Purchase this article with an account.
Emmanuelle Boloix; A multinomial processing tree model of change blindness and change detection. Journal of Vision 2008;8(6):744. doi: https://doi.org/10.1167/8.6.744.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
In most change blindness studies, the failure to report changes is attributed to either a representational failure (the pre-change scene is not represented in memory) or to a comparison failure (pre- and post-change scenes are not compared). Here, I propose a multinomial processing-tree (MPT) model, which determines the relative contributions of representational and comparison failures to change blindness, as well as the level of detail of the scene representation. MPT models are statistical models used to measure latent cognitive processes from observable raw data. Cognitive processes are represented as model's parameters, their respective weights can be assessed, and the fit of the model to the empirical data can be evaluated via goodness-of-fit tests (Batchelder & Riefer, 1999). The MPT model I propose assumes that visual information is either represented in memory or non-represented, and, if represented, that visual representation can be compared to the currently-displayed view or not. Here, I ran one change blindness experiment to test the model's predictions. Observers (N=144) were shown a map of a virtual village and had to perform a 7-stage route in the map. Changes occurred on task-relevant or task-irrelevant objects. Observers were instructed to complete the route as quickly as possible, and to report perceived changes. Results show that differences in change detection performances are due to the quality of representation in memory, which depends on object's relevance for the task, and not to a comparison process (the model's weighting of the memory representation parameter varies from 19% to 82%, whereas the weighting of the memory comparison parameters remains stable in the range from 28% to 38%). Moreover, the model showed that only a task-relevant representation theory could fit the data. Implications for several theories of scene representation (Hollinworth, 2004; Irwin & Zelinski, 2002; Rensink, 2000; Triesch et al, 2003) are discussed.
This PDF is available to Subscribers Only