Publications

Oshin Vartanian, Delaram Farzanfar, Enric Munar, Martin Skov, Gregor Hayn-Leichsenring, Pik Ki Ho, and Dirk B. Walther (2024) Neural dissociation between computational and perceived measures of curvature, Scientific Reports, 14, 26529. https://doi.org/10.1038/s41598-024-76931-8

Zach Buck, Everan Michalchyshyn, Amna Nishat, Mikayla Lisi, Yichen Huang, Hanyu Liu, Arina Makarenka, Charles Puttcharnun Plyngam, Abigail Windle, Zhen Yang, and Dirk B. Walther (2024) Aesthetic processing in neurodiverse populations, Neuroscience & Biobehavioral Reviews, Volume 166, 105878,
https://doi.org/10.1016/j.neubiorev.2024.105878

Gaeun Son, Dirk B. Walther, and Michael L. Mack (2024). Brief category learning distorts perceptual space for complex scenes. Psychonomic Bulletin and Review https://doi.org/10.3758/s13423-024-02484-6

Aedan Yue Li, Natalia Ladyka-Wojcik, Heba Qazilbash, Ali Golestani, Dirk B. Walther, Chris B Martin, Morgan Barense (2024) Experience transforms crossmodal object representations in the anterior temporal lobes. eLife 13:e83382. https://doi.org/10.7554/eLife.83382 

Diane Beck and Dirk B. Walther (2024). The natural scene network. In Oxford Research Encyclopedia of Neuroscience. https://doi.org/10.1093/acrefore/9780190264086.013.396

Charlotte A Leferink, Jordan DeKraker, Iva K Brunec, Stefan Köhler, Morris Moscovitch, and Dirk B Walther (2024). Organization of pRF size along the AP axis of the hippocampus and adjacent medial temporal cortex is related to specialization for scenes versus faces, Cerebral Cortex, Volume 34, Issue 1, January 2024, bhad429, https://doi.org/10.1093/cercor/bhad429

Morteza Rezanejad, John Wilder, Dirk B. Walther, Allan D. Jepson, Sven Dickinson, and Kaleem Siddiqi (2023). Shape-Based Measures Improve Scene Categorization.  IEEE Transactions on Pattern Analysis and Machine Intelligence, Early Access, pp. 1-14. https://doi.org/10.1109/TPAMI.2023.3333352

Elizabeth Y. Zhou, John Wilder, Claudia Damiano, and Dirk B. Walther (2023). Neural dissociation between computational and subjective image complexity. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication. https://doi.org/10.1037/aca0000605

Seohee Han, Morteza Rezanejad, and Dirk B. Walther (2023). Memorability of line drawings of scenes: the role of contour properties. Memory and Cognition, 5. https://doi.org/10.3758/s13421-023-01478-4

Dirk B. Walther, Delaram Farzanfar, Seohee Han, and Morteza Rezanejad (2023). The mid-level vision toolbox for computing structural properties of real-world images. Frontiers in Computer Science, 5. https://doi.org/10.3389/fcomp.2023.1140723

Delaram Farzanfar and Dirk B. Walther (2023). Changing What You Like: Modifying Contour Properties Shifts Aesthetic Valuations of Scenes. Psychological Science, (advance online publication). https://doi.org/10.1177/09567976231190546

Greer Gillies, Hyun Park, Jason Woo, Dirk B. Walther, Jonathan S., and Keisuke Fukuda, (2023). Tracing the emergence of the memorability benefit. Cognition, 238, 105489. https://doi.org/10.1016/j.cognition.2023.105489

Yaelan Jung, Tess Allegra Forest, Dirk B. Walther, Amy S. Finn (2023). Neither Enhanced Nor Lost: The Unique Role of Attention in Children's Neural Representations. Journal of Neuroscience, 43(21), 3849-3859. doi: https://doi.org/10.1523/JNEUROSCI.0159-23.2023 

Claudia Damiano, Pinaki Gayen, Morteza Rezanejad, Archi Banerjee, Gobinda Banik, Priyadarshi Patnaik, Johan Wagemans, Dirk B. Walther (2023). Anger is red, sadness is blue: Emotion depictions in abstract visual art by artists and non-artists. Journal of Vision Vol.23, 1. doi: https://doi.org/10.1167/jov.23.4.1

Cameron Kyle-Davidson, Elizabeth Y. Zhou, Dirk B. Walther, Adrian G. Bors, Karla K. Evans (2023) Characterising and dissecting human perception of scene complexity. Cognition 231, 105319. 

Morteza Rezanejad, Mohammad Khodadad, Hamidreza Mahyar, Herve Lombaert, Michael Gruninger, Dirk Walther, Kaleem Siddiqi (2022). Medial Spectral Coordinates for 3D Shape Analysis; In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2686-2696.

Wilder, J., Rezanejad, M., Dickinson, S., Siddiqi, K., Jepson, A., & Walther, D. B. (2022). Neural correlates of local parallelism during naturalistic vision. PLoS One, 17(1), e0260266. https://doi.org/10.1371/journal.pone.0260266 

Son, G., Walther, D. B., & Mack, M. L. (2022). Scene wheels: measuring perception and memory of real-world scenes with a continuous stimulus space. Behavior Research Methods, 54(1), 444-456. https://doi.org/10.3758/s13428-021-01630-5 

Dirk B. Walther (2021). Architectural styles as subordinate scene categories. in: Chatterjee, A., & Cardilo, E. (Eds.). (2021). Brain, Beauty, and Art: Essays Bringing Neuroaesthetics Into Focus. Oxford University Press, pages 225-229.

Rezanejad, M., Gupta, S., Gummaluru, C., Marten, R., Wilder, J., Gruninger, M., & Walther, D. B. (2021). Contour-guided Image Completion with Perceptual Grouping. Proceedings of the British Machine Vision Conference (online).

Sheng, H., Wilder, J., & Walther, D. B. (2021). Where to draw the line?. PloS One, 16(11), e0258376. https://doi.org/10.1371/journal.pone.0258376

Damiano, C., Walther, D. B., & Cunningham, W. A. (2021). Contour features predict valence and threat judgements in scenes. Scientific Reports, 11(1), 1-12 https://doi.org/10.1038/s41598-021-99044-y

Jung, Y., & Walther, D. B. (2021). Neural Representations in the Prefrontal Cortex Are Task Dependent for Scene Attributes But Not for Scene Categories. Journal of Neuroscience, 41(34), 7234-7245. https://doi.org/10.1523/JNEUROSCI.2816-20.2021 

Damiano, C., Wilder, J., Zhou, E. Y., Walther, D. B., & Wagemans, J. (2021). The role of local and global symmetry in pleasure, interest, and complexity judgments of natural scenes. Psychology of Aesthetics, Creativity, and the Arts. https://doi.org/10.1037/aca0000398

Cheng, A., Walther, D. B., Park, S., & Dilks, D. D. (2021). Concavity as a diagnostic feature of visual scenes. NeuroImage, 232, 117920. https://doi.org/10.1016/j.neuroimage.2021.117920

Jung, Y., Walther, D. B., & Finn, A. S. (2021). Children automatically abstract categorical regularities during statistical learning. Developmental Science, 24(5), e13072. doi: https://doi.org/10.1111/desc.13072

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

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.

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

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. 

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. 

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

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.

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.

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. 

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.

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. 

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. 

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. 

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.

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. 

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. 

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.

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. 

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. 

Walther, D. B. (2013). 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. 

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. 

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. 

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. 

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. 

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. 

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). 

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). 

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. 

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. 

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. 

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. 

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. 

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. 

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 (Vol. 1, pp. I-I). IEEE. https://doi.org/10.1109/CVPR.2004.1315079. 

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 (Vol. 2, pp. II-II). IEEE. https://doi.org/10.1109/CVPR.2004.1315142. 

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). 

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. 

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.