Eyewitness identification: information gain from incriminating and exonerating behaviors.

An information-gain approach to the analysis and interpretation of eyewitness identification data is described. The information-gain analysis is grounded in Bayesian statistics, permitting the important role of prior probabilities to be explored. This approach also forces a more complete treatment of the data and reveals important patterns that have escaped previous attention in the eyewitness identification literature. Particularly important is the ability of information-gain analyses to make salient the exonerating value of eyewitness behaviors rather than just their incriminating value. Analyses of sample data sets show how the exonerating value of filler identifications and "not there" responses can actually exceed the incriminating value of identifications of the suspect at certain points in the distribution of prior probabilities.