How Do Information Processing Systems Deal with Conflicting Information? Differential Predictions for Serial, Parallel, and Coactive Models

Abstract

In this paper, we analyze how different information-processing architectures deal with conflicting information. A robust finding in psychological research is that response times are slower when processing conflicting sources of information (e.g., naming the color of the word RED when printed in green in the well-known Stroop task) than when processing congruent sources of information (e.g., naming the color of the word GREEN when printed in green). We suggest that the effect of conflicting information depends on the processing architectures and derive a new measure of information processing called the conflict contrast function, which is indicative of how different architectures perform with conflicts at different levels of salience. By varying the salience of the conflicting information source, we show that serial, parallel, and coactive information processing architectures predict qualitatively distinct conflict contrast functions. We provide new analyses of three previously collected data sets: a detection task with Stroop color-word stimuli and two categorization experiments. Our novel measure provides convergent evidence about the underlying processing architecture in the categorization tasks and surprising results in the Stroop detection task.