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cog chp 4 top 12

Feature Nets and Word Recognition

QuestionAnswer
Simple -> Complex Bottom up processing
Complex->Simple Top-down processing
What is the order of the detectors on the recognition scale? Start from Feature-> Letter-> Word Ending
Define Activation level in every detector input, the activation is increased
What happens in the response threshold? Detectors fire once it's reached
T or F, Detectors are complex assemblies of neural tissue, not single or groups neurons T
Define frequency Frequent firing in higher starting activation Exercise effect
Define Recency recent firing results in higher starting activation Warmup effect
T or F, repetition forming boosts respective detector T
T or F, word commonality does not boost respective detector F
Low frequency= Unfamiliar word
High frequency= Familiar word
T or F, Well formed non/words include the bigram layer T
Define bigram 2 letter combo
Where is bigram on the simple-> complex scale? below word detector
T or F, people cannot recognize non-words at all F
The more efficient a word, the more no matter how fast you may answer, it may be incorrect
What are the upsides to Feature nets Help us resolve unclear inputs fast
Can autocorrect you to the wrong word is what is a down side of feature nets
Define disturbed knowledge knowledge is not locally represented feature nets contain distributed knowledge come from experience
Created by: Melis2003
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