The perceived spatio-temporal relations between local sensorimotor events can be very different from actual distances, durations and even temporal order. In fact, it has been shown that prior expectations, adaptation and other phenomena can warp and shift the subjective metric of space-time, producing observable effects like spatial and temporal recalibration, intentional binding and time reversal illusions. This project aims at characterizing the properties and dynamics of the structure of subjective sensorimotor space-time. The first part of the project focuses on the temporal dimension, specifically on learning, adaptation and recalibration phenomena in sensorimotor timing tasks. The principal aim consists in clarifying the role of the statistical properties of the context (e.g. priors, likelihoods, loss functions, in Bayesian terms) in typically ‘non-Bayesian’ phenomena, such as temporal recalibration. The second part of the project aims at studying phenomena of learning, adaptation and recalibration in a non-separable spatiotemporal domain. The methods of inquiry combine probabilistic (Bayesian) and computational modelling, machine learning techniques and psychophysical experiments, in a joint collaboration between Edinburgh (SLMC-IPAB) and Cambridge (WolpertLab).
Related Publications and Presentations
- Luigi Acerbi, and Sethu Vijayakumar, “Bayesian causal inference drives temporal sensorimotor recalibration”, COSYNE, 2011.
- Luigi Acerbi, Daniel Wolpert, and Sethu Vijayakumar, “Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing”, PLOS Computational Biology, 2012, 8(11), e1002771.
- Luigi Acerbi, Daniel Wolpert, and Sethu Vijayakumar, “Internal representations of temporal statistics and feedback in sensorimotor interval timing”, COSYNE, 2012.
- Luigi Acerbi, “Synchronizing the Bayesian clock: a view on cognitive probabilistic modelling through time perception”, Models and Mechanisms in Cognitive Science Workshop, 2011.