Presentation Information
[O1-04]Moments or Continuum? Testing the Temporal Resolution of Human Anticipation
*GEORGIOS MICHALAREAS1,2,3, David Poeppel4, Matthias Grabenhorst3,2 (1. Cooperative Brain Imaging Center (CoBIC), Goethe University Frankfurt (Germany), 2. Max-Planck-Institute for Empirical Aesthetics, Frankfurt (Germany), 3. Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt (Germany), 4. New York University (United States of America))
Keywords:
Temporal resolution,Anticipation,Event probability,Sampling,Interval timing
When we predict when something will occur, do we sweep a continuous timeline or focus on a handful of privileged instants? We addressed this question in a Set-Go paradigm that orthogonally manipulated two factors. First, we shaped the time-to-event (Go-time) probability over a 0.4–1.4s time interval so that it rose linearly, fell linearly, or remained flat. Second, we discretised this time interval into 3, 5, 9, or 15 Go-time sampling points, parameterising temporal granularity from coarse to fine.
Because humans rapidly internalise a probability-density function (PDF)1 we expected all participants to learn the rising, falling, or flat probability trend. Against this backdrop, three rival hypotheses were tested by the different sampling resolutions.First, according to the “selective-gain hypothesis”, widely spaced Go-times—beyond the scalar noise of interval timing (≈10 % of the interval)2—allow the brain to spotlight individual time points, yielding faster responses there. In contrast, the “chunking-cost hypothesis” suggests that sparse Go-times lead to discrete attentional episodes3. Transitioning between these episodes adds cognitive load and slows down responses. Finally, the “resolution-invariant hypothesis” proposes that the brain relies solely on the continuous PDF, regardless of sampling resolution³.
We tested the effect of temporal granularity in both visual and auditory modalities. The results showed that Reaction Times were highly similar across sampling conditions—arguing against selective-gain or chunking processes, in the case of a small number of sampling points. Temporal anticipation was primarily driven by the event probability distribution, highlighting the importance of the macroscale characteristics of event probabilities over their temporal microstructure.
References
1. M. Grabenhorst, G. Michalareas, et al., Nat. Commun. 10, 5805 (2019)
2. J. Gibbon, Psychol. Rev. 84, 279–325 (1977).
3. E. G. Akyürek, Neurosci. Biobehav. Rev. 170, 106041 (2025).
4. A.C. Nobre, F. van Ede, Nat. Rev. Neurosci. 19, 34–48 (2018).
Because humans rapidly internalise a probability-density function (PDF)1 we expected all participants to learn the rising, falling, or flat probability trend. Against this backdrop, three rival hypotheses were tested by the different sampling resolutions.First, according to the “selective-gain hypothesis”, widely spaced Go-times—beyond the scalar noise of interval timing (≈10 % of the interval)2—allow the brain to spotlight individual time points, yielding faster responses there. In contrast, the “chunking-cost hypothesis” suggests that sparse Go-times lead to discrete attentional episodes3. Transitioning between these episodes adds cognitive load and slows down responses. Finally, the “resolution-invariant hypothesis” proposes that the brain relies solely on the continuous PDF, regardless of sampling resolution³.
We tested the effect of temporal granularity in both visual and auditory modalities. The results showed that Reaction Times were highly similar across sampling conditions—arguing against selective-gain or chunking processes, in the case of a small number of sampling points. Temporal anticipation was primarily driven by the event probability distribution, highlighting the importance of the macroscale characteristics of event probabilities over their temporal microstructure.
References
1. M. Grabenhorst, G. Michalareas, et al., Nat. Commun. 10, 5805 (2019)
2. J. Gibbon, Psychol. Rev. 84, 279–325 (1977).
3. E. G. Akyürek, Neurosci. Biobehav. Rev. 170, 106041 (2025).
4. A.C. Nobre, F. van Ede, Nat. Rev. Neurosci. 19, 34–48 (2018).