信号検出理論の解説 (signal detection theory, a primer)
DESCRIPTION
駒場集中講義用資料。1/10 第2回講義分です。英語です。古いバージョンは消さずにこちらに誘導。TRANSCRIPT
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SDT primer
You have a sensor (1D continuous value).You have to decide which is a signal and which
is a noise, based on the sensor value.When you classify the data as signal,
you are aware of the signal.
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SDT primer
You have a sensor (1D continuous value).You have to decide which is a signal and which
is a noise, based on the sensor value.When you classify the data as signal,
you are aware of the signal.
1) You collect samples.2) You set the criteria for optimal discrimination.3) You classify new data by comparing the
sensor value and the criteria.
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1) You collect samples.
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1) You collect samples.2) You set the criteria for optimal discrimination.
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1) You collect samples (training data).2) You set the criteria for optimal discrimination.3) You classify new data by comparing the sensor value and
the criteria.
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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SDT primer
1) You collect samples (training data).2) You set the criteria for optimal discrimination.3) You classify test data by comparing the sensor value and
the criteria.
When you classify the data as signal,you are aware of the signal.
Let’s do it again, with different data set.
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1) You collect samples (training data).
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1) You collect samples (training data).2) You set the criteria for optimal discrimination.
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1) You collect samples.2) You set the criteria for optimal discrimination.3) You classify new data by comparing the sensor value and
the criteria.
![Page 56: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/56.jpg)
3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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3) You classify new data by comparing the sensor value and the criteria.
Criteria
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Unknown processes
Recognition model (=> model-free)
Data (signal or noise)
classifywith criteria (c=2)awareness as decision
generate
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Processes with unknown parametersNoise: N(0,1); Signal: N(d’,1)
Generative model (=> model-based)
Data (signal or noise)
Estimate parameter(d’ = 4) andclassify with criteria
generate
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SDT primer
Processes with unknown parametersNoise: N(0,1); Signal: N(d’,1)
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SDT primer
The sensitivity of the sensor is characterized as d’.
d’ is independent of criteria (c).(The correct ratio depends on c.)
Processes with unknown parametersNoise: N(0,1); Signal: N(d’,1)
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SDT primer
The sensitivity of the sensor is characterized as d’.
d’ is independent of criteria (c).(The correct ratio depends on c.)
OK, but we have no such sensor.How to estimate d’ in psychophysics?
Processes with unknown parametersNoise: N(0,1); Signal: N(d’,1)
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By changing criteria
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1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).3) You obtain data set 1 (with hit, miss, FA, CR).4) Repeat 1)-3) with different criteria.5) You reconstruct the distribution of samples.6) You estimate d’.
By changing criteria
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1) You set a criterion and classify samples.
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
![Page 83: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/83.jpg)
signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
![Page 85: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/85.jpg)
signal noise
yes hit ● FA ○no miss ○ CR ●
1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).
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1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).3) You obtain data set 1 (with hit, miss, FA, CR).
![Page 87: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/87.jpg)
4) Repeat 1)-3) with different criteria.
![Page 88: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/88.jpg)
4) Repeat 1)-3) with different criteria.
![Page 89: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/89.jpg)
4) Repeat 1)-3) with different criteria.
![Page 90: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/90.jpg)
4) Repeat 1)-3) with different criteria.
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4) Repeat 1)-3) with different criteria.
![Page 92: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/92.jpg)
4) Repeat 1)-3) with different criteria.
![Page 93: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/93.jpg)
4) Repeat 1)-3) with different criteria.
![Page 94: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/94.jpg)
4) Repeat 1)-3) with different criteria.
![Page 95: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/95.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
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5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 97: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/97.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 98: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/98.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 99: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/99.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 100: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/100.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 101: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/101.jpg)
5) You reconstruct the distribution of samples.6) You estimate d’.
![Page 102: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/102.jpg)
How do you change the criteria?
![Page 103: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/103.jpg)
How do you change the criteria?1) Confidence rating (Human study)
![Page 104: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/104.jpg)
How do you change the criteria?1) Confidence rating (Human study)
![Page 105: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/105.jpg)
How do you change the criteria?1) Confidence rating (Human study)
YesNo
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How do you change the criteria?1) Confidence rating (Human study)
Very sureUncertain SureVery sure UncertainSure
![Page 107: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/107.jpg)
How do you change the criteria?1) Confidence rating (Human study)
2) By changing value or probability (animal study)
Very sureUncertain SureVery sure UncertainSure
![Page 108: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/108.jpg)
How do you change the criteria?1) Confidence rating (Human study)
2) By changing value or probability (animal study)
Very sureUncertain SureVery sure UncertainSure
![Page 109: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/109.jpg)
How do you change the criteria?1) Confidence rating (Human study)
2) By changing value or probability (animal study)
Very sureUncertain SureVery sure UncertainSure
![Page 110: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/110.jpg)
How do you change the criteria?1) Confidence rating (Human study)
2) By changing value or probability (animal study)
Very sureUncertain SureVery sure UncertainSure
![Page 111: 信号検出理論の解説 (Signal detection theory, a primer)](https://reader033.vdocuments.site/reader033/viewer/2022052413/559b6a301a28ab32188b4623/html5/thumbnails/111.jpg)
How do you change the criteria?1) Confidence rating (Human study)
2) By changing value or probability (animal study)
Very sureUncertain SureVery sure UncertainSure