Related Experiment Video
Updated: Jan 13, 2026

Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills
Published on: January 19, 2024
The anticipation of imminent events is time-scale invariant
Matthias Grabenhorst1,2, David Poeppel3, Georgios Michalareas1,2,4
1Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt 60528, Germany.
Abstract:
Humans predict the timing of imminent events to generate fast and precise actions, decisions, and other behaviors. Such temporal anticipation is critical over wide timescales, and especially salient over the range from hundreds of milliseconds to a few seconds. Despite advances in our understanding of basic timing behavior and its underlying neural mechanisms, it remains an open question whether anticipation is stable across these short time scales. Recent work shows that the brain models the probability density function (PDF) of events across time, suggesting a canonical mechanism for temporal anticipation. Here, we investigate whether this computation holds when the event distribution covers different time spans. We show that, irrespective of the time span, anticipation, measured as reaction time, scales with the event distribution. This demonstrates that the key computation-the estimation of event probability density-is invariant across temporal scales. We further show that the precision of anticipation is also scale invariant which contradicts Weber's law. The results are established in vision and audition, suggesting that the core computations in temporal anticipation are independent of sensory modality. Perceptual systems exploit probability estimation over time independently of temporal scale to anticipate imminent events.
Related Concept Videos
Linear time-invariant Systems
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
Classification of Systems-II
Hindsight Biases
Properties of DTFT I
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
Basic Operations on Signals
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
Convolution Properties I
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

