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SPRING 2023 > Cog Psych: Exam #4 > Flashcards

Cog Psych: Exam #4 Flashcards

1
Q

symbolic definition of language

A

the use of symbols to represent ideas

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2
Q

Generative definition of language

A

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

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3
Q

Structured definition of language

A

there are rules for how language is organized and produced (grammar)

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4
Q

competence definition

A

set of rules that all users of language have available
ex: what could be real words (all fake but some follow the rules - can be subconscious)

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5
Q

performance definition

A

how rules are implemented within language behavior - these are individual differences

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6
Q

Hierarchy of language

A

phoneme - morpheme - word - phrase - sentence

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7
Q

Phonemes definition

A

sounds of language

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8
Q

Morpheme definition

A

smallest unit of meaning

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9
Q

bound morphemes

A

units that cannot stand along but are appended to stems (s, ed, ing)

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10
Q

free morphemes

A

units that stand alone

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11
Q

inflectional morphology

A

typically modify words to fit structure of sentence (does not change meaning)
ex: quacks vs. quacked

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12
Q

Derivational morphology

A

changes the meaning when it is modified
ex: gentleman (person) —> gentlemanly (a way of acting)

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13
Q

Evidence for language structure

A

speech error: unintended deviations from the speech-plan
* you can only make errors within a level*

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14
Q

Types of speech errors

A

exchange error: an error in which two linguistic units are substituted for each other during sentence production
- word exchange: writing a mother to my letter
- morpheme exchange: he relaxes –> he relax
- phoneme exchange: spill beer –> speer bill

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15
Q

Text comprehension requirements

A

1) perception: sensory input for words
2) attention + WM: updating, inhibition, shifting
3) episodic memory: retaining knowledge of prior material you read
4) semantic memory: retrieving meaning of words as you read and using schemas
5) visual imagery

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16
Q

Local coherence

A

integration of ideas within the immediate context

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17
Q

Study with local coherence (words with multiple meanings)

A

words that have multiple meanings:
1) equibiased meaning: 50-50 used
2) non-equibiased meaning: one meaning is used more than other
- track eye movements
Equibiased; if the meaning occurred after, you took longer
Nonequibiased: if the meaning occurred after, you took longer (have to go back and reread as you picked the wrong one)
Equibiased and nonequibiased: if the meaning occurred before, everything was the same

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18
Q

How quickly does context occur?

A

given a sentence with an ambiguous word (could have two meanings ex here is “bug”) - probed with a related word (will cue you into correct meaning) either immediately after or 3 syllables after
- results: if immediately primed, you are quicker to say “spy” AND “ant” are words - both meaning are activated
- if delayed primed, you are faster for “ant” - context has operated in the time span and you only have one meaning come “online” and the other is forgotten

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19
Q

expectations

A

you are quicker to say something is a word if you were expecting it

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20
Q

Global coherence definition

A

track and integrate major ideas of the story

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21
Q

Study on central ideas

A

given a story
1) without a cue: asked to recall as many details as possible
2) cue: told the central idea of the story
results: you are better if you know the central idea before reading the story - you organize facts based on a central idea/goal

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22
Q

Interruptions in a story

A

you remember interruptions (to the achievement of the central goal) the best in a recall test (better than script actions or irrelevant details)

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23
Q

causal chains

A

things that relate to each other - you can not achieve the goal if any of the connections are missing
ex: goal: walking into the house – take out keys - unlock door - open door

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24
Q

Causal chains and memory

A

events that have more casual connections in a story the better they are recalled on a later test - you think more steps mean it is more important so you focus on this

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25
Q

process memory

A

a memory that maintains active traces of past information to process incoming information in the present moment
- temporal stages: neural processing is slightly different based on what proceeds it

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26
Q

temporal receptive window

A

the window of time in which prior information can affect the processing of new information
- phonemes have a short TRW and narratives have a long TRW

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27
Q

Kintsch’s model of text comprehension

A

text: represented by a series of proposition
reader: knowledge and goals of the readers established connections and infers facts not directly stated

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28
Q

proposition definition

A

a meaningful idea that typically consists of several words

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29
Q

STM and reading

A

1) A limited set of propositions are maintained in STM → sentences get split into propositions are organized by important
2) Items in the new section (next set of important propositions) are related to old items in the STM `

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30
Q

Reinstatement search

A

a search through LTM (where the unpicked propositions go) to place words in STM where they can be used to integrate a text
- happens when you can not match the new propositions in STM with the old propositions in STM

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31
Q

Propositions and memory

A

Conditions: target → “the forest was on fire”
Context repetition: carol continued to be distressed as she drove down the road (more related to overall context of story)
Target repetition: repetition related to the sentence verification you will be asked
Subject repetition: carol continued to be distressed as she drove through the forest
Complete repetition: Complete Repetition: “Carol continued to be distressed as they drove down a dirt road through the smoke filled forest”
No repetition: carol continued to be distressed and lit a cigarette
Results: you are fastest when you get a complete repetition → the more propositions you have to relate to other pieces of information, the better you remember it

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32
Q

Inferences

A

take time and effort
- you take longer to verify a sentence if you have to make an inference about the content rather than if it is directly stated

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33
Q

Major themes of Kintsch’s model

A
  • New information is most easily integrated if it related to items in STM
  • If not, a reinstatement search through LTM is conducted
  • If this fails, ideas can be integrated through inference but this is costly
  • If all else fails, a new network is started → leads to separate ideas that are not well integrated (thus have poor recall)
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34
Q

situation model

A

integration of prior knowledge and text information to construct an understanding of the situation being described in a text

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35
Q

compensatory models

A

a decision making strategy that allows positive attributes to compensate for negative ones
1) additive compensatory models
2) additive-difference compensatory model

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36
Q

Additive compensatory models

A

Rate each alternative on several attributes and then sum them → can have different weights and then add them all up at the end with the highest score winning

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37
Q

Additive-difference compensatory model:

A

a strategy that compares two alternative by adding the different in their values for each attribute - compares alternatives line by line

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38
Q

Noncompensatory models

A

a decision making strategy that rejects alternatives that have negative attributes without considering the positive attributes
1) elimination by aspects
2) conjunctive model

39
Q

Elimination-by-aspects

A

a decision making strategy that evaluates one attribute at a time and rejects alternatives whose attribute values fail to satisfy a minimum criterion
ex: no calculations; just compare to minimum threshold - order is important

40
Q

Conjunctive model

A

a strategy that evaluates on alternative at a time and rejects it if ANY of the attributes fail to satisfy a minimum criterion
- The first alternative that meet all the criteria is the one you pick → don’t do an exhaustive search of all possibilities

41
Q

Satisficing search

A

a strategy that follow the conjunctive model and therefore selects the first alternative that satisfies the minimum criterion for each attribute
- May not be the best but it meets all the criteria so it is good enough

42
Q

Which method of decision making do we use?

A

Study: given attributes on apartments and could flip as many as they wanted over in whatever order they wanted → goal: pick the best apartment
results:
1) The more alternatives you have to consider, the less information you look at (you turn over less cards)
2) used conjunctive or elimination by aspects to reduce the load when had a lot of alternative
3) with just a few alternative, they may pick a more cognitively demanding option (additive or additive difference)

43
Q

Is extra cognitive load worth it in decision making?

A

Study: Participants: financial professional evaluate a firm’s financial stability
trained on one of three strategies: Additive, Additive difference, Elimination by aspects
- all theories had people pick the best option BUT additive/additive difference took much more time it does not give you a cognitive boost to consider all options

44
Q

recognition heuristic

A

if one of two or more objects is recognized and the other is not, we infer the recognized object has the higher value

45
Q

Uncertainty in decision making

A

Uncertainty: lock of knowledge about which events or attributes will actually occur
We must estimate probabilities of the events → use heuristic to do so + sometimes these are accurate but often not → we are bad at estimating probabilities

46
Q

Availability heuristic

A

use information about which you have seen / heard about most recently or often to make your decision
Usually know of both options but one comes to mind more readily

47
Q

Can decisions differ by mood state?

A

Yes - your current mood can affect your estimates of the probability of an event
When you are in a good mood: you rate the positive event as more likely than the negative event (and same for the negative event and negative mood)

48
Q

Representative heuristic

A

the extent of which event is typical of a larger class of events
ex: Coin flips and birth gender sequence is random → should be 50%-50% BUT We expect HHHHHT to be less likely than THTHTHTH even though they are exactly the same

49
Q

prior probability

A

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

50
Q

study with prior probability

A

Study: given characteristics about a person and then asked the likelihood that he is a lawyer or a engineer out of a specific sample → we know the probability based on the sample sizes
Results: when you are given no information, you rely on the prior probability. When given information, you rely on that and ignore the prior probability

51
Q

Expected value

A

the average value as determined by combining the value of events with the probability of their occurrence

52
Q

does value of decision matter?

A

yes you ave to look at prior probability AND the value of the decision to you
ex: COVID 19
- Probability: that threat is real
- Value: the value to not get sick or protect those you love

53
Q

Do people follow expected value rules?

A

No - we are not rational decision makers
even if trained: 33% made a better choice 66% did not change or made a worse choice - most people do not use expected value even when it is explained

54
Q

subjective value

A

has to be determined by the decision maker → what is this worth to you

55
Q

does how something is obtained influence value?

A

Study: Study: given a coffee mug
½ told it was because of a good exam score
½ told it was random
Can either keep the mug or trade it back for money
Results: students who thought they earned it traded it for more money (thought it was worth more) → worth different things to different people based on how they acquired it

56
Q

Subject probabilities

A

instead of using real probabilities, use our guess of what the probabilities are (usually wrong, based on heuristics)

57
Q

Risk dimensions: do individuals weight winning and losing equally?

A

WIN: Would you rather win $900 or have a 90% chance of winning $1000
- Prefer the sure bet (take $900)
LOSE: Would you rather lose $900 or have a 90% chance of losing $1000
- People prefer to gamble

58
Q

Loss aversion

A

reaction to loss if more severe than reactions to corresponding gains

59
Q

Decision frames

A

some people emphasize certain risk dimensions over other

60
Q

positive vs negative framing

A

Positive framing: 30/50 were successful
Negative framing: 20/50 were unsuccessful
People in the positive framing condition bought into the project more (invested more into it) → even though everything was the same (same probability of success) except for the phrasing

61
Q

Varying start information: how does this affect decision making?

A

If you say this glass is “half empty,” people say it started out as full
If you say this glass is “half full,” people say it started out as empty

62
Q

Risk adverse

A

we prefer to avoid risk
Heads you win $1 tails you lose $1
Heads you win $100, tails you lose $100
Same selected value
We all prefer the $1 option → we are risk averse

63
Q

why would someone pick a risk condition?

A

When people select risk, it’s usually because they did not perceive it as as risk correctly → we almost never consciously pick a risky condition

64
Q

Ways to help others make healthy decisions

A

Inform: think about how you are framing the decision
Order of information: also important → order of which choices are given affect final outcome
Frequencies are easier than probabilities
Incentivize
Guide
Opt in vs. opt out decision → what is the default?

65
Q

three types of problems

A

1) arrangment
2) inducing structure
3) transformation

66
Q

example of an arrangement problem

A

anagrams

67
Q

Skills needed for arrangement problems

A

1) Fluency in generating possibilities: need to generate many solution and discard the ones that don’t fit
2) Retrieval of soliton patterns: have to retrieve possible words from memory
3) Rules of language: frequency of letter combinations in allowable combinations

68
Q

Insight

A

the sudden discovery of a solution following many unsuccessful attempts

69
Q

Arrangement and insight

A

Study: solve different kinds of problems (anagrams, logic based problem)
Every ten seconds you hear a “tap” and then you have to record how close you feel you are to the solution from 0 to 10
Results: you usually go from 2 to 10 suddenly in the last ten second → you don’t feel like you are close at all and then all the sudden you have it in 10 seconds

70
Q

Barriers to arrangement problems

A

1) imposed constraints: ways we are used to seeing things (ex: matchstick problem)
2) Functional fixedness: ex: the box problem → given a problem where the material are either in the box or beside an empty box (you need the empty box to solve the problem)
Results: when the boxes were empty, you solves it 100% of the time → when the boxes were field, only 40% solved it (they saw the box as just something that held the materials, not part of the tools you have)

71
Q

inducing structure definition

A

a problem that requires finding the pattern amount a fixed set of relations

72
Q

inducing structure examples

A

1) Series extrapolations: finding a pattern among a sequence of items to continue the sequence (Ex: 1-2-8-3-4-6-5-6-?)
2)Analogy: a four term problem that required finding the answer that completes a relationship (Ex: merchant: sell, customer: __)

73
Q

Skills needed for inducing structure

A

Identify the relations among the different components
Fit relations together into a pattern

74
Q

Progressive matrices

A

figure out a pattern and input the correct missing space

75
Q

Are skills within induced structure problems generalizable?

A

if you are good at one, you are good at another → generalizable set of skills within this category
- also trainable

76
Q

Transformation problem definitions

A

a problem that required changing the initial state through a sequence of operations until it matches the goal state
differs from others because the goal state is provided
ex: men and elves

77
Q

Skills needed for transformation problems

A

Means-ends analysis: identify the different between the current state and the goal state and selection actions that will reduce those differences

78
Q

Newell and simon’s theory

A

Step 1: construct a computer program that can solve a given problem via a series of steps
Step 2: collect detailed behavioral data from human participants and see if they match
Step 3: see if the same skill set can be used to solve similar problems

79
Q

What does Newell and Simon’s theory require?

A

Requires self insight: do you actually know what step you are following
Results in a lot of details from participation and now always clear how to best summarizer
Often requires a large sample size

80
Q

Assumptions of Newell and Simon’s theory

A

Limitations of the person affect efficiency of problem solving
1) Capacity of STM: how many mental steps can be accomplished
2) Time to get things into LTM: may be difficult to remember all the steps to a problem - you repeat incorrect steps you have already tried

81
Q

Problem space

A

the set of choices available at each step → all possible choices

82
Q

Search space

A

the set of choices that are evaluated at each step as determined by the problem solver → the possible choices you decide to consider at a time

83
Q

How do we choose what to focus on in our search space?

A

1) Previous experience with the same task or nearly identical one
2) Previous experience with analogous task
3) Plans stores in LTM the generalize over a range of tasks
4) Information accumulation while solving a problem: like sudoku, what you add constraints the future possibilities guiding you

84
Q

general strategies for problem solving

A

1) means-ends analysis
2) forming subgoals
3) using analogy
4) representational transfer

85
Q

Forming subgoals study

A

Study:
Control group: just solve the problem however you can
Subgoal: given a hint about where to start (ex: try to get all the humans on one side)
Results:
- People given a subgoal took less steps to complete the task → breaking things into smaller, manageable tasks helps
- Caveat: sometimes you don’t know what the subgoal is → could be remedied with training

86
Q

Using analogy to solve a problem

A

not the same as solving analogy → solving a problem by recalling the soliton to a similar problem

87
Q

What does using analogy require

A

Noticing the similarity between the two problems
Recalling the original solution from LTM

88
Q

Study with using analogy

A

If given similar problem and then the problem, success rate increases
- More similar the problem, the better the success rate
- Caveat: only if given hint that the problems are similar or not you do better
People do not spontaneously retrieve the previous information and apply it to the new solution → it is in memory but not used

89
Q

using analogy and similar vs. dissimilar storeis

A

Given two similar stories OR two dissimilar stories relative to each other (NOT the problem you are trying to solve)
- Performance is best (even without a hint) when you get two dissimilar stories
- Dissimilar condition: the problems use the same method but in two different scenarios → the more contexts you see the general method working in the better you can later apply it to another scenario

90
Q

Schemas and problem solving

A

Relates back to schemas: if you have a good schema (creating a general idea of how problems like these can be solved), you are better at solving the problem before the hint

91
Q

Underlying principles and decision making

A

Ex: can give the underlying principle: read the story and the last sentence is “they attributed their success to divide and conquer” (or whatever method they used)
Better when given the underlying principle AND you had a good schema

92
Q

Representational transfer

A

Use of the same format in solving two problems
Different from analogical transfer: you are not transferring the solution but the way information is presented

93
Q

Types of representation

A

Network: islands and bridges vs. shaking hands
Hierarchical: sorting words vs. rat in a maze
Part whole: venn diagram vs. prove a math problem with angles

94
Q

Study with representational transfer

A

Findings:
1) No difference between the control and no hint condition → you do not spontaneously transfer representation from one situation to the other
2) Network example better than hierarchy and part whole → network is the only one we can transfer and is helpful

SPRING 2023 (5 decks)

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