Time scales of representation in the human brain: weighing past information to predict future events

Lee M. Harrison, Sven Bestmann, Maria Joao Rosa, William Penny, Gary G. R. Green

Research output: Contribution to journalArticlepeer-review


The estimates that humans make of statistical dependencies in the environment and therefore their representation of uncertainty crucially depend on the integration of data over time. As such, the extent to which past events are used to represent uncertainty has been postulated to vary over the cortex. For example, primary visual cortex responds to rapid perturbations in the environment, while frontal cortices involved in executive control encode the longer term contexts within which these perturbations occur. Here we tested whether primary and executive regions can be distinguished by the number of past observations they represent. This was based on a decay-dependent model that weights past observations from a Markov process and Bayesian Model Selection to test the prediction that neuronal responses are characterized by different decay half-lives depending on location in the brain. We show distributions of brain responses for short and long term decay functions in primary and secondary visual and frontal cortices, respectively. We found that visual and parietal responses are released from the burden of the past, enabling an agile response to fluctuations in events as they unfold. In contrast, frontal regions are more concerned with average trends over longer time scales within which local variations are embedded. Specifically, we provide evidence for a temporal gradient for representing context within the prefrontal cortex and possibly beyond to include primary sensory and association areas.

Original languageEnglish
Article number37
Number of pages8
JournalFrontiers in human neuroscience
Issue numberAPRIL
Publication statusPublished - 26 Apr 2011


  • uncertainty
  • information theory
  • surprise
  • functional MRI
  • Bayesian spatial models
  • Bayesian model selection
  • FMRI

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