Conference Paper

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

Conference: BNAIC 2005 - Proceedings of the Seventeenth Belgium-Netherlands Conference on Artificial Intelligence, Brussels, Belgium, October 17-18, 2005
Source: DBLP


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 similarity- rating task and (2) a recognition task. The results obtained with the NIM model are compared with the results of corre- sponding 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 N IM model's similarity ratings and recognition rates for individual images correlate well with those obtained in the behavioral ex- periments. We conclude that the NIM model successfully sim- ulates similarity ratings and recognition performance for indi- vidual natural stimuli.

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