Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Ince, Robin A A ^a   Van Rijsbergen, Nicola J ^a   Thut, Gregor ^a   Rousselet, Guillaume A ^a   Gross, Joachim ^a   Panzeri, Stefano ^a,b   Schyns, Philippe G ^a  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; HUMAN; HUMAN EXPERIMENT; MODEL; PERCEPTION; ALGORITHM; BIOLOGICAL MODEL; BRAIN MAPPING; COGNITION; NERVE CELL NETWORK; PHYSIOLOGY;

ALGORITHMS; BRAIN; BRAIN MAPPING; COGNITION; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; PERCEPTION;

EID: 84949267268     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep17681     Document Type: Article

References (67)

1. Turing, A. M. Computing Machinery and Intelligence. Mind 59, 433-460 (1950).
2. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. (W. H. Freeman, 1982).
3. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198 (2009).
4. Friston, K. & Kiebel, S. Predictive coding under the free-energy principle. Philos. Trans. R. Soc. B Biol. Sci. 364, 1211-1221 (2009).
5. Mumford, D. On the computational architecture of the neocortex. Biol. Cybern. 66, 241-251 (1992).
6. Clark, A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181-204 (2013).
7. Ashby, F. G. & Maddox, W. T. Human Category Learning. Annu. Rev. Psychol. 56, 149-178 (2005).
8. Gold, J. I. & Shadlen, M. N. The Neural Basis of Decision Making. Annu. Rev. Neurosci. 30, 535-574 (2007).
9. Summerfield, C. & de Lange, F. P. Expectation in perceptual decision making: neural and computational mechanisms. Nat. Rev. Neurosci. 15, 745-756 (2014).
10. Haxby, J. V., Connolly, A. C. & Guntupalli, J. S. Decoding Neural Representational Spaces Using Multivariate Pattern Analysis. Annu. Rev. Neurosci. 37, 435-456 (2014).
11. Kriegeskorte, N. & Kievit, R. A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17, 401-412 (2013).
12. Gosselin, F. & Schyns, P. G. Bubbles: a technique to reveal the use of information in recognition tasks. Vision Res. 41, 2261-2271 (2001).
13. Smith, M. L., Gosselin, F. & Schyns, P. G. Perceptual Moments of Conscious Visual Experience Inferred from Oscillatory Brain Activity. Proc. Natl. Acad. Sci. 103, 5626-5631 (2006).
14. Schyns, P. G., Bonnar, L. & Gosselin, F. Show Me the Features! Understanding Recognition From the Use of Visual Information. Psychol. Sci. 13, 402-409 (2002).
15. Murray, R. F. Classification images: A review. J. Vis. 11, 2 (2011).
16. Cover, T. M. & Thomas, J. A. Elements of information theory. (Wiley New York, 1991).
17. Makeig, S., Bell, A. J., Jung, T.-P. & Sejnowski, T. J. Independent component analysis of electroencephalographic data. Adv. Neural Inf. Process. Syst. 145-151 (1996).
18. Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791 (1999).
19. Diestel, R. Graph theory. 2005. Grad Texts Math (2005).
20. Grill-Spector, K. & Weiner, K. S. The functional architecture of the ventral temporal cortex and its role in categorization. Nat. Rev. Neurosci. 15, 536-548 (2014).
21. Cichy, R. M., Pantazis, D. & Oliva, A. Resolving human object recognition in space and time. Nat. Neurosci. 17, 455-462 (2014).
22. Schyns, P. G., Petro, L. S. & Smith, M. L. Dynamics of Visual Information Integration in the Brain for Categorizing Facial Expressions. Curr. Biol. 17, 1580-1585 (2007).
23. Kriegeskorte, N., Goebel, R. & Bandettini, P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. USA 103, 3863-3868 (2006).
24. Panzeri, S., Macke, J. H., Gross, J. & Kayser, C. Neural population coding: combining insights from microscopic and mass signals. Trends Cogn. Sci. 19, 162-172 (2015).
25. Salmelin, R. & Kujala, J. Neural representation of language: activation versus long-range connectivity. Trends Cogn. Sci. 10, 519-525 (2006).
26. Friston, K. J. Functional and Effective Connectivity: A Review. Brain Connect. 1, 13-36 (2011).
27. Kiebel, S. J., Garrido, M. I., Moran, R., Chen, C.-C. & Friston, K. J. Dynamic causal modeling for EEG and MEG. Hum. Brain Mapp. 30, 1866-1876 (2009).
28. Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. NeuroImage 19, 1273-1302 (2003).
29. Rousselet, G. A., Ince, R. A. A., Rijsbergen, N. J. van & Schyns, P. G. Eye coding mechanisms in early human face event-related potentials. J. Vis. 14, 7 (2014).
30. Schyns, P. G., Jentzsch, I., Johnson, M., Schweinberger, S. R. & Gosselin, F. A principled method for determining the functionality of brain responses. Neuroreport 14, 1665-1669 (2003).
31. Smith, M. L., Gosselin, F. & Schyns, P. G. Receptive Fields for Flexible Face Categorizations. Psychol. Sci. 15, 753-761 (2004).
32. Ratcliff, R., Philiastides, M. G. & Sajda, P. Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proc. Natl. Acad. Sci. 106, 6539-6544 (2009).
33. Bentin, S., Allison, T., Puce, A., Perez, E. & McCarthy, G. Electrophysiological Studies of Face Perception in Humans. J. Cogn. Neurosci. 8, 551-565 (1996).
34. Bentin, S. et al. Controlling interstimulus perceptual variance does not abolish N170 face sensitivity. Nat. Neurosci. 10, 801-802 (2007).
35. Liu, J., Higuchi, M., Marantz, A. & Kanwisher, N. The selectivity of the occipitotemporal M170 for faces. Neuroreport 11, 337-341 (2000).
36. Kanwisher, N., McDermott, J. & Chun, M. M. The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. J. Neurosci. 17, 4302-4311 (1997).
37. Meng, M., Cherian, T., Singal, G. & Sinha, P. Lateralization of face processing in the human brain. Proc. R. Soc. Lond. B Biol. Sci. 279, 2052-2061 (2012).
38. Philiastides, M. G., Ratcliff, R. & Sajda, P. Neural Representation of Task Difficulty and Decision Making during Perceptual Categorization: A Timing Diagram. J. Neurosci. 26, 8965-8975 (2006).
39. Brimijoin, W. O., Akeroyd, M. A., Tilbury, E. & Porr, B. The internal representation of vowel spectra investigated using behavioral response-triggered averaging. J. Acoust. Soc. Am. 133, EL118-EL122 (2013).
40. Grenander, U. General Pattern Theory: A Mathematical Study of Regular Structures. (Clarendon Press Oxford Mathematical Monographs, 1994).
41. Yu, H., Garrod, O. G. B. & Schyns, P. G. Perception-driven facial expression synthesis. Comput. Graph. 36, 152-162 (2012).
42. Jack, R. E., Garrod, O. G. B. & Schyns, P. G. Dynamic Facial Expressions of Emotion Transmit an Evolving Hierarchy of Signals over Time. Curr. Biol. 24, 187-192 (2014).
43. Einevoll, G. T., Kayser, C., Logothetis, N. K. & Panzeri, S. Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat. Rev. Neurosci. 14, 770-785 (2013).
44. Schyns, P. G., Thut, G. & Gross, J. Cracking the Code of Oscillatory Activity. PLoS Biol 9, e1001064 (2011).
45. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Intell Neurosci. 2011, 1:1-1:9 (2011).
46. Gross, J. et al. Good practice for conducting and reporting MEG research. NeuroImage 65, 349-363 (2013).
47. Makeig, S., Debener, S., Onton, J. & Delorme, A. Mining event-related brain dynamics. Trends Cogn. Sci. 8, 204-210 (2004).
48. Delorme, A., Palmer, J., Onton, J., Oostenveld, R. & Makeig, S. Independent EEG Sources Are Dipolar. PLoS ONE 7, e30135 (2012).
49. Jung, T. P. et al. Imaging brain dynamics using independent component analysis. Proc. IEEE 89, 1107-1122 (2002).
50. Makeig, S. et al. Dynamic Brain Sources of Visual Evoked Responses. Science 295, 690-694 (2002).
51. Ince, R. A. A., Mazzoni, A., Bartels, A., Logothetis, N. K. & Panzeri, S. A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features. J. Neurosci. Methods 210, 49-65 (2012).
52. Magri, C., Whittingstall, K., Singh, V., Logothetis, N. K. & Panzeri, S. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci. 10, 81 (2009).
53. Misra, N., Singh, H. & Demchuk, E. Estimation of the entropy of a multivariate normal distribution. J. Multivar. Anal. 92, 324-342 (2005).
54. Nelsen, R. B. An introduction to copulas. (Springer, 2007).
55. Ma, J. & Sun, Z. Mutual Information Is Copula Entropy. Tsinghua Sci. Technol. 16, 51-54 (2011).
56. Holmes, A. P., Blair, R. C., Watson, G. & Ford, I. Nonparametric Analysis of Statistic Images from Functional Mapping Experiments. J. Cereb. Blood Flow Metab. 16, 7-22 (1996).
57. Massey, J. Causality feedback and directed information. in Proc. Int. Symp. Inf. Theory Applic.(ISITA-90) 303-305 (Citeseer, 1990).
58. Wiener, N. The theory of prediction. 1, (McGraw-Hill: New York, NY, USA, 1956).
59. Granger, C. W. Investigating causal relations by econometric models and cross-spectral methods. Econom. J. Econom. Soc. 424-438 (1969).
60. Bressler, S. L. & Seth, A. K. Wiener-Granger Causality: A well established methodology. NeuroImage 58, 323-329 (2011).
61. Schreiber, T. Measuring Information Transfer. Phys Rev Lett 85, 461-464 (2000).
62. Amblard, P.-O. & Michel, O. J. J. On directed information theory and Granger causality graphs. J. Comput. Neurosci. 30, 7-16 (2010).
63. Pola, G., Thiele, A., Hoffmann, K. P. & Panzeri, S. An exact method to quantify the information transmitted by different mechanisms of correlational coding. Netw. Comput. Neural Syst. 14, 35-60 (2003).
64. Schneidman, E., Bialek, W. & Berry, M. J. Synergy, Redundancy, and Independence in Population Codes. J. Neurosci. 23, 11539-11553 (2003).
65. Williams, P. L. & Beer, R. D. Nonnegative Decomposition of Multivariate Information. arXiv:1004.2515 (2010). at http://arxiv. org/abs/1004.2515.
66. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059-1069 (2010).
67. Kötter, R. & Stephan, K. E. Network participation indices: characterizing component roles for information processing in neural networks. Neural Netw. 16, 1261-1275 (2003).