Inferring When to Act from Temporal Regularities
Adaptive behaviour often requires deciding when to act in the absence of an explicit sensory cue. While temporal expectations are known to optimize behaviour when anticipated events trigger responses, it remains unclear how learned temporal regularities are transformed into internally generated decisions. Here, we developed the Temporal Inference Task, a novel virtual reality paradigm designed to…
In a groundbreaking study, researchers have uncovered a novel method for determining when to take action without relying on visual cues. The Temporal Inference Task, a virtual reality paradigm, was designed to observe this intricate process. Participants were repeatedly presented with the same visual sequence, witnessing a virtual object approach their hand.
On the majority of trials, a short No-Go signal prevented them from responding, while on a select few, the absence of this signal compelled them to act. Since Go trials had no explicit signal, participants had to deduce when the anticipated No-Go signal could no longer be expected before initiating their response.
Throughout the experiment, the timing of the No-Go signal was deliberately altered across blocks, enabling participants to learn specific temporal patterns while keeping the Go trials consistent. As a result, participants gradually adjusted their response timing based on the learned temporal regularities. This behavioural shift was paralleled by corresponding changes in parietal alpha- and beta-band desynchronization, with the pre-response desynchronization rate consistently indicating faster responses.
The findings shed light on a potential neural mechanism for converting learned temporal regularities into internal decisions regarding the optimal timing of action.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
