P155 Bayesian Modelling of Explicit and Implicit Timing
Gianvito Laera*1,2,3,4, Matthew Vowels5,6,7, Tasnim Daoudi1,2, Richard Andrè1,2, Sam Gilbert8, Sascha Zuber2,3, Matthias Kliegel1,2,3, Chiara Scarampi2,3
1Cognitive Aging Lab (CAL), Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland
2Centre for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Switzerland
3LIVES, Overcoming Vulnerability: Life Course Perspective, Swiss National Centre of Competence in Research, Switzerland
4University of Applied Sciences and Arts Western Switzerland HES-SO, Geneva School of Health Sciences, Geneva Musical Minds lab (GEMMI lab), Geneva, Switzerland
5Institute of Psychology, University of Lausanne, Switzerland
6The Sense Innovation and Research Center, CHUV, Switzerland
7Centre for Vision, Speech and Signal Processing, University of Surrey, Switzerland
8Institute of Cognitive Neuroscience, University College London, London, United Kingdom
*Email: gianvito.laera@unige.ch
Introduction
Time perception supports adaptive behavior by allowing anticipation of critical events [1]. Explicit timing involves conscious estimation of durations (e.g., interval reproduction), typically modeled by Bayesian frameworks combining noisy sensory evidence with prior expectations [2]. Implicit timing emerges indirectly through tasks like foreperiod paradigms, relying on neural or motor strategies without temporal awareness. Historically treated separately, explicit tasks engage cortico-striatal circuits, whereas implicit tasks involve cerebellar or parietal regions. We hypothesized that a unified Bayesian model with a shared internal clock parameter (θ) could bridge explicit and implicit timing abilities.
Methods
Forty-five psychology students performed four within-participant tasks: Explicit Motor (spontaneous motor response), Implicit Motor (simple reaction time), Explicit Temporal (interval reproduction), and Implicit Temporal (stimulus prediction). A hierarchical Bayesian model estimated an internal clock rate parameter (θ), reflecting subjective timing (θ=1 accurate; θ>1 slower; θ<1 faster clock), alongside parameters modeling task-specific variability and individual learning effects. Explicit tasks involved duration reproduction without feedback; implicit tasks involved temporal anticipation of a stimulus. Markov Chain Monte Carlo (MCMC) sampling via Stan was used for parameter estimation.
Results
The Bayesian model indicated participants’ internal clocks ran faster than objective time (μθ≈0.80), explaining interval overestimation at short durations and confirming a regression-to-mean effect. Individual differences in θ were significant (τθ≈0.20); participants with fewer practice trials had internal clocks closer to accuracy, indicating efficient learning. Explicit tasks had higher variability than implicit tasks, confirming greater cognitive uncertainty. Implicit tasks showed typical foreperiod effects (longer expected intervals slightly slowed reaction times,a≈0.3). Explicit and implicit timing shared moderate variance (r≈0.45), and network analysis suggested θ centrally bridged both timing domains.
Discussion
The findings support a unified Bayesian model, highlighting a shared internal clock mechanism underlying explicit and implicit timing. The internal clock parameter (θ) explained significant individual differences across tasks, supporting recent integrative views proposing partially overlapping neural substrates [3, 4]: a common cognitive mechanisms (maybe striatal-thalamo-cortical circuits) provide duration information utilized differently across explicit versus implicit tasks. Task-specific differences also comprise additional factors (e.g., cognitive strategies, attention and memory load) that future versions of the model should include. The model can be promising in explaining timing difficulties in clinical and aging populations too.
AcknowledgementsNone
References
1. https://doi.org/10.1016/j.neuropsychologia.2012.08.017
2. https://doi.org/10.1016/j.tics.2013.09.009
3. https://doi.org/10.1016/j.cobeha.2016.01.004
4. https://doi.org/10.1016/j.tins.2004.10.007
