Proceedings of the Annual Meeting of the Cognitive Science Society

We present an implemented computational theory of motivated inference intended to account for a variety of experimental results. People make motivated inferences when their conclusions are biased by their general motives ot goals. Our theory postulates four elements to account for such biasing. (1) A representation of the self, including attributes and motives. (2) A mechanism for evaluating the relevance of a potential conclusion to the motives of the self. (3) Mechanisms for motivated memory search to retrieve desired conceptions of the self and evidence supporting desired conclusions. (4) Inference rules with parameters that can be adjusted to encourage desired inferences and impede undesired ones.