Implementation and simulation of the Scalar Timing Model

Perception of time is an important ability: timing plays a crucial role in accomplishing various real-life tasks. This work concerns judgments made about short temporal intervals. The main goal was to implement the well known Scalar Timing Model (STM) as an artificial neural network. The implementation of the STM resolves to mapping the architecture of the model into an artificial neural network. Another direction of experiments concerns the pacemaker and distribution of generated pulses in time. Manipulation of pulse distribution in time helps in explaining basic, empirical regularities concerning perception of time, and the relation between the stimulus and its sensation. The STM implementation described in this paper models the time-order error (TOE), a phenomenon often reported in experiments on living organisms. This research is a starting point for further wide-scale analysis of human time perception on psychological, psychophysical and biological levels.