Supramodal and modality-specific neural information supports multi-feature prediction errors across cortical levels

Predictive coding posits that the brain actively anticipates inputs from different senses, generating prediction errors when incoming information deviates from internal expectations. While much research has focused on prediction errors elicited by violations of single sensory features, natural environments frequently present more complex events deviating across multiple stimulus dimensions and sensory modalities. In this study, we employed a hierarchical oddball paradigm (n = 30) manipulating auditory and somatosensory stimuli to violate one or two sensory features while high-density EEG was recorded. Temporal decoding revealed that while both single- and double-deviants evoked sustained supramodal activation patterns, double-deviants uniquely elicited a supramodal response starting at 100 ms after the oddball. Effective connectivity analyses identified shared interhemispheric interactions between inferior frontal gyri across modalities, as well as distinct modality-specific connectivity within early and associative sensory cortices. Our findings demonstrate that multi-feature prediction errors recruit both rapid supramodal integration mechanisms and hierarchically organized modality-specific pathways. These results advance our understanding of how the brain flexibly integrates multiple sensory expectation violations across different levels of cortical processing, providing new insights into the neural architecture supporting predictive perception.

Authors

Institutions

Publication Details

Journal
Imaging Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/imag.a.149
Citations
1
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
1.43
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Supramodal and modality-specific neural information supports multi-feature prediction errors across cortical levels

Maria Niedernhuber, Micah Allen, Francesca Fardo, Tristan Bekinschtein
1 citations
Imaging Neuroscience
Functional Brain Connectivity Studies
1.43
article

Supramodal and modality-specific neural information supports multi-feature prediction errors across cortical levels

Maria Niedernhuber, Micah Allen, Francesca Fardo, Tristan Bekinschtein
article en
1 citations

Abstract

Predictive coding posits that the brain actively anticipates inputs from different senses, generating prediction errors when incoming information deviates from internal expectations. While much research has focused on prediction errors elicited by violations of single sensory features, natural environments frequently present more complex events deviating across multiple stimulus dimensions and sensory modalities. In this study, we employed a hierarchical oddball paradigm (n = 30) manipulating auditory and somatosensory stimuli to violate one or two sensory features while high-density EEG was recorded. Temporal decoding revealed that while both single- and double-deviants evoked sustained supramodal activation patterns, double-deviants uniquely elicited a supramodal response starting at 100 ms after the oddball. Effective connectivity analyses identified shared interhemispheric interactions between inferior frontal gyri across modalities, as well as distinct modality-specific connectivity within early and associative sensory cortices. Our findings demonstrate that multi-feature prediction errors recruit both rapid supramodal integration mechanisms and hierarchically organized modality-specific pathways. These results advance our understanding of how the brain flexibly integrates multiple sensory expectation violations across different levels of cortical processing, providing new insights into the neural architecture supporting predictive perception.

Imaging NeuroscienceVol. 3
Aarhus University (DK), University of Cambridge (GB), Upsher-Smith Laboratories (United Kingdom) (GB), Danish Pain Research Center (DK)
Openalex Percentile: Top 16%
Functional Brain Connectivity Studies
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Supramodal and modality-specific neural information supports multi-feature prediction errors across cortical levels — Maria Niedernhuber, Micah Allen, et al. · Imaging Neuroscience (2025) | TGRS Research Map | TGRS