September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Representational dynamics of ensemble average of simultaneously presented objects
Author Affiliations
  • Kangyong Eo
    Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon, Republic of Korea
  • Oliver James
    Center for Basic Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of Korea
  • Sangkyu Son
    Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon, Republic of Korea
  • Min-Suk Kang
    Center for Basic Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Republic of KoreaDepartment of Psychology, Sungkyunkwan University, Seoul, Republic of Korea
  • Sang Chul Chong
    Graduate Program in Cognitive Science, Yonsei University, Seoul, Republic of KoreaDepartment of Psychology, Yonsei University, Seoul, Republic of Korea
  • Yee-Joon Kim
    Center for Cognition and Sociality, Institute for Basic Science (IBS), Daejeon, Republic of Korea
Journal of Vision September 2018, Vol.18, 80. doi:10.1167/18.10.80
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      Kangyong Eo, Oliver James, Sangkyu Son, Min-Suk Kang, Sang Chul Chong, Yee-Joon Kim; Representational dynamics of ensemble average of simultaneously presented objects. Journal of Vision 2018;18(10):80. doi: 10.1167/18.10.80.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Ensemble representation is a dimensionality reduction mechanism to deal with information overload by extracting a central tendency from a set of stimuli. Behavioral studies showed that ensemble average of similar stimuli is automatically represented and that the error of ensemble average increases as inter-stimulus variability increases. However, very little is known about neural mechanisms supporting such properties of ensemble representation. Inferred from the recently developed population model of mid-level vision that explains irretrievable loss of information due to the increase in spatial pooling with eccentricity, we hypothesize that ensemble average of a specific feature is a coarse-grained representation wherein high-dimensional neural population activity is projected onto low-dimensional subspace linearly spanned by the basis feature channels. To test this hypothesis, we recorded electroencephalography (EEG) while observers performed a series of tasks related to computing average orientation of an array of 36 randomly oriented Gabor patches with various inter-Gabor orientation variability. We used a forward encoding model to decode neural representation of ensemble average orientation from full EEG signals. We found that the accuracy of recovered ensemble average orientation decreases as inter-Gabor orientation variability increases. Next, we investigated if representational dynamics of ensemble average orientation varies with task demand or not. Initial theta band activity pattern was found to represent ensemble average orientation for 500 ms regardless of whether observers were required of computing ensemble average or not. Furthermore, after transient theta band activity pattern disappeared, alpha band activity pattern was observed to maintain the neural representation of ensemble average orientation for about 800 ms only when observers were required of computing ensemble average. This multiplexed spectral code of ensemble average information may index the variance of neural computations, which translates into the precision of behavioral performance.

Meeting abstract presented at VSS 2018

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