In signal detection theory d-prime index is used as sensitivity index, but the case is different combinations of hit rates and false alarm rates can lead to the same value d-prime index, which means d-prime captures only a part of signal detection space. So additional index known as RESPONSE BIAS is needed showing hit/miss tendecy or yes/no tendency.
In other words response bias determines whether someone tends to respond YES or NO more often. Responce bias is orthogonal or unrelated to d-prime because very different d-primes can be associated with the same bias.
The formula for response bias:
BIAS = – ( z(FA) - z(H) ) / 2
where z(H) is z-score for hits and z(FA) is z-score for false alarms.
hit rate H: proportion of YES trials to which subject responded YES = P("yes" | YES)
false alarm rate F: proportion of NO trials to which subject responded YES = P("yes" | NO)
BIAS=0 is considered as NO BIAS.
BIAS>0 is considered as tendency to say NO.
BIAS<0 is considered as tendency to say YES.
Simple example of d-prime calculation:
import numpy as np import matplotlib.pyplot as plt import scipy.stats as stats # hit rates and false alarm rates hitP = 22/30 faP = 3/30 # z-scores hitZ = stats.norm.ppf(hitP) faZ = stats.norm.ppf(faP) # RESPONSE BIAS respBias = -(hitZ+faZ)/2 print(respBias)