Unzicker A, Jüttner M, Rentschler I
Institute of Medical Psychology, University of Munich, Germany.
Vision Res. 1998 Aug;38(15-16):2289-305. doi: 10.1016/s0042-6989(97)00396-9.
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.
对来自认知心理学、视觉心理物理学和联结主义的七种人类视觉识别模型进行了比较。这些模型用于预测通过对参数化灰度模式（复合伽柏信号）进行监督学习获得的心理物理学分类数据。应用了四组学习模式以及中央凹和中央凹外的观察条件。通过比较观察数据和预测数据的均方根偏差以及通过多维缩放进行信号重建来确定模型性能。结果表明，分类的心理物理学理论需要一个基于物理信号描述和认知偏差的相似性概念。后者在中央凹识别中不太明显，在中央凹识别中所有七个模型的表现几乎同样好，但在中央凹外识别中很重要。最能适应刺激和观察者依赖性的虚拟原型模型（伦施勒等人，《视觉研究》34卷，669 - 687页，1994年）具有优势。在计算效率方面，一个超BF模型（波吉奥和吉罗西，《科学》247卷，978页，1990年）速度快得多，而广义信号检测模型比平均速度慢得多。
