Question 1

If a learner cannot explicitly identify rules of learning, and are not conscious of that which has been learned, the following is true.

Accepted Answer

A)   Learning may only take place at a conscious level.
B)   Learning that has taken place may be observed in behavior.
C)   A conclusion about learning is not possible.
D)   A learning disability exists. B

Question 2

Neural network models are different from the conventional computer in that

Accepted Answer

A)   neural networks can make inferences without being given specific rules.
B)   they are symbol-based models.
C)   they need specific rules in order to make inferences.
D)   they are perfect analogies for human thought processes. A

Question 3

Which of the following does not characterize an instance of learning?

Accepted Answer

A)   implicit, non-verbalized rules
B)   conscious and rule driven
C)   explicit and unconscious
D)   explicit and symbol manipulating C

Question 4

Like humans, neural networks can

Accepted Answer

A)   make inferences after being given rules for doing so.
B)   make inferences without being given specific rules for doing so.
C)   make inferences, but not accurate ones.
D)   not make inferences.

Question 5

An algorithm, as a problem-solving procedure, may be described by which of the following?

Accepted Answer

A)   the most common subtype of a heuristic approach
B)   used only recently in the creation of programs
C)   the systematic consideration of alternatives
D)   easy application due to the minimal impact on memory storage

Question 6

Which of the following is true with regard to learning and awareness?

Accepted Answer

A)   Learning involves awareness by definition.
B)   One may be unaware of what has been learned.
C)   Learning may not occur without awareness.
D)   Lack of awareness would negate the usefulness of that learned.

Question 7

To the extent that a computer is capable of using algorithms, the following is true with regard to a computer/Chess Master chess match:

Accepted Answer

A)   The Chess Master will win the match.
B)   The Chess Master will lose the match.
C)   Computers will sometimes win, and the Chess Master will sometimes win.
D)   No prediction can be made with regard to outcome.

Question 8

Viewing computer capability as cognitive brute force is

Accepted Answer

A)   accurate.
B)   not presently accurate but soon will be.
C)   not entirely correct.
D)   only true of a PDP model.

Question 9

Though sometimes referred to as a myth, computers are indeed dependent on (slaves to) their programmers. This is confirmed by

Accepted Answer

A)   connectionist models.
B)   symbolic models.
C)   the combination of connectionist and symbolic models.
D)   It has not been confirmed.

Question 10

"Black Box" implies

Accepted Answer

A)   one reaches a dead end.
B)   a controlled environment.
C)   a maze.
D)   the contents of the mind are unknown.

Question 11

The Turing Test provides a good example of

Accepted Answer

A)   the difference between identifying words (decoding)  and understanding (comprehension) .
B)   a computer model that demonstrates that computers can read.
C)   a computer can think.
D)   the fact that a computer itself is intelligent.

Question 12

Which of the following is an assumption of a connectionist model?

Accepted Answer

A)   Rules and symbols are used in all human cognitive processing.
B)   In learning, rules must be both explicit and conscious.
C)   A serial digital computer is an improvement over a PDP computer.
D)   The "fuzzy logic" used by humans at times may be modeled.

Question 13

If a machine produces correct responses for complex problems, then

Accepted Answer

A)   it understands those problems.
B)   it has the potential to understand the problem.
C)   it understands the responses.
D)   it produces correct responses to problems.

Question 14

A type of processing that allows computers to program themselves and respond in unpredictable ways is called

Accepted Answer

A)   artificial intelligence.
B)   parallel distributed processing.
C)   symbolic model processing.
D)   reductio ad absurdum

Question 15

A parallel distributed processing computer (PDP) would seem to be useful in understanding the functioning of the human brain for which of the following reasons?

Accepted Answer

A)   It is a more advanced version of the serial processing digital computer.
B)   It has been developed specifically for this purpose.
C)   The brain functions more like a PDP computer which performs functions sequentially and with extreme speed.
D)   The brain functions more like a PDP computer which performs numerous functions simultaneously.

Question 16

The following conclusion may be reached regarding connectionist models.

Accepted Answer

A)   They are less descriptive of learning than symbolic models.
B)   They successfully achieve their purpose-the elaboration of symbolic models.
C)   They are not only able to learn, they do so like humans.
D)   It is questionable whether either connectionist models or symbolic models can be made to learn like humans.

Question 17

Which of the following represents correspondence in function between humans and computers?

Accepted Answer

A)   stimuli / keyboard
B)   sense organs / software
C)   wetware / software
D)   cognitive acts / software

Question 18

In which of the following ways is a connectionist model an accurate representation of human thinking?

Accepted Answer

A)   Its approach is always systematic and rational.
B)   Use is made of both logic and insight.
C)   The sole approach to problem solving is always "fuzzy logic."
D)   The rules of logic are always used.

Question 19

A connectionist model assumes that

Accepted Answer

A)   all information is represented in symbols.
B)   learning may be implicit.
C)   thinking does not follow identifiable rules.
D)   all logic is "fuzzy logic."

Question 20

Which of the following represents correspondence in structure between humans and computers?

Accepted Answer

A)   wetware / hardware
B)   hardware / software
C)   nervous system / software
D)   peripherals / brain

Exam 8: Symbolic Models of the Mind and Neural Networks