| Summary: | Two connectionist models are developed that predict humans' categorization of facial expressions of emotion and their judgements of similarity between two facial expressions. For each stimulus, the models predict the subjects' judgement, the entropy of the response, and the mean response time (RT). Both models involve a connectionist component which predicts the response probabilities and a response generator which predicts the mean RT. The input to the categorization model is a preprocessed picture of a facial expression, while the hidden unit representations generated by the first model for two facial expressions constitute the input of the similarity model. The data collected on 45 subjects in a single-session experiment involving a categorization and a similarity task provided the target outputs to train both models. Two response generators are tested. The first, called the threshold model , is a linear integrator with threshold inspired from Lacouture and Marley's (1991) model. The second, called the channel model, constitutes a new approach which assumes a linear relationship between entropy of the response and mean RT. It is inspired by Lachman's (1973) interpretation of Shannon's (1948) entropy equation. The categorization model explains 50% of the variance of mean RT for the training set. It yields an almost perfect categorization of the pure emotional stimuli of the training set and is about 70% correct on the generalization set. A two-dimensional representation of emotions in the hidden unit space reproduces most of the properties of emotional spaces found by multidimensional scaling in this study as well as in other studies (e.g., Alvarado, 1996). The similarity model explains 53% of the variance of mean similarity judgements; it provides a good account of subjects' mean RT; and it even predicts an interesting bow effect that was found in subjects' data.
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