Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli
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Abstract
Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between 2 alternative computational models. The "Opposition" model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the "Interaction" model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modeling showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.
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Expectation suppression is observed in non-invasive fMRI BOLD and EEG/MEG signals, and in spike rates in non-human primates and rodents.
“Moreover, expectation suppression is observed not only in non-invasive fMRI blood oxygen level-dependent (BOLD) [65,72,73] and electroencephalography (EEG)/magnetoencephalography (MEG) signals [68,69,77], but also in spike rates in non-human primates [66,67,74,76] and rodents [70,75,78].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [77]
Studies that independently manipulated expectation and attention showed that attention can counteract or even reverse the suppressive effects of expectation.
“Indeed, studies that independently manipulated the two showed that attention can counteract [77,91] or even reverse [92] the suppressive effects of expectation.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [77]
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How Do Expectations Shape Perception?
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Evaluating the neurophysiological evidence for predictive processing as a model of perception
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- Authors
- Garrido, M. I., Rowe, E. G., Halász, V., & Mattingley, J. B.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Garrido, Marta I
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-04-10
- Volume
- 28
- Issue
- 5
- Pages
- 1771-1782
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2952209665
- Cited by
- 44
- References
- 26
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 5 · 2022: 6 · 2021: 5 · 2020: 8 · 2019: 6 · 2018: 3 · 2017: 2
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Australian Research Council
- Discovery Early Career Researcher Award (DE130101393)
- University of Queensland Fellowship (2016000071)
- ARC Australian Laureate Fellowship (FL110100103)
- ARC Centre of Excellence for Integrative Brain Function (CE140100007)
- ARC Special Research Initiative - Science of Learning Research Centre (SR120300015)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- attention, predictive coding, expectations, sensory prediction error, mismatch negativity, predictability, Bayesian model selection, dynamic causal modeling, oddball paradigm, auditory attention, posterior probability maps, computational model of attention
- MeSH terms
- Acoustic Stimulation, Adult, Attention, Auditory Perception, Bayes Theorem, Brain, Brain Mapping, Electroencephalography, Evoked Potentials, Auditory, Female, Humans, Male, Models, Neurological, Predictive Value of Tests, Psychoacoustics, Young Adult, Healthy Volunteers
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