PSYCHOACOUSTICS: Signal Detection Theory Flashcards
what is Signal detection theory (SDT)?
Signal detection theory (SDT)is a means to measure the ability to differentiate between information-bearing patterns (signal) and random patterns that distract from the information (noise).
what kind of places is signal detection theory (SDT) used?
It has been used in many different fields, such as radar research, medical diagnosis, psychophysics, machine learning, etc.
Can you think of some examples of separating signal and noise using SDT?
masking noise in hearing tests
what is bias?
- Bias is theextent to which one response is more probable than another,
- anindex of the human’s decision-making criterion.
- Bias is independent of sensitivity.
what is sensitivity index?
Ameasure of an individual’s ability to detect signals; more specifically, a measure of sensitivity or discriminability derived from signal detection theory that is unaffected by response biases.
How can we determine whether a person’s perceptual system is sensitive to changes in stimuli?
Sensitivity to changes in stimuli can be assessed using the sensitivity index, d’ (d prime). It represents the standardized difference between the means of the Signal Present and Signal Absent distributions.
in terms of the sensitivity index, what does the size of d’ indicate?
Larger d’ means higher sensitivity.
how do we calculate the sensitivity index?
Sensitivity: 𝑑′=𝑧(hits)−𝑧(false alarms)
what is a Receiver operating characteristic (ROC) curve?
Receiver operating characteristic (ROC) curve is agraphical plot of how often false alarms (x-axis) occur versus how often hits (y-axis) occur for any level of sensitivity.
What is the benefit of a Receiver operating characteristic (ROC) curve?
. The advantage of ROC curves is that they capture all aspects of Signal Detection theory in one graph.
how do we capture the sensitivity of d’ with the Receiver operating characteristic (ROC) curve?
Sensitivity of d’ is captured by the “bow” in the curve. The more the curve bends up to the right, the better the sensitivity.
What does the Area under the curve (AUC) represent, and how is it interpreted?
The Area under the curve (AUC) is a measure used to evaluate the sensitivity of various devices, such as human observers, medical tests, or artificial intelligence classifiers. An AUC close to 1 indicates excellent performance, while an AUC of 0.5 suggests no discrimination. A range of 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is excellent, and above 0.9 is outstanding.
why is a higher AUC value (closer to 1 better)?
- Discriminatory Ability: A higher AUC value suggests that the device is better at discriminating between different classes or conditions.
- Accuracy: A higher AUC reflects greater accuracy in the device’s predictions or classifications. This means that it is more likely to correctly identify true positives and true negatives while minimizing false positives and false negatives.
- Sensitivity: A higher AUC signifies greater sensitivity, meaning the device is better at detecting relevant signals or patterns in the data. This is crucial for tasks where identifying subtle differences or anomalies is important, such as in perception studies or anomaly detection in machine learning.
- Reliability: A higher AUC implies greater reliability of the device’s performance. It indicates consistency in its ability to differentiate between different classes or conditions across multiple trials or datasets.
what are some interchangeable terminologies when discussing the signal detection theory (SDT)?
-hit
-miss
-false alarm
-correct rejection
-sensitivity
-specificity
Hit: true positive
Miss: false negative
False alarm: false positive
Correct rejection: true negative
Sensitivity: hit rate/true positive rate
Specificity: correct rejection rate/true negative rate
What is the relationship between sensitivity and specificity in medical tests?
In general, high sensitivity tests have low specificity. This means they are effective at detecting actual cases of the disease (true positives) but are also prone to a higher rate of false positives, where individuals without the disease are incorrectly identified as positive.
