I have calculated D-prime measurements for for a memory performance task. Subjects viewed images that were either old or new and had to indicate their response via a button press. Thus, their response was either correct or wrong. From the subjects responses, I calculated their Hit rates (correct/total old) and FA rates (incorrect/total new) and calculated the d-prime values (z(H)-z(FA)) in R.

Now, when I look at my d-prime scores, they seem to be normally distributed around zero:

Normally distributed D-prime

From what I've gathered from the literature, this should not be the case. D-prime values should be above zero and negative values could represent sampling errors or response confusion e.g. Stanislaw & Todorov (1999) https://www.researchgate.net/file.PostFileLoader.html?id=536ce28fd5a3f2b41b8b46c5&assetKey=AS%3A273596423835649%401442241878760

or in this interpretation: http://www.csic.cornell.edu/201/signal_detection/#negative_d_prime.

Now, I'm wondering if I can assume that in some cases my subjects were just confused about which button represented which response? For example, those samples around -3 really seem to indicate a large sensitivity to the signal, however in complete reverse. Could I use absolute values of these extreme negative d-prime samples to still include them in my sample and further interpretation?

EDIT: I had another look at the data, it seems like this effect does not stem from the errors subjects have made. The max error percentage was 60%, with an average of 42%. Perhaps it's the way I calculated the z-scores? I used the scale function in R, but I don't see why that would be a problem?


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