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.

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.

1) You collect samples.

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

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.

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

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.

1) You collect samples (training data).

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

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) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

3) You classify new data by comparing the sensor value and the criteria.

Criteria

Unknown processes

Recognition model (=> model-free)

Data (signal or noise)

classifywith criteria (c=2)awareness as decision

generate

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

SDT primer

Processes with unknown parametersNoise: N(0,1); Signal: N(d’,1)

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)

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)

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’.

By changing criteria

1) You set a criterion and classify samples.

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

signal noise

yes hit ● FA ○no miss ○ CR ●

1) You set a criterion and classify samples.2) You get the feedback (correct or incorrect).

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

How do you change the criteria?

How do you change the criteria?1) Confidence rating (Human study)

How do you change the criteria?1) Confidence rating (Human study)

How do you change the criteria?1) Confidence rating (Human study)

YesNo

How do you change the criteria?1) Confidence rating (Human study)

Very sureUncertain SureVery sure UncertainSure

How do you change the criteria?1) Confidence rating (Human study)

2) By changing value or probability (animal study)

Very sureUncertain SureVery sure UncertainSure

How do you change the criteria?1) Confidence rating (Human study)

2) By changing value or probability (animal study)

Very sureUncertain SureVery sure UncertainSure

How do you change the criteria?1) Confidence rating (Human study)

2) By changing value or probability (animal study)

Very sureUncertain SureVery sure UncertainSure

How do you change the criteria?1) Confidence rating (Human study)

2) By changing value or probability (animal study)

Very sureUncertain SureVery sure UncertainSure

How do you change the criteria?1) Confidence rating (Human study)

2) By changing value or probability (animal study)

Very sureUncertain SureVery sure UncertainSure