No capacity limit in attentional tracking: Evidence for probabilistic inference under a resource constraint

Journal of Vision

Volumn 9, Issue 11, 2009, Pages

  Ma, Wei Ji ^a,b   Huang, Wei ^a  

^a BAYLOR COLLEGE OF MEDICINE   (United States)

^b NONE   (United States)

Author keywords

Attention; Bayesian observer; Change detection; Multiple object tracking

Indexed keywords

ARTICLE; ATTENTION; BAYES THEOREM; BIOLOGICAL MODEL; HUMAN; MOVEMENT PERCEPTION; PERCEPTIVE DISCRIMINATION; PERCEPTIVE THRESHOLD; PHOTOSTIMULATION; PHYSIOLOGY; PROBABILITY; SHORT TERM MEMORY;

ATTENTION; BAYES THEOREM; DISCRIMINATION (PSYCHOLOGY); HUMANS; MEMORY, SHORT-TERM; MODELS, NEUROLOGICAL; MOTION PERCEPTION; PHOTIC STIMULATION; PROBABILITY; SENSORY THRESHOLDS;

EID: 70349962910     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/9.11.3     Document Type: Article

References (45)

1. Aldo Faisal, A., Selen, L. P. J., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews. Neuroscience, 9, 292-303.
2. Alvarez, G. A., & Franconeri, S. L. (2007). How many objects can you track?: Evidence for a resource-limited attentive tracking mechanism. Journal of Vision, 7(13):14, 1-10, http://journalofvision.org/ 7/13/14/, doi:10.1167/7.13.14.
3. Baldassi, S., & Verghese, P. (2002). Comparing integration rules in visual search. Journal of Vision, 2(8):3, 559-570, http://journalofvision.org/2/8/3/, doi:10.1167/2.8.3.
4. Bays, P. M., & Husain, M. (2008). Dynamic shifts of limited working memory resources in human vision. Science, 321, 851-854.
5. Bays, P. M., & Husain, M. (2009). Response to comment on "Dynamic shifts of limited working memory resources in human vision." Science, 323, 877.
6. Beck, J. M., Ma, W. J., Kiani, R., Hanks, T. D., Churchland, A. K., Roitman, J. D., et al. (2008). Bayesian decision-making with probabilistic population codes. Neuron, 60, 1142-1145.
7. Cavanagh, P., & Alvarez, G. A. (2005). Tracking multiple targets with multifocal attention. Trends in Cognitive Sciences, 9, 349-354.
8. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioural Brain Science, 24, 87-114.
9. Eckstein, M. P., Thomas, J. P., Palmer, J., & Shimozaki, S. S. (2000). A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. Perception & Psychophysics, 62, 425-451.
10. Graham, N., Kramer, P., & Yager, D. (1987). Signal detection models for multidimensional stimuli: Probability distributions and combination rules. Journal of Mathematical Psychology, 31, 366-409.
11. Green, C. S., & Bavelier, D. (2006). Enumeration versus multiple object tracking: The case of action video game players. Cognition, 101, 217-245.
12. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. Los Altos, CA: John Wiley & Sons.
13. Hospedales, T., & Vijayakumar, S. (2009). Multisensory oddity detection as Bayesian inference. PLoS ONE, 4, e4205.
14. Hulleman, J. (2005). The mathematics of multiple object tracking: From proportions correct to number of objects tracked. Vision Research, 45, 2298-2309.
15. Huys, Q., Zemel, R. S., Natarajan, R., & Dayan, P. (2007). Fast population coding. Neural Computation, 19, 404-441.
16. Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304.
17. Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27, 712-719.
18. Knill, D. C., & Richards, W. (Eds.) (1996). Perception as Bayesian Inference. New York: Cambridge University Press.
19. Kording, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007). Causal inference in multisensory perception. PLoS ONE, 2, e943.
20. Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281.
21. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9, 1432-1438.
22. Ma, W. J., Navalpakkam, V., Beck, J. M., & Pouget, A. (2008). Bayesian theory of visual search. Paper presented at the Society for Neuroscience.
23. Narasimhan, S., Tripathy, S. P., & Barrett, B. T. (2009). Loss of positional information when tracking multiple moving dots: The role of visual memory. Vision Research, 49, 10-27.
24. Nolte, L. W., & Jaarsma, D. (1966). More on the detection of one of M orthogonal signals. Journal of the Acoustical Society of America, 41, 497-505.
25. Oksama, L., & Hyona, J. (2008). Dynamic binding of identity and location information: A serial model of multiple identity tracking. Cognitive Psychology, 56, 237-283.
26. Palmer, J. (1990). Attentional limits on the perception and memory of visual information. Journal of Experimental Psychology: Human Perception and Performance, 16, 332-350.
27. Palmer, J., Verghese, P., & Pavel, M. (2000). The psychophysics of visual search. Vision Research, 40, 1227-1268.
28. Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44, 369-378.
29. Pylyshyn, Z. W., & Storm, R. W. (1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision, 3, 179-197.
30. Rensink, R. A. (2000). The dynamic representation of scenes. Visual Cognition, 7, 17-42.
31. Reynolds, J. H., & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61, 168-185.
32. Rosenholtz, R. (2001). Visual search for orientation among heterogeneous distractors: Experimental results and implications for signal detection theory models of search. Journal of Experimental Psychology: Human Perception and Performance, 27, 985-999.
33. Sato, Y., Toyoizumi, T., & Aihara, K. (2007). Bayesian inference explains perception of unity and ventriloquism aftereffect: Identification of common sources of audiovisual stimuli. Neural Computation, 19, 3335-3355.
34. Seung, H., & Sompolinsky, H. (1993). Simple model for reading neuronal population codes. Proceedings of National Academy of Sciences of the United States of America, 90, 10749-10753.
35. Shaw, M. L. (1980). Identifying attentional and decision-making components in information processing. In R. S. Nickerson (Ed.), Attention and performance (vol. VIII, pp. 277-296). Hillsdale, NJ: Erlbaum.
36. Tripathy, S. P., & Barrett, B. T. (2004). Severe loss of positional information when detecting deviations in multiple trajectories. Journal of Vision, 4(12):4, 1020-1043, http://journalofvision.org/ 4/12/4/, doi:10.1167/4.12.4.
37. Tripathy, S. P., Narasimhan, S., & Barrett, B. T. (2007). On the effective number of tracked trajectories in normal human vision. Journal of Vision, 7(6):2, 1-18, http://journalofvision.org/7/6/ 2/, doi:10.1167/7.6.2.
38. Verghese, P. (2001). Visual search and attention: A signal detection theory approach. Neuron, 31, 523-535.
39. Vincent, B. T., Baddeley, R. J., Troscianko, T., & Gilchrist, I. D. (2009). Optimal feature integration in visual search [Abstract]. Journal of Vision, 9(5):15, 1-11, http://journalofvision.org/9/ 5/15/, doi:10.1167/9.5.15.
40. Vul, E., Frank, M., Alvarez, G. A., & Tenenbaum, J. B. (2009). Statistical decision theory and the allocation of cognitive resources in multiple object tracking. Paper presented at the Computational and Systems Neuroscience meeting.
41. Wickens, T. D. (2002). Elementary signal detection theory. New York: Oxford University Press.
42. Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4(12):11, 1120-1135, http://journalofvision.org/4/12/11/, doi:10.1167/4.12.11.
43. Yantis, S. (1992). Multielement visual tracking: Attention and perceptual organization. Cognitive Psychology, 24, 295-340.
44. Yule, G. U. (1903). Notes on the theory of association of attributes in statistics. Biometrika, 2, 121-134.
45. Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453, 233-235.