Damiano, C, Wilder, J, & Walther DB. (2018). Mid-level feature contributions to category-specific gaze guidance. Attention, Perception, & Psychophysics. doi: PDF

Wilder, J, Dickinson, S, Jepson, A, & Walther DB. (2018). Spatial relationships between contours impact rapid scene classification. Journal of Vision. 18(8):1. doi: 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. 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. PDF

Jung Y, Larson B, Walther DB. (2018). Modality-independent coding of scene categories in prefrontal cortex. Journal of Neuroscience. PDF [stimuli]

Jung Y, Larson B, Walther DB. (2018). Using decoding error patterns to trace the neural signature of auditory scene perception. Proceedings of the 8th International Workshop on Pattern Recognition in NeuroImaging, Singapore. PDF


Berman D, Golomb JD, Walther DB. (2017). Scene content is predominantly conveyed by high spatial frequencies in scene-selective visual cortex. PLOS ONE 12(12): e0189828. weblink or PDF

Choo H, & Walther DB. (2017). Modeling the Effect of Stimulus Perturbations on Error Correlations between Brain and Behavior, Proceedings of the 7th International Workshop on Pattern Recognition in NeuroImaging, Toronto, Canada. PDF

Jung Y, Larsen B, & Walther DB. (2017). Modality-Independent Coding Of Concepts In Prefrontal Cortex, Biorxiv, doi:

Choo H, Nasar J, Nikrahei B, & Walther DB. (2017). Neural codes of seeing architectural styles, Scientific Report. PDF


Choo H, & Walther DB. (2016). Contour junctions underlie neural representations of scene categories in human visual cortex, Neuroimage, 135, 32-44. doi:10.1016/j.neuroimage.2016.04.021 PDF


Damiano C, & Walther DB. (2015). Content, not context, facilitates memory for real-world scenes. Visual Cognition, 23(7), 852-855.PDF

Olivetti E, & Walther DB. (2015). A Bayesian Test for Comparing Classifier Errors, Proceedings of the 5th International
Workshop on Pattern Recognition in NeuroImaging:
69-72, Stanford, CA. PDF

O’Connell T, & Walther DB. (2015). Dissociation of salience-driven and content-driven spatial attention to scene category with predictive decoding of gaze patterns, Journal of Vision, 12(5):20, 1-13, doi:10.1167/15.5.20 or PDF

Richards MR, Fields HW Jr, Beck FM, Firestone AR, Walther DB, Rosenstiel S, Sacksteder JM. (2015). Contribution of malocclusion and female facial attractiveness to smile esthetics evaluated by eye tracking. American Journal of Orthodontics and Dentofacial Orthopedics 147(4):472-82. PDF


Walther DB, & Shen D. (2014). Non-accidental properties underlie human categorization of complex natural scenes, Psychological Science, 25:851-860. PDF

Kim K, Lin KH, Walther DB, Hasegawa-Johnson MA, Huang TS. (2014). Automatic Detection of Auditory Salience with Optimized Linear Filters Derived from Human Annotation, Pattern Recognition Letters, 38: 78-85. or PDF


Walther DB. (2013). Using confusion matrices to estimate mutual information between two categorical measurements, Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging: 220-224. Philadelphia, PA. PDF

Torralbo A, Walther DB, Chai B, Caddigan E, Fei-Fei L, Beck DM. (2013). Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity. PLoS ONE. 8(3): e58594. doi: 10.1371/journal.pone.0058594 or 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 DB, Chai B, Caddigan E, Beck DM, & Fei-Fei L. (2011). Simple line drawings suffice for functional MRI decoding of natural scene categories, PNAS. 108(23): 9661-9666. PDF

Vo LTK, Walther DB, Kramer AF, Erickson KI, Boot WR, Voss M, Prakash RS, Fabiani M, Gratton G, Simons DJ, & Wang MY. (2011). Predicting Individuals’ Learning Success from Patterns of Pre-learning MRI Activity. PLoS One. 6(1): e16093. doi: 10.1371/journal.pone.0016093 or PDF


Chai B, Walther DB, Beck DM*, & Fei-Fei L*. (2009). Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis. NIPS. PDF

Yao B, Walther DB, Beck DM*, & Fei-Fei L*. (2009). Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions. NIPS. PDF

Walther DB, Caddigan E, Fei-Fei L*, & Beck DM*. (2009). Natural scene categories revealed in distributed patterns of activity in the human brain. Journal of Neuroscience, 29(34):10573–10581. PDF

Ning H, Han TX, Walther DB, Liu M, & Huang T. (2009). Hierarchical Space-Time Model Enabling Efficient Search for Human Actions. IEEE Transactions on Circuits and Systems for Video Technology, 19(6): 808-820. PDF


Walther DB, & Fei-Fei L. (2007). Task-set switching with natural scenes: Measuring the cost of deploying top-down attention. Journal of Vision, 7(11):9, 1-12, doi: 10.1167/7.11.9. or 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.

Walther D, & Koch C. (2006). Modeling attention to salient proto-objects. Neural Networks, 19, 1395-1407. 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. PDF


Walther D, Edgington DR, & Koch C. (2004). Detection and Tracking of Objects in Underwater Video. IEEE International Conference on Computer Vision and Pattern Recognition, 1, 544-549. PDF

Rutishauser U, Walther D, Koch C, & Perona P. (2004). Is bottom-up attention useful for object recognition? IEEE International Conference on Computer Vision and Pattern Recognition, 2, 37-44. (“Best Poster Presentation” award.) PDF

Walther D, Rutishauser U, Koch C, & Perona P. (2004). On the usefulness of attention for object recognition. 2nd Workshop on Attention and Performance in Computational Vision at the European Conference for Computer Vision, 96-103. PDF


Walther D, Itti L, Riesenhuber M, Poggio T, & Koch C. (2002). Attentional Selection for Object Recognition – a Gentle Way. Biologically Motivated Computer Vision – Lecture Notes in Computer Science, Springer, 2525, 472-479. 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. Biologically Motivated Computer Vision – Lecture Notes in Computer Science, Springer (2002) 2525, 558-566. PDF

Book Chapters:

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

(†,* indicates equal contribution)