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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

Temporal Prediction in Non-Deterministic Continuous Environments: Investigating the Role of Oscillatory Entrainment and Interval Learning

Elmira Hosseini¹, Assaf Breska²; ¹Max-Planck Institute, ²Max Planck institute for biological cybernetics

Presenter: Elmira Hosseini

Interaction with our continuously changing environment relies on anticipating timing of events, enhancing information processing efficiency. Abundant research has investigated temporal prediction in deterministic environments such as isochronous rhythms, where the presumed mechanism is Oscillatory Entrainment (OE) to external rhythms. However, in everyday life, continuous streams lack fully-deterministic temporal regularities. Previous research of temporal prediction in uncertain environments has focused on isolated intervals, suggesting a Distributional-Learning (DL) model. However, in non-deterministic streams, if and under which conditions either of these mechanisms drives prediction is unclear. To address this, we combined computational modeling of the two mechanisms (OE and DL) and human behavioral experiments. We found that while models are affected differently by the degree of variability in the environment, they lead to more overlapping predictions in lower degrees of variability. Next, we used the models generatively to create streams with differential temporal predictions by these two mechanisms, and presented targets at either predicted timepoint to participants conducting a speeded response task. Participants’ behavior followed OE predictions in environments with relatively lower degrees of variability to which they were sequentially exposed. Overall, these results highlight the inherent differences between OE and DL mechanisms in dealing with uncertainty, and reveal the flexibility of OE in adapting to partial irregularities, and its independence from DL.

Topic Area: Brain Networks & Neural Dynamics

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