Darby, K. P., Deng, S. W., Walther, D. B., & Sloutsky, V. M. (2020). The Development of Attention to Objects and Scenes: From Object‐Biased to Unbiased. Child Development. https://doi.org/10.1111/cdev.13469. PDF
Perfetto, S., Wilder, J., & Walther, D. B. (2020). Effects of Spatial Frequency Filtering Choices on the Perception of Filtered Images. Vision, 4(2), 29. https://doi.org/10.3390/vision4020029
Rezanejad, M., Downs, G., Wilder, J., Walther, D. B., Jepson, A., Dickinson, S., & Siddiqi, K. (2019). Scene categorization from contours: Medial axis based salience measures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4116-4124). PDF
Damiano, C., & Walther, D. B. (2019). Distinct roles of eye movements during memory encoding and retrieval. Cognition, 184, 119-129. https://doi.org/10.1016/j.cognition.2018.12.014. PDF
Damiano, C, Wilder, J, & Walther DB. (2019). Mid-level feature contributions to category-specific gaze guidance. Attention, Perception, & Psychophysics, 81: 35-46. https://doi.org/10.3758/s13414-018-1594-8. PDF
Wilder J., Rezanejad M., Dickinson S., Siddiqi K., Jepson A., & Walther D.B. (2019). Local contour symmetry facilitates scene categorization. Cognition, 182: 307-317. https://doi.org/10.1016/j.cognition.2018.09.014. PDF
Wilder, J, Dickinson, S, Jepson, A, & Walther DB. (2018). Spatial relationships between contours impact rapid scene classification. Journal of Vision. 18(8):1. https://doi.org/10.1167/18.8.1. PDF
Lowe, MX, Rajsic, J, Ferber, S, & Walther DB. (2018). Discriminating scene categories from brain activity within 100 ms. Cortex 106:275-287. https://doi.org/10.1016/j.cortex.2018.06.006. PDF
O’Connell, TP, Sederberg, PB, & Walther DB. (2018). Representational differences between line drawings and photographs of natural scenes: A dissociation between multi-voxel pattern analysis and repetition suppression. Neuropsychologia, 117: 513–519. https://doi.org/10.1016/j.neuropsychologia.2018.06.013. PDF
Jung, Y., Larsen, B., & Walther, D. B. (2018). Modality-independent coding of scene categories in prefrontal cortex. Journal of Neuroscience, 38(26), 5969-5981. https://doi.org/10.1523/JNEUROSCI.0272-18.2018. PDF [stimuli]
Jung, Y., Larsen, B., & Walther, D. B. (2018, June). Using decoding error patterns to trace the neural signature of auditory scene perception. In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (pp. 1-4). IEEE. https://doi.org/10.1109/PRNI.2018.8423950. PDF
Berman, D., Golomb, J. D., & Walther, D. B. (2017). Scene content is predominantly conveyed by high spatial frequencies in scene-selective visual cortex. PLoS One, 12(12), e0189828. https://doi.org/10.1371/journal.pone.0189828
Choo, H., & Walther, D. B. (2017, June). Modeling the effect of stimulus perturbations on error correlations between brain and behavior. In 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI) (pp. 1-4). IEEE. https://doi.org/10.1109/PRNI.2017.7981497. PDF
Jung, Y., Larsen, B., & Walther, D. B. (2017). Modality-independent coding of concepts in prefrontal cortex. bioRxiv, 142562. https://doi.org/10.1101/142562
Choo, H., Nasar, J. L., Nikrahei, B., & Walther, D. B. (2017). Neural codes of seeing architectural styles. Scientific reports, 7(1), 1-8. https://doi.org/10.1038/srep40201. PDF
Choo, H., & Walther, D. B. (2016). Contour junctions underlie neural representations of scene categories in high-level human visual cortex. Neuroimage, 135, 32-44. https://doi.org/10.1016/j.neuroimage.2016.04.021. PDF
Damiano, C., & Walther, D. B. (2015). Content, not context, facilitates memory for real-world scenes. Visual Cognition, 23(7), 852-855. https://doi.org/10.1080/13506285.2015.1093241. PDF
Olivetti, E., & Walther, D. B. (2015, June). A Bayesian Test for Comparing Classifier Errors. In 2015 International Workshop on Pattern Recognition in NeuroImaging (pp. 69-72). IEEE. https://doi.org/10.1109/PRNI.2015.11. PDF
O’Connell, T. P., & Walther, D. B. (2015). Dissociation of salience-driven and content-driven spatial attention to scene category with predictive decoding of gaze patterns. Journal of vision, 15(5), 20-20. https://doi.org/10.1167/15.5.20. PDF
Richards, M. R., Fields Jr, H. W., Beck, F. M., Firestone, A. R., Walther, D. B., Rosenstiel, S., & Sacksteder, J. M. (2015). Contribution of malocclusion and female facial attractiveness to smile esthetics evaluated by eye tracking. American Journal of Orthodontics and Dentofacial Orthopedics, 147(4), 472-482. https://doi.org/10.1016/j.ajodo.2014.12.016. PDF
Walther, D. B., & Shen, D. (2014). Nonaccidental properties underlie human categorization of complex natural scenes. Psychological science, 25(4), 851-860. https://doi.org/10.1177/0956797613512662. PDF
Kim, K., Lin, K. H., Walther, D. B., Hasegawa-Johnson, M. A., & Huang, T. S. (2014). Automatic detection of auditory salience with optimized linear filters derived from human annotation. Pattern Recognition Letters, 38, 78-85. https://doi.org/10.1016/j.patrec.2013.11.010. PDF
Walther, D. B. (2013, June). Using confusion matrices to estimate mutual information between two categorical measurements. In 2013 International Workshop on Pattern Recognition in Neuroimaging (pp. 220-224). IEEE. https://doi.org/10.1109/PRNI.2013.63. PDF
Torralbo, A., Walther, D. B., Chai, B., Caddigan, E., Fei-Fei, L., & Beck, D. M. (2013). Good exemplars of natural scene categories elicit clearer patterns than bad exemplars but not greater BOLD activity. PloS one, 8(3), e58594. https://doi.org/10.1371/journal.pone.0058594. PDF
Rivera S, Best C, Yim H, Martinez A, Sloutsky V, & Walther DB. (2012). Automatic selection of eye tracking variables in visual categorization for adults and infants. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society: 2240-2245. Austin, TX: Cognitive Science Society. PDF
Walther, D. B., Chai, B., Caddigan, E., Beck, D. M., & Fei-Fei, L. (2011). Simple line drawings suffice for functional MRI decoding of natural scene categories. Proceedings of the National Academy of Sciences (PNAS), 108(23), 9661-9666. https://doi.org/10.1073/pnas.1015666108. PDF
Vo, L. T., Walther, D. B., Kramer, A. F., Erickson, K. I., Boot, W. R., Voss, M. W., … & Simons, D. J. (2011). Predicting individuals’ learning success from patterns of pre-learning MRI activity. PLoS One, 6(1), e16093. https://doi.org/10.1371/journal.pone.0016093. PDF
Chai B†, Walther DB†, Beck DM*, & Fei-Fei L*. (2009). Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis. In Advances in neural information processing systems (NIPS) (pp. 270-278). PDF
Yao B, Walther DB, Beck DM*, & Fei-Fei L*. (2009). Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions. In Advances in neural information processing systems (NIPS) (pp. 2178-2186). PDF
Walther, D. B., Caddigan, E., Fei-Fei, L., & Beck, D. M. (2009). Natural scene categories revealed in distributed patterns of activity in the human brain. Journal of neuroscience, 29(34), 10573-10581. https://doi.org/10.1523/JNEUROSCI.0559-09.2009. PDF
Ning, H., Han, T. X., Walther, D. B., Liu, M., & Huang, T. S. (2009). Hierarchical space-time model enabling efficient search for human actions. IEEE Transactions on Circuits and Systems for Video Technology, 19(6), 808-820. https://doi.org/10.1109/TCSVT.2009.2017399. PDF
(†,* indicates equal contribution)
Walther, D. B., & Fei-Fei, L. (2007). Task-set switching with natural scenes: measuring the cost of deploying top-down attention. Journal of Vision, 7(11), 9-9. https://doi.org/10.1167/7.11.9. PDF
Walther D. (2006). Interactions of visual attention and object recognition: computational modeling, algorithms, and psychophysics. PhD thesis, California Institute of Technology, Pasadena, CA, 23th February 2006. http://resolver.caltech.edu/CaltechETD:etd-03072006-135433
Walther, D., & Koch, C. (2006). Modeling attention to salient proto-objects. Neural networks, 19(9), 1395-1407. https://doi.org/10.1016/j.neunet.2006.10.001. PDF
Walther D, Rutishauser U, Koch C, & Perona P. (2005). Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 100, 41-63. https://doi.org/10.1016/j.cviu.2004.09.004. PDF
Walther, D., Edgington, D. R., & Koch, C. (2004, June). Detection and tracking of objects in underwater video. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 1, pp. I-I). IEEE. https://doi.org/10.1109/CVPR.2004.1315079. PDF
Rutishauser, U., Walther, D., Koch, C., & Perona, P. (2004, June). Is bottom-up attention useful for object recognition?. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 2, pp. II-II). IEEE. https://doi.org/10.1109/CVPR.2004.1315142. PDF
Walther, D., Rutishauser, U., Koch, C., & Perona, P. (2004, May). On the usefulness of attention for object recognition. In Workshop on Attention and Performance in Computational Vision at ECCV (pp. 96-103). PDF
Walther, D., Itti, L., Riesenhuber, M., Poggio, T., & Koch, C. (2002, November). Attentional selection for object recognition—a gentle way. In International workshop on biologically motivated computer vision (pp. 472-479). Springer, Berlin, Heidelberg. PDF
Chung D, Hirata R, Mundhenk TN, Ng J, Peters RJ, Pichon E, Tsui A, Ventrice T, Walther D, Williams P, & Itti L. (2002). A new robotics platform for neuromorphic vision: Beobots. In International Workshop on Biologically Motivated Computer Vision (pp. 558-566). Springer, Berlin, Heidelberg. PDF
Dirk B. Walther, Diane M. Beck, and Li Fei-Fei. (2012). To err is human: correlating fMRI decoding and behavioral errors to probe the neural representation of natural scene categories. in: Nikolaus Kriegeskorte and Gabriel Kreiman (eds.), Understanding visual population codes – Toward a common multivariate framework for cell recording and functional imaging, MIT Press, Cambridge, Massachusetts. PDF
Dirk B. Walther and Christof Koch. (2007). Attention in Hierarchical Models of Object Recognition. in Paul Cisek, Trevor Drew, and John F. Kalaska (eds.), Computational Neuroscience: Theoretical insights into brain function, Progress in Brain Research, 165: 57-78. PDF