Knowledge about regularities in the environment can be used to facilitate perception, memory, and language acquisition. Given this usefulness, we hypothesized that statistically structured sources of information receive attentional priority over noisier sources, independent of their intrinsic salience or goal relevance. We report three experiments that support this hypothesis. Experiment 1 shows that regularities bias spatial attention: Visual search was facilitated at a location containing temporal regularities, even though these regularities did not predict target location, timing, or identity. Experiments 2 and 3 show that regularities bias feature attention: Attentional capture doubled in magnitude when singletons appeared, respectively, in a color or dimension with temporal regularities among task-irrelevant stimuli. Prioritization of the locations and features of regularities is not easily accounted for in the conventional dichotomy between stimulus-driven and goal-directed attention. This prioritization may in turn promote further statistical learning, helping the mind to acquire knowledge about stable aspects of the environment.
BakerC. I.OlsonC. R.BehrmannM. (2004). Role of attention and perceptual grouping in visual statistical learning. Psychological Science, 15, 460–466.
4.
BradyT. F.KonkleT.AlvarezG. A. (2009). Compression in visual short-term memory: Using statistical regularities to form more efficient memory representations. Journal of Experimental Psychology: General, 138, 487–502.
5.
BrainardD. H. (1997). The Psychophysics Toolbox. Spatial Vision,10,433–436.
6.
CampbellK. L.ZimermanS.HealeyM. K.LeeM. M.HasherL. (2012). Age differences in visual statistical learning. Psychology and Aging, 27, 650–656. doi:10.1037/a0026780
7.
ChalkM.SeitzA.SerièsP. (2010). Rapidly learned stimulus expectations alter perception of motion. Journal of Vision, 10(8), Article 2. Retrieved from http://www.journalofvision.org/content/10/8/2
8.
ChunM. M.GolombJ. D.Turk-BrowneN. B. (2011). A taxonomy of external and internal attention. Annual Review of Psychology, 62, 73–101.
9.
ChunM. M.JiangY. (1998). Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psychology, 36, 28–71.
10.
ChunM. M.JiangY. (1999). Top-down attentional guidance based on implicit learning of visual covariation. Psychological Science, 10, 360–365.
11.
CiaramelliE.GradyC. L.MoscovitchM. (2008). Top-down and bottom-up attention to memory: A hypothesis (AtoM) on the role of the posterior parietal cortex in memory retrieval. Neuropsychologia, 46, 1828–1851.
12.
CorbettaM.ShulmanG. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 215–229.
13.
FiserJ.AslinR. N. (2001). Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychological Science, 12, 499–504.
14.
FiserJ.AslinR. N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28, 458–467.
15.
FolkC. L.RemingtonR. W.JohnstonJ. C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044.
16.
FranconeriS. L.SimonsD. J. (2003). Moving and looming stimuli capture attention. Perception & Psychophysics, 65, 999–1010.
17.
GebhartA. L.AslinR. N.NewportE. L. (2009). Changing structures in midstream: Learning along the statistical garden path. Cognitive Science, 33, 1087–1116.
18.
Graf EstesK. M.EvansJ.AlibaliM. W.SaffranJ. R. (2007). Can infants map meaning to newly segmented words? Statistical segmentation and word learning. Psychological Science, 18, 254–260.
19.
HopfingerJ. B.BuonocoreM. H.MangunG. R. (2000). The neural mechanisms of top-down attentional control. Nature Neuroscience, 3, 284–291.
JohnstonW. A.HawleyK. J.PlewS. H.ElliottJ. M.DeWittM. J. (1990). Attention capture by novel stimuli. Journal of Experimental Psychology: General, 119, 397–411.
22.
JonidesJ. (1981). Voluntary vs. automatic control over the mind’s eye’s movement. In LongJ. B.BaddeleyA. D. (Eds.), Attention and performance (Vol. 9, pp. 187–203). Hillsdale, NJ: Erlbaum.
23.
JungéJ. A.SchollB. J.ChunM. M. (2007). How is spatial context learning integrated over signal versus noise? A primacy effect in contextual cueing. Visual Cognition, 15, 1–11.
24.
MillerE. K.CohenJ. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.
25.
PashlerH.JohnstonJ.RuthruffE. (2001). Attention and performance. Annual Review of Psychology, 52, 629–651.
26.
PelliD. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision,10, 437–442.
27.
PerruchetP.PactonS. (2006). Implicit learning and statistical learning: Two approaches, one phenomenon. Trends in Cognitive Sciences, 10, 233–238.
28.
PosnerM. I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25.
29.
SaffranJ. R.AslinR. N.NewportE. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.
30.
SmythA. C.ShanksD. R. (2008). Awareness in contextual cuing with extended and concurrent explicit tests. Memory & Cognition, 36, 403–415.
31.
SummerfieldJ. J.LepsienJ.GitelmanD. R.MesulamM. M.NobreA. C. (2006). Orienting attention based on long-term memory experience. Neuron, 49, 905–916.
32.
TheeuwesJ. (1992). Perceptual selectivity for color and form. Perception & Psychophysics, 51, 599–606.
TreismanA.SoutherJ. (1985). Search asymmetry: A diagnostic for preattentive processing of separable features. Journal of Experimental Psychology: General, 114, 285– 310.
35.
Turk-BrowneN. B.IsolaP. J.SchollB. J.TreatT. A. (2008). Multidimensional visual statistical learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 399–407.
36.
Turk-BrowneN. B.JungéJ.SchollB. J. (2005). The automaticity of visual statistical learning. Journal of Experimental Psychology: General, 134, 552–564.
Turk-BrowneN. B.SchollB. J.ChunM. M.JohnsonM. K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21, 1934–1945.
39.
Turk-BrowneN. B.SchollB. J.JohnsonM. K.ChunM. M. (2010). Implicit perceptual anticipation triggered by statistical learning. Journal of Neuroscience, 30, 11177–11187.
40.
UmemotoA.ScolariM.VogelE. K.AwhE. (2010). Statistical learning induces discrete shifts in the allocation of working memory resources. Journal of Experimental Psychology: Human Perception and Performance, 36, 1419–1429.
41.
WolfeJ. M.HorowitzT. S. (2004). What attributes guide the deployment of visual attention and how do they do it?Nature Reviews Neuroscience, 5, 495–501.
42.
YantisS. (2000). Goal-directed and stimulus-driven determinants of attentional control. In MonsellS.DriverJ. (Eds.), Attention and performance (Vol. 18, pp. 73–103). Cambridge, MA: MIT Press.
43.
YantisS.JonidesJ. (1984). Abrupt visual onsets and selective attention: Evidence from visual search. Journal of Experimental Psychology: Human Perception and Performance, 10, 601–621.
44.
ZhaoJ.NgoN.McKendrickR.Turk-BrowneN. B. (2011). Mutual interference between statistical summary perception and statistical learning. Psychological Science, 22, 1212–1219.
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