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Signal Detection Theory October 10, 2013

Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

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Page 1: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Signal Detection Theory

October 10, 2013

Page 2: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Some Psychometrics!• Response data from a perception experiment is usually organized in the form of a confusion matrix.

• Data from Peterson & Barney (1952)

• Each row corresponds to the stimulus category

• Each column corresponds to the response category

Page 3: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Detection• In a detection task (as opposed to an identification task), listeners are asked to determine whether or not a signal was present in a stimulus.

• For example--do the following clips contain release bursts?

• Potential response categories:

SignalResponse

Hit: Present (in stimulus) “Present”

Miss: Present “Absent”

False Alarm: Absent “Present”

Correct Rejection: Absent “Absent”

Page 4: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Confusion, Simplified• For a detection task, the confusion matrix boils down to just two stimulus types and response options…

(Response Options)

Present Absent

Present Hit Miss

Absent False Alarm Correct Rejection

(Stimulus Types)

• Notice that a bias towards “present” responses will increase totals of both hits and false alarms.

• Likewise, a bias towards “absent” responses will increase the number of both misses and correct rejections.

Page 5: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Canned Examples• From the text: in session 1, listeners are rewarded for “hits”. The resultant confusion matrix looks like this:

Present Absent

Present 82 18

Absent 46 54

• The “correct” responses (in bold) = 82 + 54 = 136

Page 6: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Canned Examples• In session 2, the listeners are rewarded for “correct rejections”…

Present Absent

Present 55 45

Absent 19 81

• The “correct” responses (in bold) = 55+ 81 = 136

• Moral of the story: simply counting the number of “correct responses” does not satisfactorily tell you what the listener is doing…

• And response bias is not determined by what they can or cannot perceive in the signal.

Page 7: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Detection Theory• Signal Detection Theory: a “parametric” model that predicts when and why listeners respond with each of the four different response types in a detection task.

• “Parametric” = response proportions are derived from underlying parameters

• Assumption #1: listeners base response decisions on the amount of evidence they perceive in the stimulus for the presence of a signal.

• Evidence = gradient variable.

perceptual evidence

Page 8: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

The Criterion• Assumption #2: listeners respond positively when the amount of perceptual evidence exceeds some internal criterion measure.

perceptual evidence

criterion ()

“present” responses“absent” responses

• evidence > criterion “present” response

• evidence < criterion “absent” response

Page 9: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

The Distribution• Assumption #3: the amount of perceived evidence for a particular stimulus includes random variation…

• and the variation is distributed normally.

perceptual evidence

Frequency

The categorization of a particular stimulus will vary between trials.

Page 10: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Normal Facts• The normal distribution is defined by two parameters:

• mean (= “average”) ()

• standard deviation ()

• The mean = center point of values in the distribution

• The standard deviation = “spread” of values around the mean in the distribution.

standard deviation standard deviation

Page 11: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Comparisons• Assumption #4: responses to both “absent” and “present” stimuli in a detection task will be distributed normally.

• Generally speaking:

• the mean of the “present” distribution will be higher on the evidence scale than that of the “absent” distribution.

• Assumption #5: both “absent” and “present” distributions will have the same standard deviation.

• (This is the simplest version of the model.)

Page 12: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Interpretationcorrect rejections false alarms

misses hitscriterion

Important: the criterion level is the same for both types of stimuli…

…but the means of the two distributions differ

Page 13: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Sensitivity• The distance (on the perceptual evidence scale)

between the means of the distributions reflects the listener’s sensitivity to the distinction.

• Q: How can we estimate this distance?

• A: We measure the distance of the criterion from each mean.

• We can use z-scores to standardize our distance measures!

• In normal distributions, this distance:

• determines the proportion of responses on either side of the criterion

Page 14: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Z-Scores

• Example 1: criterion at the mean

• Z-score = 0

• 50% hits, 50% misses

HitsMisses

Page 15: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Z-Scores

• Example 2: criterion one standard deviation below the mean

• Z-score = -1

• 84.1% hits, 15.9% misses

HitsMisses

Page 16: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Z-Scores

• Note: P(Hits) = 1-P(Misses)

• z(P(Hits)) = z(1-P(Misses)) = -z(P(Misses))

• In this case: z(84.1) = -z(15.9) = 1

HitsMisses

Page 17: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

D-Prime• D-prime (d’) is a measure of sensitivity.

• = perceptual distance between the means of the “present” and “absent” distributions.

• This perceptual distance is expressed in terms of z-scores.

d’sn

Page 18: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

D-Prime

d’sn

Hits

• d’ combines the z-score for the percentage of hits…

Page 19: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

D-Prime

z(P(H))sn

Hits

• d’ combines the z-score for the percentage of hits…

• with the z-score for the percentage of false alarms.

False Alarms

-z(P(FA))

• d’ = z(P(H)) - z(P(FA))

Page 20: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

D-Prime Examples1. Present Absent

Present 82 18

Absent 46 54

d’ = z(P(H)) - z(P(FA)) = z(.82) - z(.46) = .915 - (-.1) = 1.015

2. Present Absent

Present 55 45

Absent 19 81

d’ = z(P(H)) - z(P(FA)) = z(.55) - z(.19) = .125 - (-.878) = 1.003

• Note: there is no absolute meaning to the value of d-prime

• Also: NORMSINV() is the Excel function that converts percentages to z-scores. (qnorm() works in R)

Page 21: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Near Zero Correction• Note: the z-score is undefined at 100% and 0%.

• Fix: replace perfect scores with a minimal deviation from the limit (.5% or 99.5%)

• Present Absent

Present 100 0

Absent 72 28

d’ = z(P(H)) - z(P(FA)) = z(.995) - z(.72) = 2.57 - .58 = 1.99

Page 22: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Near Zero Correction• Also note that we do not normally deal with sets of responses that total to 100 in our experimental data!

• Here’s another example of the “fix” in which perfect scores are replaced with scores that are just half a response unit above or below the minimum and maximum scores, respectively.

• Present Absent

Present 20 0

Absent 6 14

• Replace 20 with 19.5, so P(H) = 19.5/20 = .975

d’ = z(P(H)) - z(P(FA)) = z(.975) - z(.3) = 1.96 - (-.52) = 2.48

Page 23: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Calculating Bias• An unbiased criterion would fall halfway between the means of both distributions.

• No bias (λu): P (Hits) = P (Correct Rejections)

• Bias (λb): P (Hits) != P (Correct Rejections)

u

b

Page 24: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Calculating Bias• Bias = distance (in z-scores) between the ideal criterion and the actual criterion

• Bias () = -1/2 * (z(P(H)) + z(P(FA)))

u

b

Page 25: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

For Instance Let’s say: d’ = 2

• An unbiased criterion would be one standard deviation from both means…

z(P(H)) = 1z(P(FA)) = -1

• z(P(H)) = 1 P(H) = 84.1%

• z(P(FA)) = -1 P(FA) = 15.9%

Bias () = -1/2 * (z(P(H)) + z(P(FA)))

•= -1/2 * (1 + (-1)) = -1/2 * (0) = 0

Page 26: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Wink Wink, Nudge Nudge Now let’s move the criterion over 1/2 a standard deviation…

z(P(H)) = 1.5z(P(FA)) = -.5

• z(P(H)) = 1.5 P(H) = 93.3% (cf. 84.1%)

• z(P(FA)) = -.5 P(FA) = 30.9% (cf. 15.9%)

• Bias () = -1/2 * (z(P(H)) + z(P(FA)))

= -1/2 * (1.5 + (-.5)) = -1/2 * (1) = -.5

Page 27: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Calculating Bias: Examples1. Present Absent

Present 82 18

Absent 46 54

= -1/2 * (z(P(H)) + z(P(FA)) = -1/2 * (z(.82) + z(.46)) = -1/2 * (.915 + (-.1)) = -.407

2. Present Absent

Present 55 45

Absent 19 81

= -1/2 * (z(P(H)) + z(P(FA)) = -1/2 * (z(.55) + z(.19)) = -1/2 * (.125 + (-.878)) = .376

• The higher the criterion is set, the more positive this number will be.

Page 28: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

Peach Colo(u)rs

• Listeners could replay stimuli as many times as they liked.

• Order of pictures was counterbalanced across presentations.

Page 29: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

• Target identification significantly better than chance (p < .001)

• Difference in accuracy between IDS and ADS utterances was nearly signification (p = .056).

Page 30: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

• In terms of sensitivity (d’):

• Sensitivity significantly greater in IDS utterances! (p = .003)

• The properties of Infant-directed speech provide cues to syntactic disambiguation.

Page 31: Signal Detection Theory October 10, 2013 Some Psychometrics! Response data from a perception experiment is usually organized in the form of a confusion

• In terms of bias ():

• IDS utterances induced a significantly greater bias towards NV responses (p = .032)

• Why? Perhaps duration differences between utterance types provide a clue…