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PSYCH 3330 > Final Exam > Flashcards

Final Exam Flashcards

1
Q

language

A

a collection of symbols and rules for combining symbols, which can express an infinite variety of messages

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2
Q

symbolic

A

the use of symbols, such as spoken or written words, to represent ideas

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3
Q

generative

A

the capability to produce many different messages by combining symbols in different ways

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4
Q

structures

A

the organization imposed on a language by its grammatical rules

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5
Q

Broca’s aphasia

A

a language disorder attributed to damage in the frontal lobe of the brain

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6
Q

Wernicke’s aphasia

A

a language disorder attributed to damage in the temporal lobe of the brain

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7
Q

slip of the tongue

A

a speech error

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8
Q

exchange error

A

an error in which two linguistic units are substituted for each other during sentence production

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9
Q

word exchange

A

an error in which two words are substituted for each other during sentence production

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10
Q

morpheme exchange

A

an error in which two morphemes are substituted for each other during sentence production

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11
Q

phoneme production

A

an error in which two phonemes are substituted for each other during sentence production

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12
Q

phase-structure grammar

A

a set of rules for partitioning a sentence into its grammatical units

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13
Q

transformational grammar

A

a set of rules for transforming a sentence into a closely related sentence

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14
Q

ambiguous sentence

A

a sentence that has more than one meaning

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15
Q

surface structure

A

the structure of a spoken sentence

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16
Q

deep structure

A

the underlying meaning of a sentence

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17
Q

high-constraint sentence

A

a sentence that produces a high expectation for a particular word

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18
Q

lexical decision task

A

a task that requires people to decide whether a string of letters is a word

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19
Q

low-constraint sentence

A

a sentence that produces an expectation for a broader range of words

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20
Q

arrangement problem

A

a problem that requires rearranging its parts to satisfy a specified criterion
Task: find a new arrangement or relationship
Provided: to-be-arranged components and criteria
Skills required:
-generating possibilities (leave behind possibilities that don’t work)
-retrieval of solution patterns (deal with things that we have stored in LTM
-knowledge of principles constraining the search

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21
Q

anagram

A

a problem that requires rearranging a string of letters to form a word

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22
Q

insight

A

the sudden discovery of a solution following unsuccessful attempts to solve a problem

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23
Q

functional fixedness

A

the tendency to use an object in a typical way

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24
Q

inducing-structure problem

A

a problem that requires finding a pattern among a fixed set of relations
Task: identify principle/rule/underlying structure to explain how components are related
Provided: organized components (initital stage, goal is to identify one of these things, and complete the problem
Skills required: identifying relations among components, fitting relations into patterns
Creativity: more creative people are able to see more patterns within a problem

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25
Q

series extrapolation

A

a problem that requires finding a pattern among a sequence of items to continue the sequence in the same pattern

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26
Q

analogy problem

A

a four-term problem that requires finding the answer that completes the relation: A is to B as C is to D

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27
Q

transformation problem

A

a problem that requires changing the initial state through a sequence of operations until it matches the goal state
Task: transform initial state into specified goal state
Provided: explicit description of start state, goal state, and permissible legal operators
Skills required: planning based on means-end analysis (difference reduction)
Ex: tower of Hanoi

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28
Q

means-end analysis

A

a strategy that can be used to solve transformation problems by eliminating differences between the initial and goal states

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29
Q

simulation problem

A

a computer program that attempts to reproduce the operations used by people to carry out various tasks

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30
Q

measure of sufficiency

A

a demonstration that the instructions in a computer program are capably of solving a problem

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31
Q

verbal protocol

A

a record of verbalized thought processes

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32
Q

search space

A

the set of choices at each step in solving the problem as determined by the problem

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33
Q

problem space

A

the set of choices evaluated at each step in solving a problem as determined by the problem solver

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34
Q

operator

A

an action that is selected to solve problems

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35
Q

table of connections

A

a table that links differences between problem states with operators for eliminating those differences

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36
Q

heuristic

A

a strategy that is often, but not always, helpful in solving problems

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37
Q

algorithm

A

a set of rules that will solve a problem if correctly followed

  • guaranteed solution
  • may be time consuming
  • must be used systematically
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38
Q

subgoal

A

a goal that solves part of the problem

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39
Q

analogy

A

solving a problem by using a solution to a related problem

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40
Q

analogical transfer

A

use of the same solution in solving two problems

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41
Q

representational transfer

A

use of the same format (such as a matrix) in solving two problems

42
Q

normative model

A

a model that describes what people should do

  • no constraints (perfect kind of decision that could be developed)
  • perfect rational agents (usually computer generated, humans are not perfectly rational)
  • don’t usually see these models in humans
43
Q

descriptive model

A

a model that describes what people actually do

  • takes into account our desires, what we find important
  • humans use these models
44
Q

compensatory model

A

a strategy that allows positive attributes to compensate for negative ones (pros and cons list)

45
Q

additive model

A

a strategy that adds attribute values to assign a score to each alternative (compensatory model)
attractive features + unattractive features = total score for each alternative
modifications: attribute weights and interactions
ex: deciding between 2 apartments and having certain criterion they have to pass

46
Q

additive-difference model

A

a strategy that compares two alternatives by adding the difference in their values for each attribute (compensatory model)
adding up the scores on the 2 apartments and comparing them based on different scores

47
Q

noncompensatory model

A

a strategy that rejects alternatives that have negative attributes without considering their positive attributes
(any that don’t meet our requirements will cause that alternative to be eliminated from the choices)

48
Q

elimination by aspects

A

-a strategy that evaluates one attribute at a time and rejects those alternatives whose attribute values fail to satisfy a minimum criterion
-created by Tversky
-order of attribute consideration is important because that helps us in our elimination process
-advantage: no calculations
-disadvantage: possible failure to find “best” alternative
(noncompensatory model)

49
Q

conjunctive model

A

-a strategy that evaluates one alternative at a time and rejects it if the value of one of its attributes fails to satisfy a minimum criterion (noncompensatory model)

50
Q

satisficing search

A

a strategy that follows the conjunctive model and therefore selects the first alternative that satisfies the minimum criterion for each attribute

51
Q

uncertainty

A

lacking knowledge about which events will occur

52
Q

availability heuristic

A

estimating probability by the ease with which examples can be recalled

  • affected by: recency, familiarity, mood
  • can cause: illusory correlations, systematic biases in estimating probabilities
53
Q

representative

A

the extent to which an event is typical of a larger class of events (prototype)

  • use of past experiences
  • mental representation
  • used to judge membership in a class
  • judge similarity to stereotype

insensitive to:

  • prior probability outcomes
  • sample size
54
Q

prior probability

A

the probability that an event will occur before obtaining additional evidence regarding its occurence

55
Q

expected value

A

the average value, as determined by combining the value of events with their probability of occurrence (normative model)

56
Q

utility

A

subjective value as determined by the decision maker (sentimental items, earning of an item, gift)

57
Q

subjective probability

A

an estimated probability as determined by the decision maker

58
Q

subjective expected utility

A

a variation of expected value that uses utilities and subjective probabilities instead of values and probabilities

59
Q

risk dimension

A

a component of a gamble such as the probability of winning or the amount of a loss

60
Q

duplex gamble

A

a gamble in which the probability of winning is assigned independently of the probability of losing (Slovic and Lichtenstein)

61
Q

decision aid

A

a tool for helping people make better decisions

62
Q

Baye’s theorem

A

a normative procedure for revising a probability by combining a prior probability with evidence

previous probability + new evidence = new probability

63
Q

behaviorist approach to language acquisition

A

association and pairing words (BF Skinner)

64
Q

chomsky’s response to behaviorist’s opinion on language acquisition

A
  • infinite number of sentences
  • relations between non adjacent words
  • sentences have a hierarchical structure
  • based on rules
  • innate knowledge
65
Q

what is a language acquisition device?

A
  • located in our head

- when we are exposed to spoken language it infers these rules and produces somewhat grammatically correct sentences

66
Q

support for the language acquisition device

A
  • children overgeneralization of rules
  • children’s speed in acquiring language defies experience
  • parent’s inconsistent in reinforcement
67
Q

phonemic resoration

A

when something occurs when you lose phonemes (anything like clapping or construction noises, we lose the vowels)

68
Q

tongue shape affects ______

A

vowels

69
Q

breathing affects ______

A

consonants

70
Q

grammar = ______

A

syntax

71
Q

meaning = _______

A

semantic

72
Q

speech errors give evidence for what?

A

the hierarchical structure of language

73
Q

limitations of phase structure grammar:

A

it does not answer how we can change the following:

  • an active statement into a passive statement
  • a positive statement into a negative statement
  • an assertion into a question
74
Q

problems of phase structure grammar and transformational grammar:

A
  • neither can explain ambiguous sentences

- chomsky modified TG to include deep and surface structures

75
Q

problem

A
  • purposeful, goal directed action
  • cognitive processing happens when we are actively engaging
  • the solution is not immediately available meaning that you have to actually do some solving
76
Q

3 features of problems

A
  1. initial state = what’s given
  2. goal state (solution) = what you’re trying to do
  3. obstacles or rules = constraints, not ones that we put on ourselves but something else has placed them
77
Q

Gestalt approach to arrangement problems

A

concerned with entities/experience as a whole rather than consisting of parts

  • emphasized problem structure
  • correct organization results from insight
  • ex: Metcalfe
78
Q

case based reasoning in inducing structure problems

A

when you use a problem that you’ve already solved and use it as an analogous problem for the one you’re currently working on (like how on exams you looks back at previous questions to find answers to other questions)

79
Q

Newell and Simon’s theory components

A
  • simulation programming (computer program that has theory of human problem solving)
  • verbal protocols (give human participants a problem, then when the human started solving the human would speak outloud their thought process)
80
Q

why did Newell and Simon use computers?

A

program works = no steps have been left unspecified

81
Q

theoretical assumptions of Newell and Simon’s theory

A

performance is limited by:

  • capacity
  • storage time
  • retrieval time

good plan ignores unpromising storage

  • search space
  • problem space
  • factors (previous experience, general strategies stored in LTM, infor acquired while solving problem)
82
Q

Newell and Simon’s means-end analysis

A

general procedure (select operators that lead us closer to goal state), operators (permissible changes to problem state), table of connections (relation between operators and differences in problem states)

83
Q

Novick and Hemlo experiment on representational transfer

A

conditions:

  • control
  • relevant example with NO hint
  • relevant example with hint

results:

  • no difference between control and relevant example with NO hint
  • success of hint depended on the kind of relevant representation
84
Q

what is decision making?

A

assessing and choosing between several alternatives

  • attributes: things you put on pros and cons list
  • intuitive decision making: things we decide that we don’t even have to think about (ex: stoplight)
  • analytic decision making: how to make more difficult choices
85
Q

types of decisions

A
  1. certainty: known outcome, we know the alternatives and what they mean for us
  2. risk: known probability, we don’t have a known outcome, a lot to do with gambling
  3. uncertainty: unknown outcome and probability, most anxiety provoking
86
Q

Payne’s experiment

A
  • how people search for information
    • flipping cards over and looking for # of alternatives and # of attributes

results: students changed strategies as the number of alternatives decreased
- many alternatives: conjunctive or elimination by aspects
- few alternatives: additive or additive-difference strategy

87
Q

Kahneman and Tversky

A

studied estimating probabilities by using:

  • heuristics
  • availability
  • representativeness
  • anchoring
  • simulation
88
Q

misconception of chance (representativeness)

A

people take a normal situation and try to say that it’s rare (Gambler’s Fallacy)

89
Q

misconception of regression (representativeness)

A

people take a rare occasion and try to say that it’s normal

90
Q

representativeness and confidence

A

-we adjust probabilities so that we fit into a group

91
Q

illusion of knowing (representativeness)

A

occurs when you interact with people who are knowledgeable on a subject and all of a sudden you’re an expert

92
Q

testing problem (representativeness)

A

you bomb a test because you didn’t study well and don’t really know the material, not because the exam was crazy hard

93
Q

anchoring and adjustment heuristic

A
  • estimate value/size of quantity
  • start from initial value and adjust to final estimate
  • people are influenced by an initial anchor value
    • anchor may be unreliable
    • adjustment is often insufficient
  • anchors may be qualitative (first impressions)
94
Q

overestimate probability of _____ events

A

conjunctive

95
Q

underestimate probability of _____ events

A

disjunctive

96
Q

simulation heuristic

A

probability of an event based on how easy it is to mentally picture that event
-special type of adaptation of the availability heuristic

97
Q

hindsight bias (simulation heuristic)

A

tendency of people to overestimate their ability to have predicted an outcome that could not possibly have been predicted
“I knew it” or “Obviously”

98
Q

expected value (EV) equation

A

calculate by multiplying the values (V) of each possible outcome by its probability (P) and summing values
EV = [P(win) x V(win)] + [P(loss) x V(loss)]

play if EV is positive
don’t play if EV is negative
FAILS because of normative model

99
Q

what are the 4 risk dimensions?

A
  1. probability of winning
  2. amount of win
  3. probability of losing
  4. amount of loss
100
Q

why do people take risks?

A

they don’t see the risky consequences (real world example: jury decisions)

PSYCH 3330 (3 decks)

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