Factorial comparison of working memory models

Psychological Review

Volumn 121, Issue 1, 2014, Pages 124-149

  van den Berg, Ronald ^a,b   Awh, Edward ^c   Ma, Wei Ji ^b,d  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b BAYLOR COLLEGE OF MEDICINE   (United States)

^c UNIVERSITY OF OREGON   (United States)

^d NEW YORK UNIVERSITY   (United States)

Author keywords

Capacity; Model comparison; Resource models; Short term memory; Working memory

Indexed keywords

ARTICLE; ATTENTION; COMPARATIVE STUDY; FACTORIAL ANALYSIS; HUMAN; LONG TERM MEMORY; NEUROPSYCHOLOGICAL TEST; PATTERN RECOGNITION; PHYSIOLOGY; PSYCHOLOGICAL MODEL; PSYCHOMOTOR PERFORMANCE; REACTION TIME; SHORT TERM MEMORY; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICAL MODEL; STATISTICS;

ATTENTION; DATA INTERPRETATION, STATISTICAL; FACTOR ANALYSIS, STATISTICAL; HUMANS; LIKELIHOOD FUNCTIONS; MEMORY, SHORT-TERM; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; NEUROPSYCHOLOGICAL TESTS; PATTERN RECOGNITION, VISUAL; PSYCHOMOTOR PERFORMANCE; REACTION TIME; RETENTION (PSYCHOLOGY); STATISTICAL DISTRIBUTIONS;

EID: 84893454173     PISSN: 0033295X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0035234     Document Type: Article

References (57)

1. Acerbi, L., Wolpert, D. M., & Vijayakumar, S. (2012). Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing. PLoS Computational Biology, 8(11), Article e1002771. doi:10.1371/journal.pcbi.1002771
2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723. doi:10.1109/TAC.1974.1100705
3. Anderson, D. E., & Awh, E. (2012). The plateau in mnemonic resolution across large set sizes indicates discrete resource limits in visual working memory. Attention, Perception, & Psychophysics, 74, 891-910. doi: 10.3758/s13414-012-0292-1
4. Anderson, D. E., Vogel, E. K., & Awh, E. (2011). Precision in visual working memory reaches a stable plateau when individual item limits are exceeded. The Journal of Neuroscience, 31, 1128-1138. doi: 10.1523/JNEUROSCI.4125-10.2011
5. Bae, G.-Y., Wilson, C., & Flombaum, J. (2013, May). Variability in color working memory precision reflects inherent stimulus properties. Paper presented at the annual Vision Sciences Society meeting, Naples, F.L.
6. Bays, P. M. (2013, November). Noise in neural populations accounts for errors in visual working memory. Poster presented at the Neuroscience 2013 meeting, San Diego, C.A.
7. 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. doi:10.1167/9.10.7
8. Bays, P. M., & Husain, M. (2008, August 8). Dynamic shifts of limited working memory resources in human vision. Science, 321, 851-854. doi:10.1126/science.1158023
9. Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science, 22, 384-392. doi:10.1177/0956797610397956
10. Brady, T. F., & Tenenbaum, J. B. (2013). A probabilistic model of visual working memory: Incorporating higher order regularities into working memory capacity estimates. Psychological Review, 120, 85-109. doi: 10.1037/a0030779
11. Buschman, T. J., Siegel, M., Roy, R. E., & Miller, E. K. (2011). Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences, USA, 108, 11252-11255. doi:10.1073/pnas.1104666108
12. Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011). Variance as a signature of neural computations during decision-making. Neuron, 69, 818-831. doi:10.1016/j.neuron.2010.12.037
13. Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S.,. Shenoy, K. V. (2010). Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nature Neuroscience, 13, 369-378. doi:10.1038/nn.2501
14. Cohen, M. R., & Maunsell, J. H. R. (2010). A neuronal population measure of attention predicts behavioral performance on individual trials. The Journal of Neuroscience, 30, 15241-15253. doi:10.1523/JNEUROSCI.2171-10.2010
15. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87-114. doi:10.1017/S0140525X01003922
16. Daunizeau, J., Preuschoff, K., Friston, K., & Stephan, K. (2011). Optimizing experimental design for comparing models of brain function. PLoS Computational Biology, 7(11), Article e1002280. doi:10.1371/journal.pcbi.1002280
17. Donkin, C., Nosofsky, R. M., Gold, J. M., & Shiffrin, R. M. (2013). Discrete-slots models of visual working-memory response times. Psychological Review. Advance online publication. doi:10.1037/a0034247
18. Dyrholm, M., Kyllingsbaek, S., Espeseth, T., & Bundesen, C. (2011). Generalizing parametric models by introducing trial-by-trial parameter variability: The case of TVA. Journal of Mathematical Psychology, 55, 416-429. doi:10.1016/j.jmp.2011.08.005
19. Elmore, L. C., Ma, W. J., Magnotti, J. F., Leising, K. J., Passaro, A. D., Katz, J. S., & Wright, A. A. (2011). Visual short-term memory compared in rhesus monkeys and humans. Current Biology, 21, 975-979. doi:10.1016/j.cub.2011.04.031
20. Ester, E. F., Anderson, D. E., Serences, J. T., & Awh, E. (2013). A neural measure of precision in visual working memory. Journal of Cognitive Neuroscience, 25, 754-761. doi:10.1162/jocn_a_00357
21. Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503-513.
22. Fougnie, D., Suchow, J. W., & Alvarez, G. A. (2012a). Variability in the quality of visual working memory. Nature Communications, 3, Article 1229. doi:10.1038/ncomms2237
23. Fougnie, D., Suchow, J. W., & Alvarez, G. A. (2012b, May). The volatility of working memory. Paper presented at the Vision Sciences Society annual meeting, Naples, F.L.
24. Girshick, A. R., Landy, M. S., & Simoncelli, E. P. (2011). Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience, 14, 926-932. doi:10.1038/nn.2831
25. Goris, R. L., Movshon, J. A., & Simoncelli, E. P. (2013). Response variability of visual cortical neurons explained by a modulated Poisson model. Paper presented at the annual meeting of the Society for Neuroscience, San Diego, CA. http://www.cns.nyu.edu/~lcv/pubs/makeAbs.php?loc=Goris13b
26. Hess, R. F., & Hayes, A. (1994). The coding of spatial position by the human visual system: Effects of spatial scale and retinal eccentricity. Vision Research, 34, 625-643. doi:10.1016/0042-6989(94)90018-3]
27. Jeffreys, H. (1961). The theory of probability (3rd ed.). Oxford, England: Clarendon Press.
28. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795. doi:10.1080/01621459.1995.10476572
29. Keshvari, S., Van den Berg, R., & Ma, W. J. (2013). No evidence for an item limit in change detection. PLoS Computational Biology, 9(2), Article e1002927. doi:10.1371/journal.pcbi.1002927
30. Lara, A. H., & Wallis, J. D. (2012). Capacity and precision in an animal model of short-term memory. Journal of Vision, 12(3), Article 13. doi:10.1167/12.3.13
31. Levi, D. M. (2008). Crowding-an essential bottleneck for object recognition: A mini-review. Vision Research, 48, 635-654. doi:10.1016/j.visres.2007.12.009
32. Luck, S. J., & Vogel, E. K. (1997, November 20). The capacity of visual working memory for features and conjunctions. Nature, 390, 279-281. doi:10.1038/36846
33. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9, 1432-1438. doi:10.1038/nn1790
34. Mardia, K. V., & Jupp, P. E. (1999). Directional statistics. West Sussex, United Kingdom: Wiley. doi:10.1002/9780470316979
35. Mazyar, H., Van den Berg, R., & Ma, W. J. (2012). Does precision decrease with set size? Journal of Vision, 12(6), Article 10. doi:10.1167/12.6.10
36. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. doi:10.1037/h0043158
37. Nienborg, H., & Cumming, B. G. (2009, May 7). Decision-related activity in sensory neurons reflects more than a neuron's causal effect. Nature, 459, 89-92. doi:10.1038/nature07821
38. Orhan, A. E., & Jacobs, R. A. (2013). A probabilistic clustering theory of visual short-term memory. Psychological Review, 120, 297-328. doi: 10.1037/a0031541
39. Palmer, J. (1990). Attentional limits on the perception and memory of visual information. Journal of Experimental Psychology: Human Perception and Performance, 16, 332-350. doi:10.1037/0096-1523.16.2.332
40. Paradiso, M. A. (1988). A theory of the use of visual orientation information which exploits the columnar structure of striate cortex. Biological Cybernetics, 58, 35-49. doi:10.1007/BF00363954
41. Pashler, H. (1988). Familiarity and visual change detection. Perception & Psychophysics, 44, 369-378. doi:10.3758/BF03210419
42. Pinto, N., Doukhan, D., DiCarlo, J. J., & Cox, D. D. (2009). A highthroughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology, 5(11), Article e1000579. doi:10.1371/journal.pcbi.1000579
43. Popper, K. (1959). The logic of scientific discovery. New York, NY: Basic Books.
44. Prinzmetal, W., Amiri, H., Allen, K., & Edwards, T. (1998). Phenomenology of attention: I. Color, location, orientation, and spatial frequency. Journal of Experimental Psychology: Human Perception and Performance, 24, 261-282. doi:10.1037/0096-1523.24.1.261
45. Rademaker, R. L., Tredway, C. H., & Tong, F. (2012). Introspective judgments predict the precision and likelihood of successful maintenance of visual working memory. Journal of Vision, 12(13), Article 21. doi:10.1167/12.13.21
46. 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. doi:10.1073/pnas.0711295105
47. Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464. doi:10.1214/aos/1176344136
48. Seung, H. S., & Sompolinsky, H. (1993). Simple model for reading neuronal population codes. Proceedings of National Academy of Sciences, USA, 90, 10749-10753. doi:10.1073/pnas.90.22.10749
49. Shafi, M., Zhou, Y., Quintana, J., Chow, C., Fuster, J., & Bodner, M. (2007). Variability in neuronal activity in primate cortex during working memory tasks. Neuroscience, 146, 1082-1108. doi:10.1016/j.neuroscience.2006.12.072
50. Shaw, M. L. (1980). Identifying attentional and decision-making components in information processing. In R. S. Nickerson (Ed.), Attention and performance VIII (pp. 277-296). Hillsdale, NJ: Erlbaum.
51. Sims, C. R., Jacobs, R. A., & Knill, D. C. (2012). An ideal-observer analysis of visual working memory. Psychological Review, 119, 807-830. doi:10.1037/a0029856
52. Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983). The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research, 23, 775-785. doi:10.1016/0042-6989(83)90200-6
53. Van den Berg, R., & Ma, W. J., (in press). "Plateau"-related summary statistics are uninformative for comparing working memory model. Attention, Perception, & Psychophysics.
54. Van den Berg, R., Shin, H., Chou, W.-C., George, R., & Ma, W. J. (2012). Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences, USA, 109, 8780-8785. doi:10.1073/pnas.1117465109
55. Wheeler, M. E., & Treisman, A. M. (2002). Binding in short-term visual memory. Journal of Experimental Psychology: General, 131, 48-64. doi:10.1037/0096-3445.131.1.48
56. Wilken, P., & Ma, W. J. (2004). A detection theory account of change detection. Journal of Vision, 4(12), Article 11. doi:10.1167/4.12.11
57. Zhang, W., & Luck, S. J. (2008, May 8). Discrete fixed-resolution representations in visual working memory. Nature, 453, 233-235. doi: 10.1038/nature06860