The role of crowding in parallel search: Peripheral pooling is not responsible for logarithmic efficiency in parallel search

Recent results from our laboratory showed that, in fixed-target parallel search tasks, reaction times increase in a logarithmic fashion with set size, and the slope of this logarithmic function is modulated by lure-target similarity. These results were interpreted as being consistent with a processing architecture where early vision (stage one) processes elements in the display in exhaustive fashion with unlimited capacity and with a limitation in resolution. Here, we evaluate the contribution of crowding to our recent logarithmic search slope findings, considering the possibility that peripheral pooling of features (as observed in crowding) may be responsible for logarithmic efficiency. Factors known to affect the strength of crowding were varied, specifically: item spacing and similarity. The results from three experiments converge on the same pattern of results: reaction times increased logarithmically with set size and were modulated by lure-target similarity even when crowding was minimized within displays through an inter-item spacing manipulation. Furthermore, we found logarithmic search efficiencies were overall improved in displays where crowding was minimized compared to displays where crowding was possible. The findings from these three experiments suggest logarithmic efficiency in efficient search is not the result peripheral pooling of features. That said, the presence of crowding does tend to reduce search efficiency, even in “pop-out” search situations.