It can be seen that as the set size increased, search times slowed considerably (and errors, in the bottom panel, increased although that need not concern us here). The data are indicated by black asterisks, and there are 5 observations for each set size: each observation represents one “bin” of the RT distribution. It is clear that the set size effect mainly affects the tail of the distribution (i.e., the top 2-3 points) rather than its leading edge (i.e., the bottom point).
The figure also shows the ability of the two models under consideration to explain the data: The parallel model is represented by red crosses and the serial model (called CGS for “Competitive Guided Search”) by blue diamonds. As a first approximation, both models tend to do well: they both capture the basic set size effect and the fact that this is mainly due to the stretching of the tail of the RT distribution.
Closer inspection, however, reveals that the parallel model fell a little short of accounting for the data: Note how the red crosses are dispersed less than the data for set size 18, but more than the data for set size 3. Overall, if the models are compared quantitatively, so even tiny deviations between data and predictions are added up into the comparison, then the parallel model’s success was significantly poorer than that of the serial model.
Moran and colleagues compared the models’ ability by applying them to several additional classic visual-search results. In all cases, they found that the parallel model performed less well than its serial competitor. Surprisingly, the particular serial model used here—the CGS—outperformed the parallel model even for the pop-out task! These results contradict the intuition for above that the constant search time (when the number of items increases) in the pop-out search task can only be explained by parallel attentional processing. As the authors explain, a serial attention model can account for the constant search time if there is attentional “guidance” towards the target—that is, the target is so much more salient than the distractors that attention guides search to it first.
An issue as important as the debate between parallel and serial models of visual search cannot be resolved by a single study. Nonetheless, it is highly informative that a careful quantitative comparison has consistently favored a serial-search model over its parallel counterpart. This is one instance in which a purely verbal interpretation of search slopes has been found to be borne out by quantitative modeling. Given how close the competition was in quantitative terms, however, the work by Moran and colleagues underscores the need for quantitative modeling in cognitive psychology
: Some issues ought not to be resolved by verbal theorizing alone, because our human cognitive apparatus frequently delivers incorrect intuitions about the behavior of models
After all, if Boeing and Airbus don’t design jet liners on the basis of verbal hypotheses alone
, and if the human mind is at least as complex as an A380, why would we settle for anything less than a quantitative model?
Article focused on in this post:
Moran, R., Zehetleitner, M., Liesefeld, H. R., Müller, H. J., & Usher, M. (2015). Serial vs. parallel models of attention in visual search: accounting for benchmark RT-distributions. Psychonomic Bulletin & Review. DOI: 10.3758/s13423-015-0978-1