Serial versus parallel search: A model comparison approach based on reaction time distributions

For 50 years or so, visual search experiments have been used to examine how humans find behaviourally relevant objects in complex visual scenes. For the same length of time, there has been a dispute over whether this search is performed in a serial or parallel fashion. In this paper, we approach this dispute by numerically fitting a serial search model and a parallel search model to reaction time (RT) distributions from three visual search experiments (feature search, conjunction search, spatial configuration search). In order to do so, we used a free-likelihood method based on a novel kernel density estimator (KDE).

The serial search model was the Competitive Guided Search (CGS) model by Moran et al. [(2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), 24–24.]. We were able to replicate the ability of CGS to model RT distributions from visual search experiments, and demonstrated that CGS generalizes well to new data. The parallel model was based on the biased-competition theory and utilized a very simple biologically-plausible winner-take-all (WTA) mechanism from Heinke and Humphreys’s [(2003). Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM). Psychological Review, 110(1), 29–87.]. With this mechanism, SAIM has been able to explain a broad range of attentional phenomena but it was not specifically designed to model RT distributions in visual search. Nevertheless, the WTA was able to reproduce these distributions.

However, a direct comparison of the two models suggested that the serial CGS is slightly better equipped to explain the RT distributions than the WTA mechanism. The CGS’s success was mainly down the usage of the Wald distribution which was specifically designed to model visual search. Future WTA versions will have to find a biologically plausible mechanism to reproduce such a RT distribution. Finally, both models suffered from a failure to generalize across all display sizes. From these comparisons, we developed suggestions for improving the models and motivated empirical studies to devise a stronger test for the two types of searches.