====== Title ======
==== Authors ====
Jeremy M. Wolfe, George A. Alvarez, Ruth Rosenholtz, Yoana L. Kuzmova, Ashley M. Sherman
----

===== Abstract =====

How efficient is visual search in real scenes? In
searches for targets among arrays of randomly placed
distractors, efficiency is often indexed by the slope of the
reaction time (RT) × Set Size function. However, it may be
impossible to define set size for real scenes. As an
approximation, we hand-labeled 100 indoor scenes and
used the number of labeled regions as a surrogate for set
size. In Experiment 1, observers searched for named objects
(a chair, bowl, etc.). With set size defined as the number of
labeled regions, search was very efficient (~5 ms/item).
When we controlled for a possible guessing strategy in
Experiment 2, slopes increased somewhat (~15 ms/item),
but they were much shallower than search for a random
object among other distinctive objects outside of a scene
setting (Exp. 3: ~40 ms/item). In Experiments 4–6,
observers searched repeatedly through the same scene for
different objects. Increased familiarity with scenes had
modest effects on RTs, while repetition of target items had
large effects (>500 ms). We propose that visual search in
scenes is efficient because scene-specific forms of attentional
guidance can eliminate most regions from the “functional set
size” of items that could possibly be the target.

===== Information =====

---- dataentry pubitem ----
title        : Visual search for arbitrary objects in real scenes
authors      : Jeremy M. Wolfe, George A. Alvarez, Ruth Rosenholtz, Yoana L. Kuzmova, Ashley M. Sherman
citation     : Attention, Perception, & Psychophysics, doi: 10.3758/s13414-011-0153-3
shortcite    : AP&P
year         : 2011
created_dt   : 2011-06-14
summary_page : publications:searchapp2011
keyword_tags : rosenholtz, search
pdf_url      : http://www.springerlink.com/content/k403p7t88pnt5730/fulltext.pdf
type         : publication
---