprise, Fear/Anxiety and Trust - the analysis found the Bible scored higher for anger and much lower for trust than the Quran.
The candidates then went through a series of tests, in which they categorised "me" words (myself, mine) and "wizard" words (wand, broomstick, spells, potions) by pressing one key when they appeared on the screen, and "not me" words (they, theirs) and "vampire" words (blood, undead, fangs, bitten) by pressing another key, with the test then reversed.
In order to make use of this powerful data, we have reviewed and categorised every word.
Propositions categorised here include words such as "influence", "need" and "require".
We also used a table of patterns to categorise each word in a Read term as positive, negative, optional or ignorable; this would define which words in a Read term need to be present in the text in order for the term to be matched.
Searching on a particular tag (eg China) calls up all links loaded under that tag on del.icio.us, all photos using it from Flickr and all blog posts categorised under that word.
In other words, we categorised women on the basis of their postoperative diagnosis, whereas decisions about whether to do breast conserving surgery were based on the histological diagnosis available before surgery.
The affective priming task (APT) involves participants categorising a target word as being positive or negative, with each target word preceded by another word called a 'prime' [ 20].
The key premise underlying the APT is that the experimenter can estimate the attitude toward the prime by examining how the presence of the prime influences the speed with which participants categorise the target word as 'positive' or 'negative'negative
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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