Loss of reliable temporal structure in event-related averaging of naturalistic stimuli

Aya Ben-Yakov, Christopher J. Honey, Yulia Lerner, Uri Hasson

Research output: Contribution to journalComment/debatepeer-review

35 Scopus citations


To separate neural signals from noise, brain responses measured in neuroimaging are routinely averaged across space and time. However, such procedures may obscure some properties of neural activity. Recently, multi-voxel pattern analysis methods have demonstrated that patterns of activity across voxels contain valuable information that is concealed by spatial averaging. Here we show that temporal patterns of neural activity contain information that can discriminate different stimuli, even within brain regions that show no net activation to that stimulus class. Furthermore, we find that in many brain regions, responses to natural stimuli are highly context dependent. In such cases, prototypical event-related responses do not even exist for individual stimuli, so that averaging responses to the same stimulus within different contexts may worsen the effective signal-to-noise. As a result, analysis of the temporal structures of single events can reveal aspects of neural dynamics which cannot be detected using standard event-related averaging methods.

Original languageEnglish (US)
Pages (from-to)501-506
Number of pages6
Issue number1
StatePublished - Oct 15 2012

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience


  • Event-related averaging
  • Inter-subject correlation
  • Natural stimuli
  • Signal reliability


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