Elissa Aminoff

Elissa Aminoff.

Assistant Professor of Psychology
Curriculum Vitae

Email: [email protected]

Rose Hill Campus: Dealy Hall, Room 332
Phone: 718-817-3480

Vision and Memory Lab

Currently accepting students.

    • 2001 ScB in Cognitive Neuroscience, Brown University
    • 2005 MS in Psychology, Harvard University
    • 2008 PhD in Psychology, Harvard University
    • 2008 - 2011 Postdoctoral Fellow, University of California, Santa Barbara
    • 2011 - 2013 Postdoctoral Fellow, Carnegie Mellon University
  • My research focuses on the use of a combination of neuroimaging techniques (fMRI, EEG, MEG), computational models (e.g. artificial vision models), psychophysics, and cognitive theory to investigate how visual information is processed in the brain and is expressed through behavior. Specifically, I investigate how experience and context interacts with visual perception by investigating the intersection of high-level vision, associative processing, and memory.

    • PSYC 1100 Biopsychology
    • PSYC 3110 Cognitive Neuroscience
    • PSYC 6654 Introduction to Neuroscience
  • Yang, Y., Tarr, M., Kass, R. & Aminoff, E. (In Press). Exploring spatio-temporal neural dynamics of the human visual cortex. Human Brain Mapping.

    Chang, N., Pyles, J., Marcus, A., Gupta, A., Tarr, M. & Aminoff, E. (2019). BOLD5000, a public fMRI dataset while viewing 5000 visual images. Scientific Data (6) 1, 49.

    Aminoff, E., Li, Y., Pyles, J., Ward, M., R. M. Richardson, & A. Ghuman. (2016). Associative hallucinations result from stimulating left ventromedial temporal cortex. Cortex, 83, 139-144.  

    Tarr. M & Aminoff, E. (2016). Can big data help us understand human vision? In, Jones, M. (Ed.), Big Data in Cognitive Science. Psychology Press (Taylor & Francis).    

    Kim, J.*, Aminoff, E.*, Kastner, S., & Behrmann, M. (2015). The neural basis of developmental topographic disorientation. Journal of Neuroscience, 35, 12954-12969.  * Equal contribution.

    Aminoff, E. & Tarr, M. (2015). Associative processing is inherent in scene perception. PLoS ONE, 10(6): e0128840.

    Aminoff, E., Toneva, M., Shrivastava, A., Chen, X., Misra, I., Gupta, A. & Tarr, M. (2015). Applying artificial vision models to human scene understanding. Front. Comput. Neurosci. 9:8. doi: 10.3389/fncom.2015.00008. Special Research Topic: Integrating computational and neural findings in visual object perception.

    Aminoff, E., Freeman, S., Clewett, D., Tipper, C., Frithsen, A., Johnson, A., Grafton, S., & Miller, M. (2015). Maintaining a cautious state of mind during a recognition test: A large-scale fMRI study. Neuropsychologia, 67, 132-147.

    Aminoff, E. (2014). Putting scenes in context. In, Kveraga, K. & Bar, M. (Eds), Scene Vision: Making sense of what we see (pp. 135-154). Cambridge: MIT Press.

    Hermunstad, A., Brown, K., Bassett, D., Aminoff, E., Frithsen, A., Johnson, A.,Tipper, C., Miller, M., Grafton, S., & Carlson, J. (2014). Structurally-constrained relationships between cognitive states in the human brain. PLOS Computational Biology 10: e1003591.

    Aminoff, E., Kveraga, K., & Bar, M. (2013). The role of the parahippocampal cortex in
    cognition. Trends in Cognitive Sciences, 17, 379-390.