Appearance‐Related Communication Mediates the Link Between Self‐Objectification and Health and Well‐Being Outcomes

Grounded in objectification theory, the 2 studies presented here predicted that self‐objectification is positively related to appearance‐related communication (i.e., fat talk and old talk), and, in turn, appearance‐related communication is associated with health and well‐being outcomes. Results from Study 1, which investigated only fat talk, revealed that fat talk significantly mediated the relationship between self‐objectification and body dissatisfaction, drive for thinness, bulimia, and self‐esteem. Study 2 sought to replicate the findings from Study 1, as well as extend appearance‐related communication to old talk. Fat talk was found to mediate the relationships between self‐objectification and body dissatisfaction, drive for thinness, bulimia, depression, and diet. Old talk significantly mediated the relationships between body dissatisfaction, drive for thinness, and bulimia.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.