Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items

Psychological Science

Volumn 22, Issue 3, 2011, Pages 384-392

  Brady, Timothy F ^a   Alvarez, George A ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

constructive memory; ensemble statistics; summary statistics; working memory

Indexed keywords

ARTICLE; ATTENTION; COLOR VISION; CONCEPT FORMATION; HUMAN; LEARNING; LONG TERM MEMORY; ORIENTATION; PATTERN RECOGNITION; PERCEPTION; PSYCHOLOGICAL MODEL; SHORT TERM MEMORY; STATISTICAL MODEL;

ATTENTION; COLOR PERCEPTION; CONCEPT FORMATION; GENERALIZATION, STIMULUS; HUMANS; MEMORY, SHORT-TERM; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; ORIENTATION; PATTERN RECOGNITION, VISUAL; RETENTION (PSYCHOLOGY); SIZE PERCEPTION;

EID: 79954563465     PISSN: 09567976     EISSN: 14679280     Source Type: Journal    
DOI: 10.1177/0956797610397956     Document Type: Article

References (38)

1. Alvarez, G.A., & Cavanagh, P. (2004). The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychological Science, 15, 106-111.
2. Alvarez, G.A., & Oliva, A. (2008). The representation of simple ensemble visual features outside the focus of attention. Psychological Science, 19, 392-398.
3. Alvarez, G.A., & Oliva, A. (2009). Spatial ensemble statistics: Efficient codes that can be represented with reduced attention. Proceedings of the National Academy of Sciences, USA, 106, 7345-7350.
4. Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12, 157-162.
5. Bartlett, F. (1932). Remembering: A study in experimental and social psychology. New York, NY: Macmillan.
6. Bays, P.M., Catalao, R.F.G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10), Article 7. Retrieved from http://journalofvision.org/9/10/7/.
7. Bays, P.M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321, 851.
8. Brady, T.F., Konkle, T., & Alvarez, G.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.
9. Brady, T.F., & Tenenbaum, J.B. (2010). Encoding higher-order structure in visual working memory: A probabilistic model. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 411-416). Portland, OR: Cognitive Science Society.
10. Chong, S.C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43, 393-404.
11. Chong, S.C., & Treisman, A. (2005a). Attentional spread in the statistical processing of visual displays. Perception & Psychophysics, 67, 1-13.
12. Chong, S.C., & Treisman, A. (2005b). Statistical processing: Computing the average size in perceptual groups. Vision Research, 45, 891-900.
13. de Fockert, J., & Wolfenstein, C. (2009). Rapid extraction of mean identity from sets of faces. Quarterly Journal of Experimental Psychology, 62, 1716-1722.
14. Feigenson, L. (2008). Parallel non-verbal enumeration is constrained by a set-based limit. Cognition, 107, 1-18.
15. Haberman, J., & Whitney, D. (2007). Rapid extraction of mean emotion and gender from sets of faces. Current Biology, 17, R751-R753.
16. Haberman, J., & Whitney, D. (2009). The visual system ignores outliers when extracting a summary representation [Abstract]. Journal of Vision, 9(8), Article 804. Retrieved from http://www.journalofvision.org/content/9/8/804.
17. Halberda, J., Sires, S.F., & Feigenson, L. (2006). Multiple spatially overlapping sets can be enumerated in parallel. Psychological Science, 17, 572-576.
18. Hemmer, P., & Steyvers, M. (2009). Integrating episodic memories and prior knowledge at multiple levels of abstraction. Psychonomic Bulletin & Review, 16, 80-87.
19. Hollingworth, A. (2008). Visual memory for natural scenes. In S.J. Luck & A. Hollingworth (Eds.), Visual memory (pp. 123-162). New York, NY: Oxford University Press.
20. Huang, J., & Sekuler, R. (2010). Distortions in recall from visual memory: Two classes of attractors at work. Journal of Vision, 10(2), Article 24. Retrieved from http://www.journalofvision.org/content/10/2/24.full.
21. Huang, L., Treisman, A., & Pashler, H. (2007). Characterizing the limits of human visual awareness. Science, 317, 823-825.
22. Huttenlocher, J., Hedges, L.V., & Vevea, J.L. (2000). Why do categories affect stimulus judgment? Journal of Experimental Psychology: General, 129, 220-241.
23. Jiang, Y., Olson, I.R., & Chun, M.M. (2000). Organization of visual short-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 683-702.
24. Johnson, J.S., Spencer, J., Luck, S., & Schöner, G. (2009). A dynamic neural field model of visual working memory and change detection. Psychological Science, 20, 568-577.
25. Konkle, T., & Oliva, A. (2007). Normative representation of objects: Evidence for an ecological bias in object perception and memory. In D.S. McNamara & J.G. Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 407-413). Austin, TX: Cognitive Science Society.
26. Lampinen, J.M., Copeland, S., & Neuschatz, J.S. (2001). Recollections of things schematic: Room schemas revisited. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1211-1222.
27. Lin, P.-H., & Luck, S.J. (2008). The influence of similarity on visual working memory representations. Visual Cognition, 17, 356-372.
28. Luck, S.J., & Vogel, E.K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281.
29. Oliva, A. (2005). Gist of the scene. In L. Itti, G. Rees, & J.K. Tsotsos (Eds.), The encyclopedia of neurobiology of attention (pp. 251-256). San Diego, CA: Elsevier.
30. Parkes, L., Lund, J., Angelucci, A., Solomon, J.A., & Morgan, M. (2001). Compulsory averaging of crowded orientation signals in human vision. Nature Neuroscience, 4, 739-744.
31. Rosenholtz, R., & Alvarez, G.A. (2007) How and why we perceive sets: What does modeling tell us? [Abstract]. Perception, 36(ECVP Abstract Supplement), 8. Retrieved fromhttp://www.perceptionweb.com/ecvp/ecvp07.pdf.
32. Rouder, J.N., Morey, R.D., Cowan, N., Zwilling, C.E., Morey, C.C., & Pratte, M.S. (2008). An assessment of fixed-capacity models of visual working memory. Proceedings of the National Academy of Sciences, USA, 105, 5975-5979.
33. Sanocki, T., Sellers, E., Mittelstadt, J., & Sulman, N. (2010). How high is visual short term memory capacity for object layout? Attention, Perception, & Psychophysics, 72, 1097-1109.
34. Turk-Browne, N.B., Jungé, J.A., & Scholl, B.J. (2005). The automaticity of visual statistical learning. Journal of Experimental Psychology: General, 134, 552-564.
35. Victor, J.D., & Conte, M.M. (2004). Visual working memory for image statistics. Vision Research, 44, 541-556.
36. Vidal, J.R., Gauchou, H.L., Tallon-Baudry, C., & O'Regan, J.K. (2005). Relational information in visual short-term memory: The structural gist. Journal of Vision, 5(3), Article 8. Retrieved from http://journalofvision.org/5/3/8/.
37. Wilken, P., & Ma, W.J. (2004). A detection theory account of change detection. Journal of Vision, 4(12), Article 11. Retrieved from http://www.journalofvision.org/content/4/12/11/I.
38. Zhang, W., & Luck, S.J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 452, 233-235.