When and How-Long: A Unified Approach for Time Perception.
Bottom Line:
This information, although rather standard in humans, is largely missing from artificial cognitive systems.In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory.The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events.
View Article:
PubMed Central - PubMed
Affiliation: Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas Heraklion, Greece.
ABSTRACT
The representation of the environment assumes the encoding of four basic dimensions in the brain, that is the 3D space and time. The vital role of time for cognition is a topic that recently attracted increasing research interest. Surprisingly, the scientific community investigating mind-time interactions has mainly focused on interval timing, paying less attention on the encoding and processing of distant moments. The present work highlights two basic capacities that are necessary for developing temporal cognition in artificial systems. In particular, the seamless integration of agents in the environment assumes they are able to consider when events have occurred and how-long they have lasted. This information, although rather standard in humans, is largely missing from artificial cognitive systems. In this work we consider how a time perception model that is based on neural networks and the Striatal Beat Frequency (SBF) theory is extended in a way that besides the duration of events, facilitates the encoding of the time of occurrence in memory. The extended model is capable to support skills assumed in temporal cognition and answer time-related questions about the unfolded events. No MeSH data available. |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC4814468&req=5
Mentions: Besides extensively testing the model with randomly specified interval times up to M simulation steps, we explore whether the output of the model exhibits the scalar characteristics that are typical observed in biological timing mechanisms (Lejeune and Wearden, 2006). Scalar timing implies that (i) measurements should vary linearly and near-accurately as time increases and (ii) the variance of perceptual mechanism increases as the duration of time also increases. To get an estimate of the scalar characteristics of the model, we have studied its ability to correctly estimate durations of 20, 25, 30, 35, 40, and 45 moments (without this limiting the model to perform successfully for in-between durations). For each one of the six durations considered here, we perform 50 statistically independent runs, feeding the model with randomly initialized oscillatory inputs. The mean and standard deviation for each one of the durations considered are shown in Table 1. Clearly, the average of the estimated intervals remains close to the true time in all cases, satisfying mean accuracy. The variance increases as the model experiences longer intervals, however, in a rate that is slower to the increase of the mean. The scalar property assumes a constant coefficient of variation (the ratio of the standard deviation to the mean), which is not true for our model. This is depicted more clearly in Figure 3, where relevant output distributions are scaled by the expected duration value. Even if the model is not fully compatible with the scalar property, Table 1 shows that the output of the model is sufficiently accurate for making the model usable in robotic systems. Nevertheless, it is worth emphasizing that, currently, the two main characteristics of the scalar property have been self-organized without any explicit instructions by the modeler. Therefore, it seems valid to assume that our model can be easily rendered fully compatible to the scalar property, by introducing a constraint for a constant coefficient of variation in the fitness function of the evolutionary design procedure. |
View Article: PubMed Central - PubMed
Affiliation: Computational Vision and Robotics Laboratory, Institute of Computer Science, Foundation for Research and Technology Hellas Heraklion, Greece.
No MeSH data available.