Pre-stimulus alpha oscillations encode stimulus-specific visual predictions
Hetenyi, D.; Haarsma, J.; Kok, P.
Show abstract
Predictions of future events have a major impact on how we process sensory signals. However, it remains unclear how the brain keeps predictions online in anticipation of future inputs. Here, we combined magnetoencephalography (MEG) and multivariate decoding techniques to investigate the content of perceptual predictions and their frequency characteristics. Participants were engaged in a shape discrimination task, while auditory cues predicted which specific shape would likely appear. Frequency analysis revealed significant oscillatory fluctuations of predicted shape representations in the pre-stimulus window in the alpha band (10 - 11Hz). Furthermore, we found that this stimulus-specific alpha power was linked to expectation effects on shape discrimination. Our findings demonstrate that sensory predictions are embedded in pre-stimulus alpha oscillations and modulate subsequent perceptual performance, providing a neural mechanism through which the brain deploys perceptual predictions.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Disentangling Semantic Composition and Semantic Association in the Left Temporal Lobe 97%
- A shared theta-rhythmic process for selective sampling of environmental information and internally stored information 96%
- Spatial processing of limbs reveals the center-periphery bias in high level visual cortex follows a nonlinear topography 95%
Similar papers in this journal
- THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior 96%
- Linguistic processing of task-irrelevant speech at a Cocktail Party 96%
- Differential destinations, dynamics, and functions of high- and low-order features in the feedback signal during object processing 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.