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Psychology 277 > Midterm 2 > Flashcards

Midterm 2 Flashcards

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1
Q

where is the signal detection theory stemmed from?

A

radar activation (world war II)

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2
Q

what does the signal detection theory state?

A

that nearly ALL reasoning and decision making takes place in the presence of some uncertainty

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3
Q

what is the purpose of signal detection theory?

A

provides a precise language and graphic notation for analyzing decision making in the presence of uncertainty

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4
Q

what are some direct applications of signal detection theory?

A
  • sensory experiments –> ex: outside events detected by nervous system
  • medicine & diagnosis –> ex: have disease or not
  • electronics & telecommunication –> ex: radars
  • legal settings –> ex: someone guilty or innocent
  • alarm management –> ex: emergency occurring or not
  • inferential statistics & hypothesis testing –> ex: assume uncertainty has an effect but still form hypothesis and conduct experiment, just need to see if results are significant or not
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5
Q

what is the signal detection theory framework?

A
  • how to look at the situation in question
  • table –> 4 squares total
    - vertical = ground truth (present or absent) –> not known to us
    - horizontal = decision (yes or no) –> known through physical evidence
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6
Q

what is the purpose of the signal detection theory framework?

A

to maximize correct decisions (hit and CR) as much as possible while still acknowledging that errors may be present

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7
Q

describe the 4 outcomes of signal detection theory framework?

A
  • hit –> ground truth = present; decision = yes (correct)
  • miss –> ground truth = present; decision = no (incorrect)
  • false alarm –> ground truth = absent; decision = yes (incorrect)
  • correct reject –> ground truth = absent; decision = no (correct)
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8
Q

what was the signal detection theory framework example explained in class?

A

radiologist examining a CT scan

  • presence or absence of a tumor
  • radiologist say yes or no for tumor
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9
Q

what are the 2 main components of the decision-making process?

A

1) information acquisition

2) criterion

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10
Q

what is information acquisition?

A
  • capturing all relevant empirical evident (aka information) to try to make the best decision possible
  • uniformly leads to more correct decisions (hit & CR)
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11
Q

what will improve information acquisition

A

more evidence = less noise = better –> each decision is based on information from empirical evidence, but there can still be an attempt to get more or better evidence

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12
Q

ways to improve information acquisition and real life examples

A
  • using better measurement devices –> ex: CT scans show shape, brightness, and texture of healthy tissue vs. tumors
  • more training and practice to learn more information –> ex: doctors will know how to read a CT scan so they can use that tool to help them make their decision
  • running another test –> ex: can try a better resolution test (MRI compared to CT) or can try test from a different angle
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13
Q

what is another benefit (that relates to midterm 1 content) of acquiring more information?

A

more information = more accuracy

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14
Q

what is criterion

A
  • same information, same expertise, same test BUT different decisions –> based on individual’s criterion, not type of information given
  • leads to a trade-off bw correct decisions (hits & CR)
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15
Q

what is an example of criterion

A

doctor isn’t sure if there is a tumor or not –> some are more likely to say yes (resulting in more false alarms), or some are more likely to say no (resulting in more misses)

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16
Q

criterion also includes when decisions are made based on… ?

A

what kind of error is perceived as more or less acceptable –> ex: some doctors may think its acceptable to have more false alarms compared to misses, or vice versa

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17
Q

impact of criterion: criterion shift results in more “yes”

A

more hits (less misses) but also more false alarms (less correct rejects)

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18
Q

impact of criterion: criterion shift results in more “no”

A

more correct reject (less false alarms) but also more misses (less hits)

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19
Q

what do the 2 components of the decision process (information acquisition and criterion) impact in terms of decisions?

A

accuracy of decisions

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20
Q

___ signal and ___ information uniformly leads to more correct decisions (hits or correct rejects)

A

greater; better

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21
Q

what is the trade off of criterion changes?

A
  • they have opposing influence on the 2 incorrect decisions –> decreasing misses increases false alarms, decreasing false alarms increases misses
  • more evidence not always better
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22
Q

what is internal response?

A

the variable that forms the basis of the observer’s decision

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23
Q

what contributed to internal response?

A

internal response = signal + noise

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24
Q

internal response takes on values that ___ from one occasion to another for the ___ same stimulus (aka “signal”)

A

vary; same

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25
Q

what are frequency of occurrence curves?

A

internal response (x-axis), frequency (y-axis) –> all possible realizations about a topic and how often they occur

26
Q

what is the central tendency of a frequency of occurrence curve?

A

value that is the most likely possibility for a realization –> spread of the curve indicates that error is present

27
Q

what is the criterion of a frequency of occurrence curve?

A

indicates in what situations to say “yes” (right from criterion line) and “no” (left from criterion line)

28
Q

what is optical criterion?

A

the criterion that leads to the optimal amount of correct decisions based on the situation in question

29
Q

criterion shift

A

moving the criterion line along the frequency occurrence curve(s) –> moving right = more conservative (less yes’s); moving left = less conservative (more yes’s)

30
Q

hit + miss = ___

A

100%

31
Q

false alarm + correct reject = ___

A

100%

32
Q

what should you consider when choosing criterion?

A

which criterion optimizes accuracy

33
Q

what does a criterion shift do?

A

changes the percentage of hits and false alarms of the decision –> moving right = less hits, even less false alarms; moving left = more hits, more false (but not as many as hits)

34
Q

what is accuracy defined as in class in terms of frequency of occurrence curves?

A

how many correct answers gotten

35
Q

how do you change percentage to frequency?

A

percentage (%) / 100 = frequency –> usually a decimal

36
Q

what information do you need in order to calculate the accuracy of a situation?

A
  • frequency (often percentage) of hits and false alarms in the population
  • probability of the situation being present or absent in the population
37
Q

how can the same criterion under the same noise and signal distributions results in different total accuracy?

A

accuracy itself depends on base rates (how the cases for the situation is distributed) –> criterion in terms of accuracy isn’t optimized until you know the base rates for that event

  • base rates >50/50 (very variable) –> change criterion to be very conservative
  • base rates ~50/50 –> different criterion would be represented at particular accuracy level
  • base rates <50/50 –> different accuracy represented overall
38
Q

how can you increase your calculated accuracy?

A

by shifting criterion (more or less conservative)

39
Q

are some criterion values better than others?

A

yes, some criterion values can be better depending on…

  • the outcome you are optimizing (better to maximize accuracy or minimize cost, etc)
  • proportion of “signal present” and “signal absent” cases
40
Q

choice of criterion: sensory experiment

A
  • observer views a single interval (noise or signal embedded in noise) –> each event equally likely
  • yes/no task –> whether or not they think signal happened
41
Q

what does “populate the table” mean

A

fill our the percentages of hit/miss & FA/CR in the framework table

42
Q

calculation of accuracy

A

accuracy = [ (number-of-cases-of-signal-present)(frequency-of-hits-in-decimals) + (number-of-cases-of-signal-absent)(frequency-of-correct-rejects) ] / [total-number-of-cases]

43
Q

conservative

A

need more evidence to be able to say “yes”

44
Q

liberal

A

say “yes”, even with just a little evidence present

45
Q

what is the accuracy if you randomly tossed a coin to make your “yes/no” decision?

A

the accuracy you would get if you didn’t even consider empirical evidence –> not very good

46
Q

how to determine optical criterion of a situation?

A
  • sketch graph –> x axis = criterion (liberal to conservative); y axis = what trying to maximize
  • find the peak of the graph (is optimal criterion)
47
Q

choice of criterion: a special case

A

in the special case where signal absent (N) and signal present (S+N) are equally likely (base rates are 50/50), the optimal criterion that maximizes accuracy is the point where the two internal response curves cross (exact middle of the 2 distributions)

48
Q

choice of criterion: signal present and absent not equally likely

A
  • usually more representative of what happens in real life
  • everything (curves, % of hits/CR, etc) all same –> just base rate diff)
  • calculate accuracy the same way as before (just different number of present and absent cases –> not 50/50)
  • determine optimal criterion in same way as with 50/50 base rates
49
Q

why does the optimal criterion change for base rates that aren’t 50/50?

A

event less (or more) likely –> criterion needs to be more (or less) conservative

50
Q

why is the accuracy peak higher in base rates that aren’t 50/50?

A

have a piece of info that creates a “pedestal” at the beginning –> results in increase accuracy

51
Q

choice of criterion: optimize different parameter

A
  • same thing to determine optimal criterion
    • populate table
    • calculate what trying to maximize (ex: cost)
    • sketch graph
    • determine peak of graph
52
Q

calculation for total cost (ex of optimizing different parameter)

A

total cost = [ (number-of-cases-present)(percent-of-outcome-in-question-in-decimals)(cost-of-outcome-in-question-in-decimals) + (number-of-cases-absent)(percent-of-other-outcome-in-question-in-decimals)(cost-of-outcome-in-question-in-decimals) ]

53
Q

what was the optimal criterion for cost in the example in class?

A

between C2 and C3 –> where the costs were equal

54
Q

what does optimal criterion depend on?

A
  • base rates (likelihood of proportion of “signal present” and “signal absent” cases)
  • what outcome trying to maximize –> ex: accuracy, cost
55
Q

discriminability

A

error can be minimized by reducing overlap bw the two curves

56
Q

why is there always error (based on distribution curves)

A

because of the overlap bw the two curves

57
Q

how to reduce overlap between the two curves?

A
  • increase separation bw the curves –> get more info/evidence
  • reduce the spread of the curves
58
Q

discriminability index

A

the signal is highly discriminable from the noise when there is a large separation and a small spread

59
Q

what does the discriminability index d-prime (d’) capture?

A

both separation and spread

60
Q

calculating d’?

A

d’ = separation / spread

Psychology 277 (3 decks)

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