
430 Exam 2 Review
Authored by Katherine Moen
Science
University
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14 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Laura suffered brain damage and now has difficulty identifying objects. Specifically, she can see individual features, but has a hard time assembling those features into complex wholes. She is likely suffering from
prosopagnosia
apperceptive agnosia
aphasia
memory loss
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Participants’ recognition thresholds are
lower for frequently seen words.
higher for recently seen words.
not affected by priming.
lower for words with unusual spelling.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is an example of feature search?
Looking for a black dog surrounded by black and brown cats and dogs.
Looking for a black dog surrounded by squirrels
Looking for a black dog surrounded by other black dogs
Looking for two black dogs that match
4.
MATCH QUESTION
1 min • 1 pt
Match the situation to the outcome based on signal detection theory.
Miss
Not hearing your phone when it IS ringing
False Alarm
Not hearing your phone when it isn't ringing
Hit
Hearing your phone when it IS ringing
Correct Rejection
Hearing your phone when it isn't ringing
5.
REORDER QUESTION
1 min • 1 pt
Reorder the detectors in feature nets from first to last
Feature
Word
Letter
Bigram
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Feature nets help with word recognition based on a combination of information from multiple sources, rather than just one source. This means that feature nets prioritize:
efficiency
accuracy
detail
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What's the main difference between the McClelland Rumelhart Model and Feature Nets?
Feature nets only focus on signal enhancement
McClelland Rumelhart only focus on signal enhancement
Feature nets only focus on signal inhibition
McClelland Rumelhart only focus on signal inhibition
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