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Don't admit defeat: A new dawn for the item in visual search

Published online by Cambridge University Press:  24 May 2017

Stefan Van der Stigchel
Affiliation:
Department of Experimental Psychology, Helmholtz Institute, Utrecht University, 3584 CS Utrecht, The Netherlands; [email protected]://www.attentionlab.nl
Sebastiaan Mathôt
Affiliation:
Aix-Marseille University, CNRS, LPC UMR 7290, Marseille, 13331 Cedex 1, France. [email protected]://www.cogsci.nl/smathot

Abstract

Even though we lack a precise definition of “item,” it is clear that people do parse their visual environment into objects (the real-world equivalent of items). We will review evidence that items are essential in visual search, and argue that computer vision – especially deep learning – may offer a solution for the lack of a solid definition of “item.”

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

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References

Becker, S. I., Ansorge, U. & Horstmann, G. (2009) Can inter-trial priming effects account for the similarity effect in visual search? Vision Research 49:1738–56.CrossRefGoogle Scholar
Desimone, R., Albright, T., Gross, C. & Bruce, C. (1984) Stimulus-selective properties of inferior temporal neurons in the macaque. Journal of Neuroscience 4(8):2051–62.CrossRefGoogle ScholarPubMed
Egly, R., Driver, J. & Rafal, R. D. (1994) Shifting visual attention between objects and locations: Evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General 123:161–77. doi: 10.1037//0096-3445.123.2.161.CrossRefGoogle ScholarPubMed
Einhäuser, W., Spain, M. & Perona, P. (2008) Objects predict fixations better than early saliency. Journal of Vision 8(14):18.CrossRefGoogle ScholarPubMed
He, K., Zhang, X., Ren, S. & Sun, J. (2015) Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. arXiv 1502.01852.CrossRefGoogle Scholar
Hubel, D. H. & Wiesel, T. N. (1959) Receptive fields of single neurons in the cat's striate cortex. Journal of Physiology 148(3):574–91.CrossRefGoogle ScholarPubMed
Kristjánsson, Á. & Driver, J. (2008) Priming in visual search: Separating the effects of target repetition, distractor repetition and role-reversal. Vision Research 48(10):1217–32. Available at: http://doi.org/10.1016/j.visres.2008.02.007.CrossRefGoogle ScholarPubMed
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012) ImageNet classification with deep convolutional neural networks. In: Conference of Advances in neural information processing systems, ed. Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q., pp. 1097–105. Neural Information Processing Systems Foundation. Available at: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.Google Scholar
Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J. & Ng, A. Y. (2012) Building high-level features using large scale unsupervised learning. Paper presented at the ICML.CrossRefGoogle Scholar
LeCun, Y., Kavukvuoglu, K. & Farabet, C. (2010) Convolutional networks and applications in vision. Paper presented at the ISCAS.CrossRefGoogle Scholar
Lee, H., Grosse, R., Ranganath, R. & Ng, A. Y. (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Paper presented at the ACM.CrossRefGoogle Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W.H. Freeman.Google Scholar
Meeter, M. & Van der Stigchel, S. (2013) Visual priming through a boost of the target signal: Evidence from saccadic landing positions. Attention, Perception, and Psychophysics 75:1336–41.CrossRefGoogle ScholarPubMed
Meeter, M., Van der Stigchel, S. & Theeuwes, J. (2010) A competitive integration model of exogenous and endogenous eye movements. Biological Cybernetics 102:271–91.CrossRefGoogle ScholarPubMed
Theeuwes, J., Kramer, A. F., Hahn, S. & Irwin, D. E. (1998) Our eyes do not always go where we want them to go: Capture of eyes by new objects. Psychological Science 9:379–85.CrossRefGoogle Scholar
Theeuwes, J., Mathôt, S. & Grainger, J. (2013) Exogenous object-centered attention. Attention Perception, and Psychophysics 75:812–18. doi: 10.3758/s13414-013-0459-4.CrossRefGoogle ScholarPubMed
Theeuwes, J., Mathôt, S. & Kingstone, A. (2010) Object-based eye movements: The eyes prefer to stay within the same object. Attention Perception, and Psychophysics 72(3):1221. doi: 10.3758/APP.72.3.597.CrossRefGoogle ScholarPubMed
Theeuwes, J., Mathôt, S. & Grainger, J. (2013) Exogenous object-centered attention. Attention, Perception, and Psychophysics 75:812–18.CrossRefGoogle ScholarPubMed
Trappenberg, T. P., Dorris, M. C., Munoz, D. P. & Klein, R. M. (2001) A model of saccade initiation based on the competitive integration of exogenous and endogenous signals in the superior colliculus. Journal of Cognitive Neuroscience 13(2):256–71.CrossRefGoogle Scholar