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Bosco S. Tjan

Professor of Psychology

Bosco S. Tjan

Research Topics

  • Peripheral vision (object recognition, scene perception, reading)
  • Visual impairments, retinal prosthesis, assistive technologies
  • Neural (f)MRI (BOLD, ASL, DTI, Structural MR, MR Microscopy, various data analysis techniques)
  • Visual psychophysics
  • Bayesian observer models

Research Images

The BOLD response functions (average peak %BOLD signal change from fixation baseline vs. stimulus SNR) from the eight ROIs log-log axes (A). The progressive increase in the log-log slope of the BOLD response functions (B) along the ventral and dorsal visual pathways were confirmed by within-subject ANOVA and planned polynomial contrasts. (From Tjan, Lestou & Kourtzi, 2006, J. Neurophys., where we explained how this ordering of the log-log slope across different brain regions may correspond to the sequential ordering of these regions in an information processing pathway.)Zones of inappropriate integration. The pooled and normalized differences (Online Methods, equation (13)) between saccade-confounded (Fig. 4d,e) and veridical (Fig. 4b,c) image statistics (mutual information) between a reference hypercolumn and neighboring hypercolumns are shown in visual space for three reference hypercolumns at 2°, 4° and 6°. The color scale shows the magnitude and sign of the deviation from the veridical statistics, indicative of inappropriate integration: shades of red indicate that the mutual information between a reference hypercolumn and an adjacent hypercolumn is higher in saccade-confounded statistics than in veridical statistics, implying over-integration; shades of blue indicate lower mutual information than the veridical, implying under- integration. Elliptical fits (dashed lines at 40% of peak normalized difference) illustrate the elongated shape of the spatial extent of inappropriate integration. The time constant of the decay of spatial attention (λ) was set at 16 ms. (From Nandy & Tjan, 2012, Nature Neuroscience)Panels B and C show the BOLD time-courses measured from 4 visual cortical areas (V1, V2, V3, and hV4) over the stimulus block for noncrowded and crowded configurations, respectively, with the target-present (blue and red curves) and without (cyan and magenta curves). The gray bar in the first panel of B indicates the stimulus duration. Subtractions of the target-absent from target-present responses are shown in (D). Shaded regions in all plots represent the within-subjects standard error (Loftus and Masson 1994). Saturated color marks the time-points used in analyses. Addition of the target led to a significant increase in BOLD response in the noncrowded configuration, but not in the crowded configuration. This interaction of target-presence with crowding was observed as early as V1. (From Millin, Arman, Chung & Tjan, 2013, Cerebral Cortex)Development and Retention of Rereferenced Saccades Measured by the First Saccade Landing Site (A) Probability density maps of the retinal position of a target object at the completion of the first saccade after target movement (same format as in
Figure 2A). (B) Variance (BCEA) of the first saccade landing site as a function of block number (same format as in Figure 2B). The first saccade landing site after each target movement was near the fixational PRL and distant from the fovea. Similar to the fixational PRL, the variance of the first saccade landing sites decreased rapidly within a short period of time without explicit training, demonstrating a spontaneous shift in oculomotor reference from the fovea to the PRL. With explicit training, the BCEA was further reduced to the normal range of intact foveal vision. (From Kwon, Nandy & Tjan, 2013, Current Biology)Second-order feature maps obtained from the human observers. (From Nandy & Tjan, 2007, JOV, where we describe a method for revealing first- and second-order features used by an observer for a letter-identification task.)Example of stimuli used in Wallace & Tjan, 2011, Journal of Vision.

Research Overview

We study the human visual system by exploring neural computations that underlie the perception of form, a domain that includes object recognition, scene perception, and reading. Our research addresses a broad spectrum of topics from basic questions in object recognition to clinical applications. We approach these topics from the theoretical framework of optimal computation: by considering the computation and behavior of a mathematically optimal observer (an ideal observer) for the given stimuli, task, and known limitations of the human visual system. The tools that we use include psychophyiscal experimentation, fMRI, and mathematical modeling.

On-going research projects:
  • Form Processing in the Periphery
  • Mid-Level Vision Systems for Low Vision
  • Retinal Prosthesis and neural plasticity
  • Separating BOLD Nonlinearity from Neuronal Nonlinearity in Human with Achiasma
  • In vivo MR Microscopy of the human eye
  • Uncertainty and the Order of Visual Processing in Cortex
  • Visual Speech Perception and Neural Processing
  • Development of a Digital Sign System for Indoor Wayfinding by Visually Impaired Pedestrians

Contact Information

Mailing Address University of Southern California
Department of Psychology, SGM 501
Los Angeles, CA 90089-1061
Office Location SGM 1017A
Office Phone (213) 821-2953
Lab Location SGM 1017A
Lab Phone (213) 821-2954
Fax (213) 746-9082
Office Location SGM 1017A



  • Research Associate, NEC Research Institute, 1998-2000
  • Post-doc., Max-Planck Institute for Biological Cybernetics, 1997-98
  • Ph.D., Computer Science, University of Minnesota, 1997
  • B.Sc. (honor), Computer Science, University of Kansas

Selected Publications

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  • Kwon, M., Nandy, A. S., & Tjan, B. S. (2013). Rapid and persistent adaptability of human oculomotor control in response to simulated central vision loss. Current Biology, PubMed
  • Millin, R., Arman, A. C., Chung, S. T. L., & Tjan, B. S. (2013). Visual crowding in V1. Cerebral Cortex. doi:10.1093/cercor/bht159 PubMed
  • Gold, J. M., Mundy, P. J., & Tjan, B. S. (2012). The perception of a face is no more than the sum of its parts. Psychological Science, 23(4), 427–434. doi:10.1177/0956797611427407 PubMed
  • Nandy, A. S., & Tjan, B. S. (2012). Saccade-confounded image statistics explain visual crowding. Nature Neuroscience, 15(3), 463–469. doi:10.1038/nn.3021 PubMed
  • Wallace, J. M., & Tjan, B. S. (2011). Object crowding. Journal of Vision, 11(6):19, 1-17. PubMed Link
  • Sun, G. J., Chung, S. T. L., & Tjan, B. S. (2010). Ideal observer analysis of crowding and the reduction of crowding through learning. Journal of Vision, 10(5):16, 1-14 PubMed Link
  • Chung, S. T. L. & Tjan, B. S. (2009). Spatial-frequency and contrast properties of reading in central and peripheral vision. Journal of Vision, 9(9):16, 1-19. PubMed Link
  • Li X, Lu ZL, Tjan BS, Dosher BA, Chu W. (2008) Blood oxygenation level-dependent contrast response functions identify mechanisms of covert attention in early visual areas. Proc Natl Acad Sci U S A. 105(16):6202-6207. PubMed
  • Nandy, A. S., & Tjan, B. S. (2008). Efficient integration across spatial frequencies for letter identification in foveal and peripheral vision. Journal of Vision, 8(13):3, 1-20. PubMed Link
  • Nandy A.S. & Tjan, B.S. (2007). The nature of letter crowding as revealed by first- and second-order classification images. Journal of Vision, 7(2):5, 1-26. PubMed Link
  • Tjan, B.S., & Nandy, A. S. (2006). Classification images with uncertainty. Journal of Vision, 6(4), 387-413, doi:10.1167/6.4.8. PubMed Link
  • Tjan, B.S., Lestou, V., & Kourtzi Z. (2006). Uncertainty and invariance in the human visual cortex. Journal of Neurophysiology (May 24, 2006; Epub ahead of print), doi:10.1152/jn.01367.2005 PubMed