Control Test

Comparison question tests (also called control question tests) compare examinees’ responses to relevant questions to their responses to other questions that are believed to elicit physiological reactions from innocent examinees. Relevant questions are defined as in the relevant-irrelevant test. Comparison questions ask about general undesirable acts, sometimes of the type of an event under investigation. In probable-lie comparison question tests, the instructions are designed to induce innocent people to answer in the negative, even though most are lying. Innocent examinees are expected to experience concern about these answers that shows in their physiological responses. In directed-lie tests, examinees are instructed to respond negatively and untruthfully to comparison questions.


The peak-of-tension test is concerned with questions which are asked in an easily recognized order. A guilty examinee is expected to show a pattern of responsiveness that increases as the correct alternative approaches in the question sequence and decreases when it has passed. In a known-solution peak-of-tension test, the examiner knows which alternative is the one truly connected to the incident and evaluates the examinee’s pattern of responses for evidence of involvement in the incident.


Control Test Input variable:


After getting the physiological response from the polygraph, in this case as well, we need to convert the receiving operator characteristic (ROC) curve/graph which is the analog signal, to digital signal associated with each response. The difference of consecutive peaks and lows is taken and averaged out over the total number of such differences to give I2i. Here I2i is the input variable for the ith response in the control test.


Thus, for every set of physiological responses, we now have a value that is the input variable for a particular data point.


If there are n physiological parameters, then our number of input variables becomes 2n+1 (n from the general test, n from the control test, and 1 from pre –test input).

The greater the values of the input variables, the closer will the final output is to one.


However, there is no explicit mathematical relation that defines the final output.

These input variables are then fed into the neural network to generate the output. However, as mentioned earlier, the neural network should be initially trained in order to generate a reliable output.