1. 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.
2. 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.
4. 1) You collect samples.
2) You set the criteria for optimal discrimination.
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24. 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.
25. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
26. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
27. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
28. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
29. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
30. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
31. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
32. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
33. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
34. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
35. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
36. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
37. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
38. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
39. 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.
41. 1) You collect samples (training data).
2) You set the criteria for optimal discrimination.
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55. 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.
56. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
57. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
58. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
59. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
60. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
61. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
62. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
63. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
64. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
65. 3) You classify new data by comparing the sensor value and
the criteria.
Criteria
66. Recognition model (=> model-free)
Unknown processes
classify
generate with criteria (c=2)
awareness as decision
Data (signal or noise)
67. Generative model (=> model-based)
Processes with unknown parameters
Noise: N(0,1); Signal: N(d’,1)
Estimate parameter
generate (d’ = 4) and
classify with criteria
Data (signal or noise)
69. SDT primer
Processes with unknown parameters
Noise: N(0,1); Signal: N(d’,1)
The sensitivity of the sensor is
characterized as d’.
d’ is independent of criteria (c).
(The correct ratio depends on c.)
70. SDT primer
Processes with unknown parameters
Noise: N(0,1); Signal: N(d’,1)
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?
72. By changing criteria
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’.
103. How do you change the criteria?
1) Confidence rating (Human study)
104. How do you change the criteria?
1) Confidence rating (Human study)
105. How do you change the criteria?
1) Confidence rating (Human study)
No Yes
106. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
107. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
2) By changing value or probability (animal study)
108. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
2) By changing value or probability (animal study)
109. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
2) By changing value or probability (animal study)
110. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
2) By changing value or probability (animal study)
111. How do you change the criteria?
1) Confidence rating (Human study)
Very sure Sure Uncertain Uncertain Sure Very sure
2) By changing value or probability (animal study)