CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Moore, C. S. Control of the memory image. Psychol. Rev. Monogr. Suppl. 4, 277–306 (1903).


    Google Scholar
     

  • 2.

    Perky, C. W. An experimental study of imagination. Am. J. Psychol. 21, 422–452 (1910).

    Article 

    Google Scholar
     

  • 3.

    Jacobson, E. Electrophysiology of mental activities. Am. J. Psychol. 44, 677–694 (1932).

    Article 

    Google Scholar
     

  • 4.

    Neisser, U. Cognitive psychology (Appleton-Century-Crofts, New York, 1967).


    Google Scholar
     

  • 5.

    Hebb, D. O. Concerning imagery. Psychol. Rev. 75, 466 (1968).

    CAS 
    Article 

    Google Scholar
     

  • 6.

    Brandt, S. A. & Stark, L. W. Spontaneous eye movements during visual imagery reflect the content of the visual scene. J. Cognit. Neurosci. 9, 27–38. https://doi.org/10.1162/jocn.1997.9.1.27 (1997).

    CAS 
    Article 

    Google Scholar
     

  • 7.

    Johansson, R., Holsanova, J. & Holmqvist, K. Pictures and spoken descriptions elicit similar eye movements during mental imagery, both in light and in complete darkness. Cognit. Sci. 30, 1053–1079. https://doi.org/10.1207/s15516709cog0000_86 (2006).

    Article 

    Google Scholar
     

  • 8.

    Johansson, R., Holsanova, J. & Holmqvist, K. The dispersion of eye movements during visual imagery is related to individual differences in spatial imagery ability. Proc. Cognit. Sci. Soc. 33, 1 (2011).


    Google Scholar
     

  • 9.

    Johansson, R. & Johansson, M. Look here, eye movements play a functional role in memory retrieval. Psychol. Sci. 25, 236–242. https://doi.org/10.1177/0956797613498260 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • 10.

    Laeng, B. & Teodorescu, D.-S. Eye scanpaths during visual imagery reenact those of perception of the same visual scene. Cognit. Sci. 26, 207–231. https://doi.org/10.1207/s15516709cog2602_3 (2002).

    Article 

    Google Scholar
     

  • 11.

    Laeng, B., Bloem, I. M., Dascenzo, S. & Tommasi, L. Scrutinizing visual images: The role of gaze in mental imagery and memory. Cognition 131, 263–283. https://doi.org/10.1016/j.cognition.2014.01.003 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • 12.

    Richardson, D. C. & Spivey, M. J. Representation, space and hollywood squares: Looking at things that are not there anymore. Cognition 76, 269–295 (2000).

    CAS 
    Article 

    Google Scholar
     

  • 13.

    Scholz, A., Mehlhorn, K. & Krems, J. F. Listen up, eye movements play a role in verbal memory retrieval. Psychol. Res. 80, 149–158 (2016).

    Article 

    Google Scholar
     

  • 14.

    Spivey, M. J. & Geng, J. J. Oculomotor mechanisms activated by imagery and memory: eye movements to absent objects. Psychol. Res. 65, 235–241 (2001).

    CAS 
    Article 

    Google Scholar
     

  • 15.

    Johansson, R., Holsanova, J., Dewhurst, R. & Holmqvist, K. Eye movements during scene recollection have a functional role, but they are not reinstatements of those produced during encoding. J. Exp. Psychol. Hum. Percept. Perform. 38, 1289–1314 (2012).

    Article 

    Google Scholar
     

  • 16.

    Altmann, G. T. Language-mediated eye movements in the absence of a visual world: the blank screen paradigm. Cognition 93, B79–B87 (2004).

    Article 

    Google Scholar
     

  • 17.

    Ferreira, F., Apel, J. & Henderson, J. M. Taking a new look at looking at nothing. Trends Cognit. Sci. 12, 405–410 (2008).

    Article 

    Google Scholar
     

  • 18.

    Richardson, D. . C., Altmann, G. . T., Spivey, M. . J. & Hoover, M. . A. Much ado about eye movements to nothing: a response to ferreira et al.: taking a new look at looking at nothing. Trends Cognit. Sci. 13, 235–236 (2009).

    Article 

    Google Scholar
     

  • 19.

    Mulder, T., Zijlstra, S., Zijlstra, W. & Hochstenbach, J. The role of motor imagery in learning a totally novel movement. Exp. Brain Res. 154, 211–217 (2004).

    Article 

    Google Scholar
     

  • 20.

    Scholz, A., Klichowicz, A. & Krems, J. F. Covert shifts of attention can account for the functional role of eye movements to nothing. Memory Cognit. 1, 1–14 (2017).


    Google Scholar
     

  • 21.

    Vredeveldt, A., Tredoux, C. G., Kempen, K. & Nortje, A. Eye remember what happened: eye-closure improves recall of events but not face recognition. Appl. Cognit. Psychol. 29, 169–180. https://doi.org/10.1002/acp.3092 (2015).

    Article 

    Google Scholar
     

  • 22.

    Mastroberardino, S. & Vredeveldt, A. Eye-closure increases childrens memory accuracy for visual material. Front. Psychol. 5, 241. https://doi.org/10.3389/fpsyg.2014.00241 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 23.

    Gbadamosi, J. & Zangemeister, W. H. Visual imagery in hemianopic patients. J. Cognit. Neurosci. 13, 855–866 (2001).

    CAS 
    Article 

    Google Scholar
     

  • 24.

    Blazhenkova, O. & Kozhevnikov, M. The new object-spatial-verbal cognitive style model: theory and measurement. Appl. Cognit. Psychol. 23, 638–663 (2009).

    Article 

    Google Scholar
     

  • 25.

    Wang, X. et al. The mental image revealed by gaze tracking. vol. 9 of CHI Conference on Human Factors in Computing Systems Proceedings, https://doi.org/10.1145/3290605.3300839 (ACM, 2019).

  • 26.

    Shen, G., Horikawa, T., Majima, K. & Kamitani, Y. Deep image reconstruction from human brain activity. bioRxiv
    https://doi.org/10.1101/240317 (2017).

  • 27.

    Schroff, F., Kalenichenko, D. & Philbin, J. Facenet: A unified embedding for face recognition and clustering. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, 815–823 (2015).

  • 28.

    Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679 (2005).

    CAS 
    Article 

    Google Scholar
     

  • 29.

    Harrison, S. A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632. https://doi.org/10.1038/nature07832 (2009).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 30.

    Du, C., Du, C. & He, H. Sharing deep generative representation for perceived image reconstruction from human brain activity. In Neural Networks (IJCNN), 2017 International Joint Conference on, 1049–1056 (IEEE, 2017).

  • 31.

    Nemrodov, D., Niemeier, M., Patel, A. & Nestor, A. The neural dynamics of facial identity processing: insights from eeg-based pattern analysis and image reconstruction. eNeuro
    https://doi.org/10.1523/ENEURO.0358-17.2018 (2018).

  • 32.

    Sattar, H., Bulling, A. & Fritz, M. Predicting the category and attributes of visual search targets using deep gaze pooling. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2740–2748, (2017).

  • 33.

    Karessli, N., Akata, Z., Schiele, B., Bulling, A. et al. Gaze embeddings for zero-shot image classification. in Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2017).

  • 34.

    Zander, T. O. & Kothe, C. Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J. Neural Eng. 8, 025005 (2011).

    ADS 
    Article 

    Google Scholar
     

  • 35.

    Lance, B. J., Kerick, S. E., Ries, A. J., Oie, K. S. & McDowell, K. Brain-computer interface technologies in the coming decades. Proc. IEEE 100, 1585–1599. https://doi.org/10.1109/JPROC.2012.2184830 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 36.

    Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M. & r. Muller, K. Optimizing spatial filters for robust eeg single-trial analysis. IEEE Signal Process. Mag. 25, 41–56. https://doi.org/10.1109/MSP.2008.4408441 (2008).

    ADS 
    Article 

    Google Scholar
     

  • 37.

    Millán, J. d. R. et al. Combining brain–computer interfaces and assistive technologies: State-of-the-art and challenges. Frontiers in Neuroscience 4, 161, https://doi.org/10.3389/fnins.2010.00161 (2010).

  • 38.

    Davis, J. J. J., Lin, C.-T., Gillett, G. & Kozma, R. An integrative approach to analyze eeg signals and human brain dynamics in different cognitive states. J. Artif. Intell. Soft Comput. Res. 7, 287–299 (2017).

    Article 

    Google Scholar
     

  • 39.

    Linsley, D., Shiebler, D., Eberhardt, S. & Serre, T. Learning what and where to attend with humans in the loop. in International Conference on Learning Representations (2019).

  • 40.

    Yang, Z., He, X., Gao, J., Deng, L. & Smola, A. Stacked attention networks for image question answering. in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).

  • 41.

    Isola, P., Xiao, J., Torralba, A. & Oliva, A. What makes an image memorable?. CVPR 145–152, 2011. https://doi.org/10.1109/CVPR.2011.5995721 (2011).

    Article 

    Google Scholar
     

  • 42.

    Goetschalckx, L., Andonian, A., Oliva, A. & Isola, P. Ganalyze: Toward visual definitions of cognitive image properties. arXiv preprint arXiv:1906.10112 (2019).

  • 43.

    Chadwick, M. J., Hassabis, D., Weiskopf, N. & Maguire, E. A. Decoding individual episodic memory traces in the human hippocampus. Curr. Biol. 20, 544–547. https://doi.org/10.1016/j.cub.2010.01.053 (2010).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    Cowen, A. S., Chun, M. M. & Kuhl, B. A. Neural portraits of perception: reconstructing face images from evoked brain activity. Neuroimage 94, 12–22 (2014).

    Article 

    Google Scholar
     

  • 45.

    Judd, T., Ehinger, K., Durand, F. & Torralba, A. Learning to predict where humans look. IEEE International Conference on Computer Vision (ICCV) 2106–2113, https://doi.org/10.1109/ICCV.2009.5459462 (2009).

  • 46.

    Branson, S., Van Horn, G., Wah, C., Perona, P. & Belongie, S. The ignorant led by the blind: A hybrid human-machine vision system for fine-grained categorization. International Journal of Computer Vision 108, 3–29. https://doi.org/10.1007/s11263-014-0698-4 (2014).

    MathSciNet 
    Article 
    MATH 

    Google Scholar
     

  • 47.

    Ribelles, J., Gutierrez, D. & Efros, A. Buildup: interactive creation of urban scenes from large photo collections. Multimedia Tools and Applications 76, 12757–12774 (2017).

    Article 

    Google Scholar
     

  • 48.

    Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Teh, Y. W. & Titterington, D. M. (eds.) AISTATS, vol. 9 of JMLR Proceedings, 249–256 (JMLR.org, 2010).

  • 49.

    Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014).



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