A Connectionist Model of Attitude Strength and Change
نویسنده
چکیده
A localist constraint satisfaction neural network model is presented to account for a broad collection of attitude and attitude change phenomena. Activations and weights among units in the recurrent network are used to vary the structure and properties of the attitude and a persuasive message. The model uses Hebbian learning to update weights, allowing it to represent long term changes in attitude as well as temporary construction of attitudes. Phenomena modeled include attitude strength, motivated reasoning, and heuristic vs. systematic persuasion. The same set of simple mechanisms is used to model all the phenomena, which allows the model to offer a parsimonious theoretical account of much of the literature on attitude change. The model also provides a specified structural description of attitudes that could lead to new predictions and a more complete understanding of the construct and its effects.
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