Search Results

You are looking at 1 - 1 of 1 items for

  • Author or Editor: Michail Maniadakis x
  • Search level: All x
Clear All

The computational modeling of cognitive processes provides a systematic means to study hidden and particularly complex aspects of brain functionality. Given our rather limited understanding of how the brain deals with the notion of time, the implementation of computational models addressing duration processing can be particularly informative for studying possible time representations in our brain. In the present work we adopt a connectionist modeling approach to study how time experiencing and time processing may be encoded in a simple neural network trained to accomplish time-based robotic tasks. A particularly interesting characteristic of the present study is the implementation of a single computational model to accomplish not only one but three different behavioral tasks that assume diverse manipulation of time intervals. This setup enables a multifaceted exploration of duration-processing mechanisms, revealing a rather plausible hypothesis of how our brain deals with time. The model is implemented through an evolutionary design procedure, making a very limited set of a priori assumptions regarding its internal structure and machinery. Artificial evolution facilitates the unconstrained self-organization of time representation and processing mechanisms in the brain of simulated robotic agents. Careful examination of the artificial brains has shown that the implemented mechanisms incorporate characteristics from both the ‘intrinsic’ time representation scheme and the ‘dedicated’ time representation scheme. Even though these two schemes are widely considered as contradictory, the present study shows that it is possible to effectively integrate them in the same cognitive system. This provides a new view on the possible representation of time in the brain, and paves the way for new and more comprehensive theories to address interval timing.

In: Timing & Time Perception