Dr. Sereno’s lab studies the representation of shape and space in the primate brain using experimental and computational approaches. Recent work has focused on investigating the neural basis of 3D form perception using functional magnetic resonance imaging (fMRI) in humans and monkeys, the relationship between shape constancy and the artistic skill of drawing, spatial navigation and map use, and responses to nature’s patterns (fractals). Additional collaborative projects focus on the representation of space from eye-position modulated neural signals and the interaction between perception and language. Dr. Sereno has collaborations with PIs from other Departments (Prof. Richard Taylor, Physics; Prof. Ihab Elzeyadi, Architecture) and Universities (Prof. Anne Sereno, Purdue University; Dr. Sidney Lehky, Salk Insitute; Profs. Sara Sereno and Paddy O’Donnell, University of Glasgow, Scotland; Prof. Martin Sereno, San Diego State University; and Prof. Nikos Logothetis, Max Planck Institute for Biological Cybernetics, Tübingen, Germany) as well as students from Psychology, Physics, Biology, and Architecture at the University of Oregon.

3D Shape Perception and Artistic Skill


This project investigates the neural basis of 3-D form perception using non-invasive high-resolution functional magnetic resonance imaging (fMRI) in human and monkey subjects and behavioral experiments. Results in the monkey suggest that 3D shape from static (e.g., shading) and motion cues is represented in both dorsal and ventral pathways. Behavioral and imaging experiments in human subjects explore 3D shape representation as well as executive function (i.e., the ability to attend to 3D vs. 2D aspects of 3D displays) in participants with a range of drawing skill.

Sereno, M.E., Robles, K.E., Kikumoto, A., Bies, A.J. (2019). The effects of 3-dimensional context on shape perception. Psychological Science. (In Press).

Edwards, K.N., Bies, A.J., Kikumoto, A., Lazarides, S., Sereno, M. E. (2018). Shape constancy in anaglyphs: Effects of drawing training. Vision Sciences Society Abstract.

Peng, X., Sereno, M.E., Silva, A.K., Lehky, S.R., & Sereno, A.B. (2008). Shape selectivity in primate frontal eye field. Journal of Neurophysiology, 100, 796-814. [pdf]

Sereno, M.E., Augath, M., & Logothetis, N.K. (2005). Differences in processing of 3-D shape from multiple cues in monkey cortex revealed by fMRI. Society for Neuroscience Abstracts. [pdf]

Sereno, M.E., Trinath, T., Augath,M., & Logothetis, N.K. (2002). Three-dimensional shape representation in monkey cortex. Neuron33, 635-652. [pdf] [movie1] [movie2]

Fractal Perception

Complex natural forms (mountains, trees, clouds, shore lines, rivers) are fractal in that they possess structure that repeats at increasingly fine magnifications. We investigate human behavioral and neural responses to visual fractal stimuli using simpler 2D stimuli and more complex 3D immersive virtual reality environments.

Van Dusen, B., Scannell, B.C., Sereno, M.E., Spehar, B., Taylor, R.P. (2019). The Sinai light show: Using science to tune fractal aesthetics. In: Wuppuluri, S., Wu, D. (Eds.) On Art and Science: Tango of an Eternally Inseparable Duo. The Frontiers Collection. Springer Nature Switzterland AG.

Abboushi, B., Elzeyadi, I., Taylor, R.P., Sereno, M.E. (2019). Fractals in Architecture: the visual interest, preference, and mood response to projected fractal light patterns in interior spaces. Journal of Environmental Psychology, 61, 57-70.

Taylor, R.P., Juliani, A.W., Bies, A.J., Boydston C.R., Spehar, B., Sereno, M.E. (2018). The implications of fractal fluency for biophilic architecture. Journal of BioUrbanism, 6, 23-40.

Bies, A.J., Tate, W.M., Taylor, R.P., Sereno, M.E. (2018). A factor analytic approach reveals variability and consistency in perceived complexity ratings of landscape photographs. Vision Sciences Society Abstract.

Tate, W.M., Taylor, R.P., Sereno, M.E., Bies, A.J. (2018). Perceived complexity and aesthetic responses to landscape photographs. Vision Sciences Society Abstract.

Bies, A.J., Boydston, C. R., Taylor, R.P., & Sereno, M.E. (2016). Relationship between fractal dimension and spectral decay rate in computer-generated fractals. Symmetry, 8:66. [pdf]

Juliani, A.W., Bies, A.J., Boydston, C.R., Taylor, R.P., & Sereno, M.E. (2016). Navigation performance in virtual environments varies as a function of fractal dimension. Journal of Environmental Psychology, 47, 155-165. [pdf] [movie1] [movie2] [jenvp-website]

Bies, A.J., Blanc-Goldhammer, D.R., Boydston C.R., Taylor, R.P., & Sereno, M.E. (2016). Aesthetic responses to exact fractals driven by physical complexity. Frontiers in Human Neuroscience, 10:210. [pdf]

Bies, A.J., Kikumoto, A., Boydston C.R., Greenfield, A.L., Chauvin, K.A., Taylor, R.P., Sereno, M.E. (2016). Percepts from noise patterns: The role of fractal dimension in object pareidolia. Vision Sciences Society Abstract. [pdf]

Bies, A., Wekselblatt, J.B., Boydston C.R., Taylor, R.P., Sereno, M.E. (2015). The effects of visual scene complexity on human cortex. Society for Neuroscience Abstract. [pdf]

Bies A.J., Taylor R.P., Sereno, M.E. (2015). An edgy image statistic: Semi-automated edge extraction and fractal box-counting algorithm allows for quantification of edge dimension in natural scenes. Vision Sciences Society Abstract. [pdf]

Spatial and Map Cognition

This work uses brain imaging and computational modeling methods to understand components of cognitive processing during real world tasks such as map reading. We examine the relationship between different spatial skills or abilities (like mental rotation), as outlined by psychometric tests, and the cognitive processing that occurs during map reading exercises (such as finding the shortest path on a map). We also investigate brain responses to both visual and tactile spatial and map-like stimuli in sighted and blind participants. Finally, we model brain processes in map reading and navigation using deep reinforcement learning in neural networks.

Juliani, A.W. & Sereno, M.E. (2019). Learning to integrate egocentric and allocentric information using a goal-directed reward signal. Vision Sciences Society Abstract.

Juliani, A.W., Bies, A.J., Boydston, C.R., Taylor, R.P., & Sereno, M.E. (2016). Navigation performance in virtual environments varies as a function of fractal dimension. Journal of Environmental Psychology, 47, 155-165. [pdf] [movie1] [movie2] [jenvp-website]

Bies, A.J. & Sereno, M.E. (2016). Understanding the relationship between specific spatial abilities and map reading skills using fMRI. Society for Neuroscience Abstract. [pdf]

Elucidating the Representation of Visual Shape and Space using Population Coding

This PopCoding-NeuralComputation-2013_CoverImgproject focuses on the representation of shape and space from neural signals using population decoding methods.

Sereno, A.B., Lehky S.R., Sereno, M.E. (2019). Representation of shape, space, and attention in monkey cortex. Cortex. (In Press).

Lehky S.R., Sereno M.E., & Sereno A.B. (2016). Characteristics of eye-position gain field populations determine geometry of visual space. Frontiers in Integrative Neuroscience, 9:72. [pdf]

Sereno, A.B., Sereno, M.E., Lehky, S.R. (2014). Recovering stimulus locations using populations of eye-position modulated neurons in dorsal and ventral visual streams of nonhuman primates. Frontiers in Integrative Neuroscience, 8:28. [pdf]

Lehky, S.R., Sereno, M.E., & Sereno, A.B. (2013). Population coding and the labeling problem: extrinsic versus intrinsic representations. Neural Computation, 25, 2235-2264. [pdf]

Lehky, S.R., Sereno, A.B., & Sereno, M.E. (2013). Monkeys in space: Primate neural data suggest volumetric representations. Behavioral and Brain Sciences, 36, 555-556. [Commentary on BBS target article “Navigating in a three dimensional world” by Jeffrey K.J., Jovalekic, A., Verriotis M., & Hayman, R. (2013). Behavioral and Brain Sciences, 36, 523-543.] [pdf]

Perception-Language Interactions

This project investigates the interaction between perception and language. We show that processing words that denote large things in the world (e.g., “ocean”) is faster than processing words that denote small things (e.g., “apple”) and that this semantic size effect plays a role in the recognition of words expressing abstract (e.g., “eternal” vs. “impulse”) as well as concrete concepts.

Yao, B., Vasiljevic, M., Weick, M., Sereno, M.E., O’Donnell, P.J., & Sereno, S.C. (2013). Semantic size of abstract concepts: It gets emotional when you can’t see it. PLOS ONE, 8:e75000. [pdf]

Sereno, S.C., O’Donnell, P.J., & Sereno, M.E. (2009). Size matters: Bigger is faster. The Quarterly Journal of Experimental Psychology, 62, 1115-1122. [pdf]

Motion Perception

BookCoverOne aspect of this research involves building a partially pre-specified, multistage model of the visual system in which response properties of higher stages develop as the model “learns from experience.” Such a structured learning system (like developing biological systems) can extract environmental regularities by combining information from lower levels to represent complex, abstract properties of the input array to reveal features of the environment represented at intermediate and higher-level stages of visual processing. One project has focused on neural models of motion perception – determining large-scale object motion from spatially localized motion signals. These models have made counter intuitive predictions about the perception of the speed and direction of simple patterns and the anatomical basis of position-invariant responses to rotation and dilation in the visual system. Another project investigates the influence of 2D center-surround neural mechanisms (e.g., in neurons in area MT) on the perception of 3D structure-from-motion.

Sereno, M.E., & Sereno, M.I. (1999). 2-D center-surround effects on 3-D structure-from-motion. Journal of Experimental Psychology: Human Perception and Performance, 25, 1834-1854. [pdf]

Sereno M.E. (1993). Neural Computation of Pattern Motion: Modeling stages of motion analysis in the primate visual cortex. Cambridge: MIT Press/Bradford Books.

Zhang, K., Sereno, M.I., & Sereno, M.E. (1993). How position-independent detection of sense of rotation or dilation is learned by a Hebb rule:a theoretical analysis. Neural Computation, 5, 597-612. [pdf] [html]

Sereno, M.I. & Sereno, M.E. (1991). Learning to see rotation and dilation with a Hebb rule. In Lippmann, R.P., Moody, J., Touretzky, D.S. (eds.) Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann Publishers. 320-326. [pdf]

Kersten, D.K., O’toole, A.J., Sereno, M.E., Knill, D.C., & Anderson, J.A. (1987). Associative learning of scene parameters from images. Applied Optics, 26, 4999-5006. [pdf]

Sereno, M.E.  (1987). Implementing stages of motion analysis in neural networks.  In Proceedings of the Ninth Annual Conference of the Cognitive Science Society.  405-416.