| |
The centroid paradigm: Quantifying feature-based attention in terms of attention filters
- 作者:Peng Sun ; Charles Chubb ; Charles E. Wright…
- 关键词:Selective attention ; Methodology ; Centroid estimation ; Statistical summary representations (SSRs) ; Feature ; based attention ; Attention filter
- 刊名:Attention, Perception, & Psychophysics
- 出版年:2016
- 出版时间:February 2016
- 年:2016
- 卷:78
- 期:2
- 页码:474-515
- 全文大小:7,522 KB
- 参考文献:Alvarez, G.A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in Cognitive Sciences, 15(3), 122–131.CrossRef PubMed
Alvarez, G.A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological Science, 19(4), 392–398.PubMedCentral CrossRef PubMed Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12(2), 157–162.CrossRef PubMed Arman, A.C., Ciaramitaro, V.M., & Boynton, G.M. (2006). Effects of feature-based attention on the motion aftereffect at remote locations. Vision Research, 46(18), 2968–2976.CrossRef PubMed Baldassi, S., & Verghese, P. (2005). Attention to locations and features: Different topdown modulation of detector weights. Journal of Vision, 5(6), 556–570.CrossRef PubMed Ball, K., & Sekuler, R. (1981). Adaptive processing of visual motion. Journal of Experimental Psychology: Human Perception and Performance, 7(4), 780–794.PubMed Blair, G., Wright, C. E., Chubb, C., & Sperling, G. (2015). Disc size supports top-down, selective attention in a task requiring integration across multiple targets. Journal of Vision, 15(12), 897–897. Chong, S.C., & Treisman, A.M. (2003). Representation of statistical properties. Vision Research, 43(4), 393–404.CrossRef PubMed Chong, S.C., & Treisman, A.M. (2005a). Attentional spread in the statistical processing of visual displays. Perception and Psychophysics, 67(1), 1–13.CrossRef PubMed Chong, S. C., & Treisman, A. M. (2005b). Statistical processing: Computing the average size in perceptual groups. Vision Research, 45(7), 891–900.CrossRef PubMed Chubb, C., & Nam, J.-H. (2000). The variance of high contrast texture is sensed using negative half-wave rectification. Vision Research, 40, 1695–1709.CrossRef PubMed Chubb, C., & Talevich, J. (2002). Attentional control of texture orientation judgments. Vision Research, 42, 311–330.CrossRef PubMed Dakin, S.C. (2001). Information limit on the spatial integration of local orientation signals. Journal of the Optical Society of America A, 18, 1016–1026.CrossRef Davis, E.T., & Graham, N. (1981). Spatial frequency uncertainty effects in the detection of sinusoidal gratings. Vision Research, 21(5), 705–712.CrossRef PubMed Drew, S., Chubb, C., & Sperling, G. (2010). Precise attention filters for Weber contrast derived from centroid estimations. Journal of Vision, 10(10:20), 1–16. http://www.journalofvision.org/content/10/10/20 . Felisberti, F.M., & Zanker, J.M. (2005). Attention modulates perception of transparent motion. Vision Research, 45(19), 2587–2599.CrossRef PubMed Fieller, E. (1954). Some problems in interval estimation. Journal of the Royal Statistical Society, Series B, 16 (2), 175–185. Graybill, F. (1961). An introduction to linear statistical models, Volume 1: McGraw-Hill. Haenny, P.E., Maunsell, J.H., & Schiller, P.H. (1988). State-dependent activity in monkey visual cortex. ii. Retinal and extraretinal factors in v4. Experimental Brain Research, 69(2), 245–259.CrossRef PubMed Hasher, L., & Zacks, R.T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), Psychology of Learning and Motivation (Vol. 22 pp. 193–225). New York: Academic Press. Hayden, B.Y., & Gallant, J.L. (2005). Time course of attention reveals different mechanisms for spatial and feature-based attention in area v4. Neuron, 47(5), 637–643.CrossRef PubMed Ho, T.C., Brown, S., Abuyo, N.A., Ku, E.-H.J., & Serences, J. T. (2012). Perceptual consequences of feature-based attentional enhancement and suppression. Journal of Vision, 12, 1–17. (8:15 (http://www.journalofvision.org/content/12/8/15 , 10.1167/12.8.15 ))CrossRef Inverso, M., Sun, P., Chubb, C., Wright, C.E., & Sperling, G. (In press). Evidence against global attention filters selective for absolute bar-orientation in human vision. Attention, Perception and Psychophysics. Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature and Neuroscience, 8(5), 679–685.CrossRef Lankheet, M.J., & Verstraten, F.A. (1995). Attentional modulation of adaptation to two-component transparent motion. Vision Research, 35(10), 1401–1412.CrossRef PubMed Ling, S., Liu, T., & Carrasco, M. (2009). How spatial and feature-based attention affect the gain and tuning of population responses. Vision Research, 49(10), 1194–1204.PubMedCentral CrossRef PubMed Liu, T., Larsson, J., & Carrasco, M. (2007). Feature-based attention modulates orientation-selective responses in human visual cortex. Neuron, 55(2), 313–323.PubMedCentral CrossRef PubMed Liu, T., & Mance, I. (2011). Constant spread of feature-based attention across the visual field. Vision Research, 51(1), 26–33.CrossRef PubMed Liu, T., Slotnick, S.D., Serences, J. T., & Yantis, S. (2003). Cortical mechanisms of feature-based attentional control. Cerebral Cortex, 13(12), 1334–1343.CrossRef PubMed Lu, Z.-L., & Dosher, B.A. (1998). External noise distinguishes attention mechanisms. Vision Research, 38, 1183–1198.CrossRef PubMed Lustig, C., Hasher, L., & Zacks, R.T. (2007). Inhibitory deficit theory: Recent developments in a “new view.” In D. S. Gorfein, & C. M. MacLeod (Eds.), The place of inhibition in cognition (pp. 145–162). Washington: American Psychological Association.CrossRef Martinez-Trujillo, J.C., & Treue, S. (2004). Feature-based attention increases the selectivity of population responses in primate visual cortex. Current Biology, 14(9), 744–751.CrossRef PubMed Maunsell, J.H., Sclar, G., Nealey, T.A., & DePriest, D.D. (1991). Extraretinal representations in area v4 in the macaque monkey. Visual Neuroscience, 7(6), 561–573.CrossRef PubMed McAdams, C.J., & Maunsell, J.H. (2000). Attention to both space and feature modulates neuronal responses in macaque area v4. Journal of Neurophysiology, 83(3), 1751–1755.PubMed Motter, B.C. (1994). Neural correlates of attentive selection for color or luminance in extrastriate area v4. The Journal of Neuroscience, 14(4), 2178–2189.PubMed Muller, M.M., Anderson, S., Trujillo, N.J., Valdes-Sosa, P., & Hillyard, S.A. (2006). Feature-selective attention enhances color signals in early visual areas of the human brain. Proceedings of the National Academy of Sciences, USA, 103(38), 14250–14254.CrossRef Nam, J.-H., & Chubb, C. (2000). Texture luminance judgments are approximately veridical. Vision Research, 40, 1677–1694.CrossRef PubMed O’Craven, K.M., Rosen, B.R., Kwong, K.K., Treisman, A.M., & Savoy, R.L. (1997). Voluntary attention modulates fMRI activity in human MT-MST. Neuron, 18(4), 591–598.CrossRef PubMed Quigley, C., Andersen, S. K., Schultz, L., Grunwald, M., & Muller, M. M. (2010). Feature-selective attention: Evidence for a decline in old age. Neuroscience Letters, 474(1), 5–8.CrossRef PubMed Quigley, C., & Muller, M.M. (2014). Feature-selective attention in healthy old age: A selective decline in selective attention? The Journal of Neuroscience, 34(7), 2471–2476.PubMedCentral CrossRef PubMed Rossi, A.F., & Paradiso, M.A. (1995). Feature-specific effects of selective visual attention. Vision Research, 35(5), 621–634.CrossRef PubMed Saenz, M., Buracas, G.T., & Boynton, G.M. (2002). Global effects of feature-based attention in human visual cortex. Nature Neuroscience, 5(7), 631–632.CrossRef PubMed Saenz, M., Buracas, G. T., & Boynton, G.M. (2003). Global feature-based attention for motion and color. Vision Research, 43(6), 629–637.CrossRef PubMed Schoenfeld, M.A., Hopf, J.M., Martinez, A., Mai, H.M., Sattler, C., Gasde, A., Heinze, H.J., & Hillyard, S.A. (2007). Spatio-temporal analysis of feature-based attention. Cerebral Cortex, 17(10), 2468–2477.CrossRef PubMed Seidemann, E., & Newsome, W.T. (1999). Effect of spatial attention on the responses of area MT neurons. Journal of Neurophysiology, 81(4), 1783–1794.PubMed Serences, J.T., & Boynton, G.M. (2007). Feature-based attentional modulations in the absence of direct visual stimulation. Neuron, 55(2), 301–312.CrossRef PubMed Serences, J.T., Saproo, S., Scolari, M., Ho, T., & Muftuler, L.T. (2009). Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions. Neuroimage, 44(1), 223–231.CrossRef PubMed Shih, S.I., & Sperling, G. (1996). Is there feature-based attentional selection in visual search? Journal of Experimental Psychology: Human Perception and Performance, 22(3), 758–779.PubMed Solomon, J.A. (2010). Visual discrimination of orientation statistics in crowded and uncrowded arrays. Journal of Vision, 10, 1–16. (14:19 http://www.journalofvision.org/content/10/14/19 , 10.1167/10.14.19 )CrossRef PubMed Sperling, G. (1963). A model for visual memory tasks. Human Factors, 5, 19–31.PubMed Sperling, G., Wurst, S. A., & Lu, Z.-L. (1992). Using repetition detection to define and localize the processes of selective attention. In D. E. Meyer, & S. Kornblum (Eds.), Attention and performance XIV: Synergies in experimental psychology, artificial intelligence, and cognitive neuroscience–A silver jubilee, chapter 12 (pp. 265–298). Cambridge: MIT Press. Treue, S. (2001). Neural correlates of attention in primate visual cortex. Trends in Neuroscience, 24(5), 295–300.CrossRef Treue, S., & Martinez-Trujillo, J.C. (1999). Feature-based attention influences motion processing gain in macaque visual cortex. Nature, 399(6736), 575–579.CrossRef PubMed White, A.L., & Carrasco, M. (2011). Feature-based attention involuntarily and simultaneously improves visual performance across locations. Journal of Vision, 11, 1–10. (6:15(http://www.journalofvision.org/content/11/6/15 , 10.1167/11.6.15 )) Zhang, W., & Luck, S.J. (2009). Feature-based attention modulates feedforward visual processing. Nature Neuroscience, 12(1), 24–25.CrossRef PubMed
- 作者单位:Peng Sun (1) (2)
Charles Chubb (1) Charles E. Wright (1) George Sperling (1)
1. Department of Cognitive Sciences, University of California, Irvine, CA, 92697-5100, USA 2. Department of Psychology, New York University, New York, NY, 10003, USA
- 刊物主题:Cognitive Psychology;
- 出版者:Springer US
- ISSN:1943-393X
文摘
This paper elaborates a recent conceptualization of feature-based attention in terms of attention filters (Drew et al., Journal of Vision, 10(10:20), 1–16, 2010) into a general purpose centroid-estimation paradigm for studying feature-based attention. An attention filter is a brain process, initiated by a participant in the context of a task requiring feature-based attention, which operates broadly across space to modulate the relative effectiveness with which different features in the retinal input influence performance. This paper describes an empirical method for quantitatively measuring attention filters. The method uses a “statistical summary representation” (SSR) task in which the participant strives to mouse-click the centroid of a briefly flashed cloud composed of items of different types (e.g., dots of different luminances or sizes), weighting some types of items more strongly than others. In different attention conditions, the target weights for different item types in the centroid task are varied. The actual weights exerted on the participant’s responses by different item types in any given attention condition are derived by simple linear regression. Because, on each trial, the centroid paradigm obtains information about the relative effectiveness of all the features in the display, both target and distractor features, and because the participant’s response is a continuous variable in each of two dimensions (versus a simple binary choice as in most previous paradigms), it is remarkably powerful. The number of trials required to estimate an attention filter is an order of magnitude fewer than the number required to investigate much simpler concepts in typical psychophysical attention paradigms. Keywords Selective attention Methodology Centroid estimation Statistical summary representations (SSRs) Feature-based attention Attention filter
| |
NGLC 2004-2010.National Geological Library of China All Rights Reserved.
Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
For exchange or info please contact us via email.
| |