Predicting experimental similarity ratings and recognition rates for individual natural stimuli with the NIM model

In earlier work, we proposed a recognition memory model that operates directly on digitized natural images. The model is called the Natural Input Memory (NIM) model. When presented with a natural image, the NIM model employs a biologically-informed perceptual pre-processing method that translates the image into a similarity-space representation. In this paper, the NIM model is validated on individual natural stimuli (i.e., images of faces) in two tasks: (1) a similarityrating task and (2) a recognition task. The results obtained with the NIM model are compared with the results of corresponding behavioral experiments. The similarity structure of the face images that is reflected in the similarity space forms the basis for the comparison. The results reveal that the NIM model’s similarity ratings and recognition rates for individual images correlate well with those obtained in the behavioral experiments. We conclude that the NIM model successfully simulates similarity ratings and recognition performance for individual natural stimuli.