Similarity-based models of human visual recognition

Seven models of human visual recognition from cognitive psychology, visual psychophysics and connectionism were compared. They were used to predict psychophysical classification data obtained via supervised learning with parametrised grey-level patterns (compound Gabor signals). Four sets of learning patterns, as well as foveal and extrafoveal viewing conditions, were applied. Model performance was determined by comparing observed and predicted data with respect to root mean square deviation and to signal reconstruction via multidimensional scaling. Results show that a psychophysical theory of classification requires a similarity concept that is based both on physical signal description and on cognitive bias. The latter is less pronounced in foveal recognition, where all seven models performed almost equally well, but matters in extrafoveal recognition. Virtual prototype models (Rentschler et al. (1994), Vision Research 34, 669-687), which best accommodate stimulus- and observer-dependencies, are then of advantage. Concerning computational efficiency, a hyperBF model (Poggio and Girosi (1990), Science 247, 978) was much faster, and generalized signal detection models were much slower than the average.