exam 3 Flashcards

(38 cards)

1
Q

What is knowledge?

A

-what is known about a particular field,
-expertise and skills acquired through
experience or education
-awareness or familiarity gained by
experience of fact or situation

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2
Q

Usefulness of categories

A
Can provide lots of information if the
category of something is known
• Don’t need to have experience with
specific cat to know about it
• Allow you to make inferences
• Understand behavior
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3
Q

What are concepts?

A

• = complex, abstract ideas or mental
representations about something
• = unit of knowledge

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4
Q

Definitional approach to categorization

A

we can decide whether something
is a member of a category by determining whether a particular object meets the definition
of the category

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5
Q

limitation of the definitional approach to categorization

A

Definitions work well for some things, such as geometric objects. Thus,
defining a square as “a plane figure having four equal sides, with all internal angles the
same” works. However, for most natural objects (such as birds, trees, and plants) and many
human-made objects (like chairs), definitions do not work well at all.

The problem is that not all of the members of everyday categories have the same
features. So, although the dictionary definition of a chair as “a piece of furniture consisting of a seat, legs, back, and often arms, designed to accommodate one person”
may sound reasonable, there are objects we call “chairs” that don’t meet that definition.

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6
Q

family resemblence

A

Family resemblance refers to the idea
that things in a particular category resemble one another in a number of ways

there is No definite criteria every member must
meet

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7
Q

prototype approach to categorization

A

the membership in a category is determined by comparing the object to a prototype that represents the category

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8
Q

prototypicality-2 types

A
Variation in a category
• High prototypicality- category member
closely resembles prototype
• Low prototypicality- does not resemble
a typical member of the category
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9
Q

exemplar approach to categorization

A

Involves comparing objects to an exemplar object you have experienced in the past
Exemplars are members of the category

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10
Q

hierarchy of categories

A

global-furniture-members
are dissimilar in some aspect – more
abstract

basic-table-members who
are maximally similar; broadest and
share the most attributes

specific-kitchen table-members
are less distinct than basic level; less
abstract

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11
Q

sentence verification task

A

A technique in which the participant is asked to indicate
whether a particular sentence is true or false. For example, sentences like “An apple is a
fruit” have been used in studies on categorization

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12
Q

mental imagery

A

experiencing a sensory impression in the
absence of sensory input

Not limited to just one sense-Can imagine tastes, smells, tactile experiences, sounds

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13
Q

imageless thought debate

A

The debate about whether thought is possible in the absence of
images

some psychologists taking up
Aristotle’s idea that “thought is impossible without an image” and others contending that
thinking can occur without images

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14
Q

history of imagery research

A

Wilhelm Wundt proposed that along with sensations & feelings, images are a basic element of
consciousness

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15
Q

behaviorism’s effect on imagery research

A

Watson says studying imagery is unproductive because they are invisible to everyone but the person seeing the images

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16
Q

paired-associate learning

A

A learning task in which participants are first presented with pairs
of words, then one word of each pairis presented and the task is to recall the other word

17
Q

paired-associate learning

A

A learning task in which participants are first presented with pairs of words, then one word of each pair is presented and the task is to recall the other word

18
Q

conceptual-peg hypothesis

A

A hypothesis, associated with Paivio’s dual coding theory, that states that concrete nouns create images that other words can hang onto, which enhances
memory for these words.

19
Q

Stephen Kosslyns mental scanning experiment

A

Participants would memorize a picture, Participants create mental images and then scan them in their minds and answer Y/N to find part of the image

20
Q

metcalfe & wiebe

A

did an experiment designed to distinguish
between insight problems and non-insight problems

Hypothesis: people should feel differently as they work on insight & non-insight problems
• Prediction: insight problems- bad at predicting progress
• Prediction: Non-insight problems-good at predicting progress
• Method: gave people both insight & non-insight problems (math problems)
• People gave warmth ratings every 15 sec
• 1 (cold-far from solution) to 7 (hot-close to solution)
• Algebra- warmth ratings slowly moved towards hot, used a method for solving problem, good at predicting success
• Insight- solution is sudden, no clear method, combining information in new ways, harder to monitor closeness to
answer

21
Q

funtional fixedness

A

An effect that occurs when the ideas a person has about an object’s function inhibit the person’s ability to use the object for a different function

an obstacle to problem-solving that is illustrated by Duncker’s candle problem and Maier’s two string problem. Situationally produced mental set is illustrated by the Luchins water jug problem

22
Q

fixation

A

people’s tendency to focus on a specific characteristic

of the problem that keeps them from arriving at a solution

23
Q

what is a problem, according to psychologists?

A

A problem occurs when there is an obstacle between a present state and a goal and it is not immediately obvious how to get around the obstacle.
A problem is difficult and the solution not immediately obvious

24
Q

means-ends analysis

A

A way of solving a problem in which the goal is to reduce the difference between the initial and goal states; subgoals

25
subgoals
intermediate states that are closer to the goal
26
reasoning
a cognitive process in which people start with | information and come to conclusions that go beyond that information
27
inductive reasoning
reasoning where a conclusion follows from a consideration of evidence. This conclusion is stated as being probably true rather than definitely true
28
deductive reasoning
Reasoning that involves syllogisms in which a conclusion logically follows from premises
29
categorical syllogism
A syllogism in which the premises and conclusion describe the relationship between two categories by using statements that begin with All, No, or Some
30
belief bias
Tendency to think a syllogism is valid if its conclusion is believable or that it is invalid if the conclusion is not believable
31
Conditional syllogism
Syllogism with two premises and a conclusion, like a categorical syllogism, but whose first premise is an “If … then” statement
32
falsification principle
The reasoning principle that to test a rule, it is necessary to look for situations that would falsify the rule
33
law of large numbers
The larger the number of individuals that are randomly drawn from a population, the more representative the resulting group will be of the entire population
34
availability heuristic
states that events that are more easily remembered are judged as being more probable than events that are less easily remembered
35
illusory correlation
occur when a correlation between two events appears to exist, but in reality there is no correlation or it is much weaker than it is assumed to be can occur when we expect two things to be related, so we fool ourselves into thinking they are related even when they are not.
36
representativeness heuristic
``` states that the probability that A is a member of class B can be determined by how well the properties of A resembles the properties we usually associate with class B ```
37
representativeness heuristic
states that the probability that A is a member of class B can be determined by how well the properties of A resembles the properties we usually associate with class B
38
conjunction rule
states that the probability of a conjunction of two events (A and B) cannot be higher than the probability of the single constituents (A alone or B alone).