FA no miss ○ CR ● The document describes the process of signal detection theory (SDT). It involves: 1) Collecting training data samples from known signals and noises. 2) Setting criteria for optimal discrimination between signals and noises. 3) Classifying new test data by comparing the sensor values to the discrimination criteria. The goal is to accurately detect signals while minimizing incorrect classifications of noise as signals.

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.

3. 1) You collect samples.

4. 1) You collect samples.
2) You set the criteria for optimal discrimination.

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.

40. 1) You collect samples (training data).

41. 1) You collect samples (training data).
2) You set the criteria for optimal discrimination.

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)

68. SDT primer
Processes with unknown parameters
Noise: N(0,1); Signal: N(d’,1)

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?

71. By changing criteria

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’.

73. 1) You set a criterion and classify samples.

74. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

75. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

76. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

77. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

78. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

79. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

80. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

81. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

82. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

83. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

84. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

85. signal noise
yes hit ● ○
FA
no miss ○ CR ●
1) You set a criterion and classify samples.
2) You get the feedback (correct or incorrect).

86. 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).

87. 4) Repeat 1)-3) with different criteria.

88. 4) Repeat 1)-3) with different criteria.

89. 4) Repeat 1)-3) with different criteria.

90. 4) Repeat 1)-3) with different criteria.

91. 4) Repeat 1)-3) with different criteria.

92. 4) Repeat 1)-3) with different criteria.

93. 4) Repeat 1)-3) with different criteria.

94. 4) Repeat 1)-3) with different criteria.

95. 5) You reconstruct the distribution of samples.
6) You estimate d’.

96. 5) You reconstruct the distribution of samples.
6) You estimate d’.

97. 5) You reconstruct the distribution of samples.
6) You estimate d’.

98. 5) You reconstruct the distribution of samples.
6) You estimate d’.

99. 5) You reconstruct the distribution of samples.
6) You estimate d’.

100. 5) You reconstruct the distribution of samples.
6) You estimate d’.

101. 5) You reconstruct the distribution of samples.
6) You estimate d’.

102. How do you change the criteria?

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)