A new and unique prediction for cue-search in a parallel-constraint satisfaction network model: The attraction search effect

Abstract

A common assumption of many established models for decision making is that information is searched according to some pre-specified search rule. While the content of the information influences the termination of search, usually specified as a stopping rule, the direction of search is viewed as being independent of the valence of the retrieved information. We propose an extension to the parallel constraint satisfaction network model (iCodes: integrated coherence-based decision and search), which assumes—in contrast to pre-specified search rules—that the valence of available information influences search of concealed information. Specifically, the model predicts an attraction search effect in that information search is directed towards the more attractive alternative given the available information. In three studies with participants choosing between two options based on partially revealed probabilistic information, the attraction search effect was consistently observed for environments with varying costs for information search although the magnitude of the effect decreased with decreasing monetary search costs. We also find the effect in reanalyses of five published studies. With iCodes, we propose a fully specified formal model and discuss implications for theory development within competing modeling frameworks.