White, C. N., Brown, S., and Ratcliff, R.
J Exp Psychol Hum Percept Perform, 38:489–497, 2012
DOI, Google Scholar
Abstract
Two Bayesian observer models were recently proposed to account for data from the Eriksen flanker task, in which flanking items interfere with processing of a central target. One model assumes that interference stems from a perceptual bias to process nearby items as if they are compatible, and the other assumes that the interference is due to spatial uncertainty in the visual system (Yu, Dayan, & Cohen, 2009). Both models were shown to produce one aspect of the empirical data, the below-chance dip in accuracy for fast responses to incongruent trials. However, the models had not been fit to the full set of behavioral data from the flanker task, nor had they been contrasted with other models. The present study demonstrates that neither model can account for the behavioral data as well as a comparison spotlight-diffusion model. Both observer models missed key aspects of the data, challenging the validity of their underlying mechanisms. Analysis of a new hybrid model showed that the shortcomings of the observer models stem from their assumptions about visual processing, not the use of a Bayesian decision process.
Review
This is a response to Yu2009 in which the authors show that Yu et al.'s main Bayesian models cannot account for the full data of an Eriksen flanker task. In particular, Yu et al.'s models predict a far too high overall error rate with the suggested parameter settings that reproduce the inital drop of accuracy below chance level for very fast responses. The argument put forward by White et al. is that the mechanisms used in Yu et al.'s models to overcome initial, flanker-induced biases is too slow, i.e., the probabilistic evidence accumulation implemented by the models is influenced by the flankers for too long. White et al's shrinking spotlight models do not have such a problem, mostly because the speed with which flankers loose influence is fitted to the data. The argument seems compelling, but I would like to understand better why it takes so long in the Bayesian model to overcome flanker influence and whether there are other ways of speeding this up than the one suggested by White et al..