title: Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model.
abstract: We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory.
authors.affiliation: University of California, Santa Barbara, USA.
authors.forename: Phillip J
authors.initials: PJ
authors.lastname: Ehret
authors.affiliation: University of Alabama, Tuscaloosa, USA.
authors.forename: Brian M
authors.initials: BM
authors.lastname: Monroe
authors.affiliation: University of Southern California, Los Angeles, USA read@rcf.usc.edu.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Read
mesh_terms: D001290:Attitude; D006801:Humans; D008606:Mental Processes; D008960:Models, Psychological; D055361:Multilevel Analysis; D016571:Neural Networks, Computer; D012938:Social Perception; D066269:Social Theory; D013240:Stereotyping
keywords: attitudes; automatic/implicit processes; computational modeling; evaluation; neural networks; person perception; social cognition; social neuroscience