July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
A cause for non-Granger causality
Author Affiliations
  • Yury Petrov
    Psychology Department, Northeastern University
Journal of Vision July 2013, Vol.13, 1194. doi:10.1167/13.9.1194
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Yury Petrov; A cause for non-Granger causality. Journal of Vision 2013;13(9):1194. doi: 10.1167/13.9.1194.

      Download citation file:


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

      ×
  • Supplements
Abstract

Identifying causal interactions in neural ensembles is, generally, a challenging task when multiple variables, causal loops, self-interactions, and strong nonlinearities are present. The Granger causality (GC) along with its multivariate and nonlinear descendants recently gained popularity. GC paradigm is based on removing one variable at a time from the ensemble and estimating how this affects predictability of the remaining variables. This approach cannot be used to evaluate self-interactions and, besides, has a strong tendency to weaken causality estimates when applied to non-separable systems (both linear and nonlinear), where a variable’s information is not completely removed by simply removing the variable. For example, consider a variable A, which causally influences variables B and C, A->C having a longer time lag. A->C causality will be underestimated by GC-based measures compared to A->B causality because A’s information is encoded in B even with A eliminated. Here a non-GC approach, which avoids such artifacts, is proposed. The variable tested for causality is never removed from the ensemble, its causal input is estimated from the full ensemble predictions instead. Based on this approach two new causality measures are proposed, one (linear) based on multivariate autoregressive fit (CARMA), the other (nonlinear) based on higher-order mutual information estimates (CIM). The measures were tested on various simulated and biological/neural datasets and compared favorably with GC-based measures. When applied to visually evoked potentials CARMA and CIM produced similar results, hence validating them given the very different methods. Strong causal interactions were observed between occipital (OP) and lateral-occipital (LO) scalp regions, when the stimulation (contrast reversal) was contralateral to the ROIs. For both measures the LO->OP causality peaked around 100 - 150 msec from the stimulus onset. CIM indicated ~5 msec time lag of the causal interaction, possibly, a feedback from the LOC/MT complex to early visual areas V1/V2/V3.

Meeting abstract presented at VSS 2013

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×