Oberwiesenfeld 上草原

Schlosberg H.認為，人類的表情可以跟生氣、厭惡、輕蔑、笑、驚訝、恐懼等六種情緒可相互對應。這六者的關係可以用一個六角型來表示。然而，這並不是完全的一對一，有時候某種表情也可能是表達相鄰的情感。這其中，相互比鄰的兩者，表示這兩種表情容易混淆。「厭惡」與「生氣」、「驚訝」與「恐懼」的確是很難判別。按照這個法則來看的話，「笑」跟「輕蔑」或「驚訝」竟然有類似的地方。 所以，看到某人在笑的時候，會是輕蔑或是驚訝或是真的是單純的笑？


最近有空閒時在唸一本關於心理學的大眾讀物，上段的文字大致上從裡頭擷取出，而越讀越有興趣，就不小心發揮了自己被訓練出來的追根究底老毛病而繼續從學術網路上往下找paper來唸。


EMPATH: A Neural Network that Categorizes Facial Expressions Journal of Cognitive Neuroscience. 2002;14:1158-1173

Matthew N. Dailey and Garrison W. Cottrell, University of California, San Diego

Curtis Padgett, Jet Propulsion Laboratory

Ralph Adolphs, University of Iowa

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain.