A computational cognition model of perception, memory, and judgment

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• 作者：XiaoLan Fu (1)
  LianHong Cai (2)
  Ye Liu (1)
  Jia Jia (2)
  WenFeng Chen (1)
  Zhang Yi (3)
  GuoZhen Zhao (4)
  YongJin Liu (2)
  ChangXu Wu (4)
  
• 关键词：perception ; memory ; judgment ; computational cognition model
• 刊名：SCIENCE CHINA Information Sciences
• 出版年：2014
• 出版时间：February 2014
• 年：2014
• 卷：57
• 期：3
• 页码：1-15
• 全文大小：791 KB
• 参考文献：1. Gallistel C R, King A. Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience. New York: Blackwell/Wiley, 2009. iiv鈥搙vi
  2. Hu S M, Chen T, Xu K, et al. Internet visual media processing: a survey with graphics and vision applications. Vis Comput, 2013, 29: 393鈥?05
  3. Hulusic V, Debattista K, Aggarwal V, et al. Maintaining frame rate perception in interactive environments by exploiting audio-visual cross-modal interaction. Vis Comput, 2011, 27: 57鈥?6
  4. Vazquez P-P, Marco J. Using normalized compression distance for image similarity measurement: an experimental study. Vis Comput, 2012, 28: 1063鈥?084
  5. Eysenck M W, Keane M T. Cognitive Psychology: a Student鈥檚 Handbook. 6th ed. New York: Psychology Press, 2010. 1鈥?0
  6. National Institute on Drug Abuse. Computational neuroscience at the NIH. Nat Neurosci, 2000, 3: 1161鈥?164
  7. Buschman T J, Miller E K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science, 2007, 315: 1860鈥?862
  8. Navalpakkam V, Itti L. Search goal tunes visual features optimally. Neuron, 2007, 53: 605鈥?17
  9. Katsuki F, Constantinidis C. Early involvement of prefrontal cortex in visual bottom-up attention. Nat Neurosci, 2012, 15: 1160鈥?166
  10. Corbetta M, Shulman G L. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci, 2002, 3: 201鈥?15
  11. Zanto T P, Rubens M T, Thangavel A, et al. Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory. Nat Neurosci, 2011, 14: 656鈥?61
  12. Tomita H M, Ohbayashi K, Nakahara I, et al. Top-down signal from prefrontal cortex in executive control of memory retrieval. Nature, 1999, 401: 699鈥?03
  13. Itti L, Koch C. Computational modelling of visual attention. Nat Rev Neurosci, 2001, 2: 194鈥?03
  14. Cox D, Meyers E, Sinha P. Contextually evoked object-specific responses in human visual cortex. Science, 2004, 303: 115鈥?17
  15. Kouh M, Poggio T. A canonical neural circuit for cortical nonlinear operations. Neural Comput, 2008, 20: 1427鈥?451
  16. Poggio T, Bizzi E. Generalization in vision and motor control. Nature, 2004, 431: 768鈥?74
  17. Hung C P, Kreiman G, Poggio T, et al. Fast readout of object identity from macaque inferior temporal cortex. Science, 2005, 310: 863鈥?66
  18. Pylyshyn Z W. Computation and Cognition: toward a Foundation for Cognitive Science. Cambridge: The MIT Press, 1984. 1鈥?6
  19. Hasselmo M E, Sarter M. Modes and models of forebrain cholinergic neuromodulation of cognition. Neuropsychopharmacology, 2010, 36: 52鈥?3
  20. Tamietto M, de Gelder B. Neural bases of the non-conscious perception of emotional signals. Nat Rev Neurosci, 2010, 11: 697鈥?09
  21. Fries P, Reynolds J H, Rorie A E, et al. Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 2001, 291: 1560鈥?563
  22. Roberts M, Delicato L S, Herrero J, et al. Attention alters spatial integration in macaque V1 in an eccentricitydependent manner. Nat Neurosci, 2007, 10: 1483鈥?491
  23. Qiu F T, Sugihara T, von der Heydt R. Figure-ground mechanisms provide structure for selective attention. Nat Neurosci, 2007, 10: 1492鈥?499
  24. H眉bner R, Steinhauser M, Lehle C. A dual-stage two-phase model of selective attention. Psychol Rev, 2010, 117: 759鈥?84
  25. Gondan M, Blurton S P, Hughes F, et al. Effects of spatial and selective attention on basic multisensory integration. J Exp Phychol-Hum Percep Perf, 2011, 37: 1887鈥?897
  26. Schafer R J, Moore T. Selective attention from voluntary control of neurons in prefrontal cortex. Science, 2011, 332: 1568鈥?571
  27. Couperus J W. Perceptual load influences selective attention across development. Develop Psychol, 2011, 47: 1431鈥?439
  28. Cosman J D, Vecera S P. Object-based attention overrides perceptual load to modulate visual distraction. J Exp Phychol-Hum Percep Perf, 2012, 38: 576鈥?79
  29. Chen C C, Wu J K, Lin H W, et al. Visualizing long-term memory formation in two neurons of the drosophila brain. Science, 2012, 335: 678鈥?85
  30. Fell J, Axmacher N. The role of phase synchronization in memory processes. Nat Rev Neurosci, 2011, 12: 105鈥?18
  31. Fusi S, Abbott L F. Limits on the memory storage capacity of bounded synapses. Nat Neurosci, 2007, 10: 485鈥?93
  32. Eichenbaum H. A cortical-hippocampal system for declarative memory. Nat Rev Neurosci, 2000, 1: 41鈥?0
  33. McGaugh J L. Memory-a century of consolidation. Science, 2000, 287: 248鈥?51
  34. Tronson N C, Taylor J R. Molecular mechanisms of memory reconsolidation. Nat Rev Neurosci, 2007, 8: 262鈥?75
  35. Edelson M, Sharot T, Dolan R J, et al. Following the crowd: brain substrates of long-term memory conformity. Science, 2011, 333: 108鈥?11
  36. Frankland P W, Bontempi B. The organization of recent and remote memories. Nat Rev Neurosci, 2005, 6: 119鈥?30
  37. Nadel L, Hardt O. Update on memory systems and processes. Neuropsychopharmacology, 2011, 36: 251鈥?73
  38. Nader K, Hardt O. A single standard for memory: the case for reconsolidation. Nat Rev Neurosci, 2009, 10: 224鈥?34
  39. Gonzalez C, Dutt V. Instance-based learning: Integrating sampling and repeated decisions from experience. Psychol Rev, 2011, 118: 523鈥?51
  40. Homa D, Hout M C, Milliken L, et al. Bogus concerns about the false prototype enhancement effect. J Exp Psychol-Learn Mem Cogn, 2011, 37: 368鈥?77
  41. Smith J D, Minda J P. Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning. J Exp Psychol-Learn Mem Cogn, 2002, 28: 800鈥?11
  42. Smith J D, Redford J S, Haas S M. Prototype abstraction by monkeys (Macaca mulatta). J Exp Psychol-Gen, 2008, 137: 390鈥?01
  43. Lewandowsky S, Palmeri T J, Waldmann M R. Introduction to the special section on theory and data in categorization: integrating computational, behavioral, and cognitive neuroscience approaches. J Exp Psychol-Learn Mem Cogn, 2012, 38: 803鈥?06
  44. Freedman D J, Assad J A. A proposed common neural mechanism for categorization and perceptual decisions. Nat Neurosci, 2011, 14: 143鈥?46
  45. Gold J I, Shadlen M N. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J Neurosci, 2003, 23: 632鈥?51
  46. Kable J W, Glimcher P W. The neurobiology of decision: consensus and controversy. Neuron, 2009, 63: 733鈥?45
  47. Freedman D J, Assad J A. Experience-dependent representation of visual categories in parietal cortex. Nature, 2006, 443: 85鈥?8
  48. Freedman D J, Riesenhuber M, Poggio T, et al. Categorical representation of visual stimuli in the primate prefrontal cortex. Science, 2001, 291: 312鈥?16
  49. Freedman D J, Riesenhuber M, Poggio T, et al. A comparison of primate prefrontal and inferior temporal cortices during visual categorization. J Neurosci, 2003, 23: 5235鈥?246
  50. Williams Z M, Elfar J C, Eskandar E N, et al. Parietal activity and the perceived direction of ambiguous apparent motion. Nat Neurosci, 2003, 6: 616鈥?23
  51. Toth L J, Assad J A. Dynamic coding of behaviourally relevant stimuli in parietal cortex. Nature, 2002, 415: 165鈥?68
  52. Stoet G, Snyder L H. Single neurons in posterior parietal cortex of monkeys encode cognitive set. Neuron, 2004, 42: 1003鈥?012
  53. Gottlieb J. From thought to action: the parietal cortex as a bridge between perception, action, and cognition. Neuron, 2007, 53: 9鈥?6
  54. Chen Y, Martinez-Conde S, Macknik S L, et al. Task difficulty modulates the activity of specific neuronal populations in primary visual cortex. Nat Neurosci, 2008, 11: 974鈥?82
  55. Asplund C L, Todd J J, Snyder A P, et al. A central role for the lateral prefrontal cortex in goal-directed and stimulus-driven attention. Nat Neurosci, 2010, 13: 507鈥?12
  56. Solway A, Botvinick M M. Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. Psychol Rev, 2012, 119: 120鈥?54
  57. Purcell B A, Heitz R P, Cohen J Y, et al. Neurally constrained modeling of perceptual decision making. Psychol Rev, 2010, 117: 1113鈥?143
  58. Palmeri T J, Gauthier I. Visual object understanding. Nat Rev Neurosci, 2004, 5: 291鈥?04
  59. Peyrin C, Michel C M, Schwartz S, et al. The neural substrates and timing of top-down processes during coarse-to-fine categorization of visual scenes: a combined fMRI and ERP study. J Cognitive Neurosci, 2010, 22: 2768鈥?780
  60. Gao Z F, Bentin S. Coarse-to-fine encoding of spatial frequency information into visual short-term memory for faces but impartial decay. J Exp Phychol-Hum Percep Perf, 2011, 37: 1051鈥?064
  61. Goffaux V, Peters J, Haubrechts J, et al. From coarse to fine? Spatial and temporal dynamics of cortical face processing. Cereb Cortex, 2011, 21: 467鈥?76
  62. Griffiths O, Mitchell C J. Selective attention in human associative learning and recognition memory. J Exp Psychol-Gen, 2008, 137: 626鈥?48
  63. Deng W, Aimone J B, Gage F H. New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory? Nat Rev Neurosci, 2010, 11: 339鈥?50
  64. De Fockert J W, Rees G, Frith C D, et al. The role of working memory in visual selective attention. Science, 2001, 291: 1803鈥?806
  65. Saalmann Y B, Pigarev I N, Vidyasagar T R. Neural mechanisms of visual attention: how top-down feedback highlights relevant locations. Science, 2007, 316: 1612鈥?615
  66. Sigala N, Logothetis N K. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature, 2002, 415: 318鈥?20
  67. Kundel H L, Nodine C F. Interpreting chest radiographs without visual search. Radiology, 1975, 116: 527鈥?32
  68. Treisman A M, Gelade G. A feature-integration theory of attention. Cog Psychol, 1980, 12: 97鈥?36
  69. Liu Y J, Fu Q F, Liu Y, et al. 2D-line-drawing-based 3D object recognition. In: Computational Visual Media, Beijing, 2012. 146鈥?53
  70. Liu Y J, Luo X, Joneja A, et al. User-adaptive sketch-based 3D CAD model retrieval. IEE Trans Autom Sci Eng, 2013, 99: 1鈥?3
  71. Wolfe J M. Guided Search 4.0: current progress with a model of visual search. In: Integrated Models of Cognitive Systems. New York: Oxford, 2007. 99鈥?19
  72. Wolfe J M, Cave K R, Franzel S L. Guided search: an alternative to feature integration model for visual search. J Exp Phychol-Hum Percep Perf, 1989, 15: 419鈥?33
  73. Williams C C, Henderson J M, Zacks R T. Incidental visual memory for targets and distractors in visual search. Percept Psychophys, 2005, 67: 816鈥?27
  74. Wolfe J M. Guided search 2.0: a revised model of visual search. Psychonomic Bull Rev, 1994, 1: 202鈥?38
  75. Hao F, Zhang H, Fu X L. Modulation of attention by faces expressing emotion: evidence from visual marking. In: Tao J H, Tan T N, Picard R W, eds. Affective Computing and Intelligent Interaction. Berlin/Heidelberg: Springer-Verlag, 2005. 127鈥?34
  76. Hao F, Fu X L. Visual marking: a mechanism of prioritizing selection. Adv Psychol Sci, 2006, 14: 7鈥?1
  77. Sternberg S. High-speed scanning in human memory. Science, 1966, 153: 652鈥?54
  78. Hawkins J, Blakeslee S. On Intelligence. New York: Times Books, 2004
  79. Bear M F, Connors B W, Paradiso M A. Neuroscience: exploring the brain. 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2006
  80. Rinkus G J. A cortical sparse distributed coding model linking mini鈥檃nd macrocolumn-scale functionality. Front Neuroanat, 2010, 4: 17
  81. Mountcastle V. An Organizing Principle for Cerebral Function: the Unit Model and the Distributed System. Cambridge: MIT Press, 1978
  82. Elad M. Sparse and Redundant Representations: from Theory to Applications in Signal and Image Processing. New York: Springer, 2010
  83. Bruckstein A M, Donoho D L, Elad M. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev, 2009, 51: 34鈥?1
  84. Yi Z, Tan K K. Convergence Analysis of Recurrent Neural Networks. Dordrecht: Kluwer Academic Publishers, 2004
  85. Tang H J, Tan K C, Yi Z. Neural Networks: Computational Models and Applications. Heidelberg: Springer-Verlag, 2007
  86. Seung H S. How the brain keeps the eyes still. Proc Nat Acad Sci USA, 1996, 93: 13339鈥?3344
  87. Wu S, Amari S, Nakahara H. Population coding and decoding in a neural field: a computational study. Neural Comput, 2002, 14: 999鈥?026
  88. Zhang K. Representation of spatial orientation by the intrinsic dynamics of head-direction cell ensembles: a theory. J Neurosci, 1996, 16: 2112鈥?126
  89. Yu J, Yi Z, Zhang L. Representations of continuous attractors of recurrent neural networks. IEEE Trans Neural Netw, 2009, 20: 368鈥?72
  90. Wu C, Liu Y. Queuing network modeling of the psychological refractory period (PRP). Psychol Rev, 2008, 115: 913鈥?54
  91. Johnson J G, Busemeyer J R. Rule-based decision field theory: a dynamic computational model of transitions among decision-making strategies. In: Betsch T, Haberstroh S, eds. The Routines of Decision Making. Mahwah: Lawrence Erlbaum, 2005. 3鈥?9
  92. Zhao G, Wu C, Qiao C. A mathematical model for the prediction of speeding with its validation. IEEE Trans Intell Transp Syst, 2013, 14: 828鈥?36
  93. Wang X H, Jia J, Hu P Y, et al. Understanding the emotional impact of image. In: ACM Multimedia, Nara, 2012. 1369鈥?370
  94. Jia J, Wu S, Wang X H, et al. Can we understand van Gogh鈥檚 mood? Learning to infer affects from images in social networks. In: ACM Multimedia, Nara, 2012. 857鈥?60
  95. Wang X H, Jia J, Cai L H. Affective image adjustment with a single word. Vis Comput, 2013, 29: 1121鈥?133
  96. Wang X H, Jia J, Liao H Y, et al. Affective image colorization. J Comput Sci Technol, 2012, 27: 1119鈥?128
  97. Kobayashi S. Art of Color Combinations. Tokyo: Kodansha International, 1995
  98. Zhang Y F, Hu S M, Martin R R. Shrinkability maps for content-aware video resizing. Comput Graph Forum, 2008, 27: 1797鈥?804
  99. Zhang G X, Cheng M M, Hu S M, et al. A shape-preserving approach to image resizing. Comput Graph Forum, 2009, 28: 1897鈥?906
  100. Dahan M J, Chen N, Shamir A, et al. Combining color and depth for enhanced image segmentation and retargeting. Vis Comput, 2012, 28: 1181鈥?193
  101. Liu Y J, Luo X, Xuan Y M, et al. Image retargeting quality assessment. Comput Graph Forum, 2011, 30: 583鈥?92
  102. Chen L. Topological structure in visual perception. Science, 1982, 218: 699鈥?00
  103. Anderson J R, Bothell D, Byrne M D, et al. An integrated theory of the mind. Psychol Rev, 2004, 111: 1036鈥?060
  
• 作者单位：XiaoLan Fu (1)
  LianHong Cai (2)
  Ye Liu (1)
  Jia Jia (2)
  WenFeng Chen (1)
  Zhang Yi (3)
  GuoZhen Zhao (4)
  YongJin Liu (2)
  ChangXu Wu (4)
  
  1. State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
  2. TNLIST, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
  3. College of Computer Science, Sichuan University, Chengdu, 610064, China
  4. Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
  
• ISSN：1869-1919

文摘

The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.