Guided Search (GS) has been an effective computational model for studying visual search involving rudimentary stimuli, such as lines of various colors and orientations. The visual qualities of such stimuli can often be operationalized based on wavelength of light or degree of rotation, but this kind of quantification is not as straightforward for more complex visual stimuli, such as images of real-world objects. In the current investigation, we propose a method to use a multidimensional scaling (MDS) analysis of search stimuli as input into a modified GS model. To test the effectiveness of the proposed GS “plug-in,” we conducted two experiments in which participants searched for novel objects among distractors that varied in how similar they were to the target and to each other (defined by distance in MDS space). Search was more efficient when distractors were dissimilar from the target compared to when they were similar to the target. Modeled data from the modified GS model effectively captured trends in target-present responses. This analysis supports the use of MDS to model similarity and suggests that it may be a viable tool for extending computational uses of GS, as well as for further investigating target-absent responses within the GS framework.
AizenmanA. M.EhingerK. A.WickF. A.MichelettoR.ParkJ.JurgensenL.WolfeJ. M. (2022). Hiding the Rabbit: Using a genetic algorithm to investigate shape guidance in visual search. Journal of Vision, 22(1), Article 7.
2.
AlexanderR. G.ZelinskyG. J. (2011). Visual similarity effects in categorical search. Journal of Vision, 11(8), Article 9.
3.
AlexanderR. G.SchmidtJ.ZelinskyG. J. (2014). Are summary statistics enough? Evidence for the importance of shape in guiding visual search. Visual Cognition, 22(3–4), 595–609.
4.
BeckerS. I. (2010). The role of target-distractor relationships in guiding attention and the eyes in visual search. Journal of Experimental Psychology: General, 139(2), 247–265.
5.
BeckerS. I. (2011). Determinants of dwell time in visual search: Similarity or perceptual difficulty?PloS One, 6(3), e17740.
6.
BorgI.GroenenP. J. (2007). Modern multidimensional scaling: Theory and applications. Springer Science & Business Media.
7.
BuettiS.CroninD. A.MadisonA. M.WangZ.LlerasA. (2016). Towards a better understanding of parallel visual processing in human vision: Evidence for exhaustive analysis of visual information. Journal of Experimental Psychology: General, 145(6), 672–707.
8.
CarrollJ. D.ArabieP. (1998). Multidimensional scaling. In BirnbaumM. H. (Ed.), Measurement, judgment and decision making (pp. 179–250). Academic Press.
9.
ChanL. K.HaywardW. G. (2009). Feature integration theory revisited: Dissociating feature detection and attentional guidance in visual search. Journal of Experimental Psychology: Human Perception and Performance, 35(1), 119–132.
10.
ChunM. M.WolfeJ. M. (1996). Just say no: How are visual searches terminated when there is no target present?Cognitive Psychology, 30(1), 39–78.
11.
CohenM. A.AlvarezG. A.NakayamaK.KonkleT. (2017). Visual search for object categories is predicted by the representational architecture of high-level visual cortex. Journal of Neurophysiology, 117(1), 388–402.
12.
DaggettE. W.HoutM. C. (2025). A tutorial review on methods for collecting similarity judgments from human observers. Attention, Perception, & Psychophysics, 87, 737–751.
13.
DuncanJ.HumphreysG. W. (1989). Visual search and stimulus similarity. Psychological Review, 96(3), 433–458.
14.
EnnsJ. T.KingstoneA. (1995). Access to global and local properties in visual search for compound stimuli. Psychological Science, 6(5), 283–291.
15.
GodwinH. J.HoutM. C.MenneerT. (2014). Visual similarity is stronger than semantic similarity in guiding visual search for numbers. Psychonomic Bulletin & Review, 21, 689–695.
16.
GodwinH. J.WalenchokS. C.HouptJ. W.HoutM. C.GoldingerS. D. (2015). Faster than the speed of rejection: Object identification processes during visual search for multiple targets. Journal of Experimental Psychology: Human Perception and Performance, 41(4), Article 1007.
17.
HoffmanJ. E. (1979). A two-stage model of visual search. Perception & Psychophysics, 25(4), 319–327.
18.
HorstJ. S.HoutM. C. (2016). The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research. Behavior Research Methods, 48, 1393–1409.
19.
HorstmannG.HerwigA.BeckerS. I. (2016). Distractor dwelling, skipping, and revisiting determine target absent performance in difficult visual search. Frontiers in Psychology, 7, Article 1152.
20.
HoutM. C.GodwinH. J.FitzsimmonsG.RobbinsA.MenneerT.GoldingerS. D. (2016). Using multidimensional scaling to quantify similarity in visual search and beyond. Attention, Perception, & Psychophysics, 78, 3–20.
21.
HoutM. C.GoldingerS. D. (2015). Target templates: The precision of mental representations affects attentional guidance and decision-making in visual search. Attention, Perception, & Psychophysics, 77, 128–149.
HuangL. (2020). Space of preattentive shape features. Journal of Vision, 20(4), Article 10.
24.
KruijneW.MeeterM. (2015). The long and the short of priming in visual search. Attention, Perception, & Psychophysics, 77(5), 1558–1573.
25.
KruskalJ. B. (1964). Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29(2), 115–129.
26.
LiesefeldH. R.MüllerH. J. (2020). A theoretical attempt to revive the serial/parallel-search dichotomy. Attention, Perception, & Psychophysics, 82, 228–245.
27.
MadridJ.CunninghamC. A.RobbinsA.HoutM. C. (2019). You’re looking for what? Comparing search for familiar, nameable objects to search for unfamiliar, novel objects. Visual Cognition, 27(1), 8–20.
28.
MoranR.ZehetleitnerM.MüllerH. J.UsherM. (2013). Competitive guided search: Meeting the challenge of benchmark RT distributions. Journal of Vision, 13(8), Article 24.
29.
MruczekR. E.SheinbergD. L. (2005). Distractor familiarity leads to more efficient visual search for complex stimuli. Perception & Psychophysics, 67(6), 1016–1031.
NeiderM. B.ZelinskyG. J. (2008). Exploring set size effects in scenes: Identifying the objects of search. Visual Cognition, 16(1), 1–10.
32.
PeirceJ. W.GrayJ. R.SimpsonS.MacAskillM. R.HöchenbergerR.SogoH.KastmanE.LindeløvJ. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51, 195–203. https://doi.org/10.3758/s13428-018-01193-y
33.
PilarczykJ.KunieckiM. (2014). Emotional content of an image attracts attention more than visually salient features in various signal-to-noise ratio conditions. Journal of Vision, 14(12), Article 4.
34.
SchmidtJ.ZelinskyG. J. (2009). Short article: Search guidance is proportional to the categorical specificity of a target cue. Quarterly Journal of Experimental Psychology, 62(10), 1904–1914.
35.
SchwarzW.EiseltA. K. (2012). Numerical distance effects in visual search. Attention, Perception, & Psychophysics, 74, 1098–1103.
36.
ShepardR. N. (1980). Multidimensional scaling, tree-fitting, and clustering. Science, 210(4468), 390–398.
37.
TorgersonW. S. (1958). Theory and methods of scaling. Wiley.
38.
TreismanA. M.GeladeG. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136.
39.
VõM. L. H.HendersonJ. M. (2009). Does gravity matter? Effects of semantic and syntactic inconsistencies on the allocation of attention during scene perception. Journal of Vision, 9(3), Article 24.
40.
von GrünauM.DubéS.GaleraC. (1994). Local and global factors of similarity in visual search. Perception & Psychophysics, 55, 575–592.
41.
WolfeJ. M. (1994). Guided search 2.0 a revised model of visual search. Psychonomic Bulletin & Review, 1, 202–238.
42.
WolfeJ. M. (2007). Guided search 4.0: Current progress with a model of visual search. In GrayW. (Ed.), Integrated models of cognitive systems (pp. 99–119). Oxford University Press.
43.
WolfeJ. M. (2021). Guided Search 6.0: An updated model of visual search. Psychonomic Bulletin & Review, 28(4), 1060–1092.
44.
WolfeJ. M.HorowitzT. S. (2017). Five factors that guide attention in visual search. Nature Human Behaviour, 1(3), Article 0058.
45.
WolfeJ. M.CainM.EhingerK.DrewT. (2015). Guided Search 5.0: Meeting the challenge of hybrid search and multiple-target foraging. Journal of Vision, 15(12), 1106–1106.
46.
WolfeJ. M.CaveK. R.FranzelS. L. (1989). Guided search: An alternative to the feature integration model for visual search. Journal of Experimental Psychology: Human Perception and Performance, 15(3), 419–433.
47.
WolfeJ. M.PalmerE. M.HorowitzT. S. (2010). Reaction time distributions constrain models of visual search. Vision Research, 50(14), 1304–1311.
48.
ZsidoA. N.HoutM. C.HernandezM.WhiteB.PolákJ.KissB. L.GodwinH. J. (2024). No evidence of attentional prioritization for threatening targets in visual search. Scientific Reports, 14(1), Article 5651.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
