Classical theories propose that items are identified by serially deploying focal attention to their locations. While this accounts for set-size effects over a continuum of task difficulties, it has been suggested that parallel models can account for such effects equally well.
We compared the serial Competitive Guided Search model with a parallel model in their ability to account for RT distributions and error rates from a large visual search data-set featuring three classical search tasks: 1 a spatial configuration search 2 vs. Classical theories propose that items are identified by serially deploying focal attention to their locations.
While this accounts for set-size effects over a continuum of task difficulties, it has been suggested that parallel models can account for such effects equally well. We compared the serial Competitive Guided Search model with a parallel model in their ability to account for RT distributions and error rates from a large visual search data-set featuring three classical search tasks: 1 a spatial configuration search 2 vs. The parallel model was highly flexible in that it allowed both for a parametric range of capacity-limitation and for set-size adjustments of identification boundaries.
The serial model was found to be superior to the parallel model, even before penalizing the parallel model for its increased complexity. We discuss the implications of the results and the need for future studies to resolve the debate.
Serial vs. It is for this reason that we have already discussed visual search multiple times on this blog. To illustrate why this task is so revealing, now imagine you are looking at a display of green blobs and one red blob.
How much longer would it take you to find the red blob now, compared to when there were only 17 green distractors? The figure below shows the two displays:.
If your intuition tells you that it would take a roughly equal amount of time to find the red blob irrespective of whether there are 17 or green distractors, then that intuition is correct. Although it may appear intuitively obvious, it has had profound psychological implications.
Indeed, on first thought it would seem that the only way the speed of detection can be nearly invariant with the number of distractors is if the search proceeds in parallel: Somehow your attentional-visual system must be taking in all the information in each display at once, otherwise the number of distractors must matter. Now consider a situation in which your task is still to look for a red blob, but the distractors can be any of the colors of the rainbow or more.
What do you think might happen to your search times? Looking at the displays below might give you an idea:. Not surprisingly, under those circumstances people require considerably longer to find the target if there are more distractors.
In fact, the additional search time is often a linear function of the number of distractors, such that each additional distractor adds the same increment to total search time. On this view, each item is examined one-by-one until the target is found or until all distractors have been ruled out if no target is present. The issue is not fully settled, however: While there has been little doubt that the popout phenomenon requires a parallel account, it is less clear whether the slower search is truly serial in nature.
Specifically, the flat or shallow search slopes with popout searches and the steeper slopes with harder searches can be generated by a parallel process as well, for example by assuming that the attentional resources that can be allocated to each item in parallel decreases with the number of distractors , thereby slowing search. Researchers Rani Moran and colleagues focused on the diagnosticity of response-time RT distributions to differentiate between the different explanations. The key measure in a visual search task is the time it takes to locate the target or report its absence.
Often, researchers focus on the average of such RTs across many trials of a given type e. But that average is just one limited way of summarizing an entire distribution of RTs, as shown in the figure below :.
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