Cortical coupling reflects Bayesian belief updating in the deployment of spatial attention
Open accessAbstract
The deployment of visuospatial attention and the programming of saccades are governed by the inferred likelihood of events. In the present study, we combined computational modeling of psychophysical data with fMRI to characterize the computational and neural mechanisms underlying this flexible attentional control. Sixteen healthy human subjects performed a modified version of Posner's location-cueing paradigm in which the percentage of cue validity varied in time and the targets required saccadic responses. Trialwise estimates of the certainty (precision) of the prediction that the target would appear at the cued location were derived from a hierarchical Bayesian model fitted to individual trialwise saccadic response speeds. Trial-specific model parameters then entered analyses of fMRI data as parametric regressors. Moreover, dynamic causal modeling (DCM) was performed to identify the most likely functional architecture of the attentional reorienting network and its modulation by (Bayes-optimal) precision-dependent attention. While the frontal eye fields (FEFs), intraparietal sulcus, and temporoparietal junction (TPJ) of both hemispheres showed higher activity on invalid relative to valid trials, reorienting responses in right FEF, TPJ, and the putamen were significantly modulated by precision-dependent attention. Our DCM results suggested that the precision of predictability underlies the attentional modulation of the coupling of TPJ with FEF and the putamen. Our results shed new light on the computational architecture and neuronal network dynamics underlying the context-sensitive deployment of visuospatial attention. SIGNIFICANCE STATEMENT: Spatial attention and its neural correlates in the human brain have been studied extensively with the help of fMRI and cueing paradigms in which the location of targets is pre-cued on a trial-by-trial basis. One aspect that has so far been neglected concerns the question of how the brain forms attentional expectancies when no a priori probability information is available but needs to be inferred from observations. This study elucidates the computational and neural mechanisms under which probabilistic inference governs attentional deployment. Our results show that Bayesian belief updating explains changes in cortical connectivity; in that directional influences from the temporoparietal junction on the frontal eye fields and the putamen were modulated by (Bayes-optimal) updates.
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Model-derived agent-specific trajectories of predictions and PEs have proven useful for identifying neural and physiological correlates of computational quantities in empirical data.
“Model-derived agent-specific trajectories of predictions and PEs have proven particularly useful for identifying potential neural and physiological correlates of computational quantities in empirical data (Iglesias et al., 2013b; Vossel et al., 2015; de Berker et al., 2016; Diaconescu et al., 2017; Weilnhammer et al., 2018; Katthagen et al., 2018; Palmer et al., 2019; Deserno et al., 2020; Henco”
From The generalized Hierarchical Gaussian Filter (2026) · cites this paper as Vossel et al., 2015
The Introduction and Statistical analysis of imaging data sections say that subject-specific, trialwise model estimates of beliefs and prediction precision were used to identify neural correlates in fMRI data. The paper does not report using prediction-error trajectories as imaging regressors; its reported regressors were precision-dependent attention and volatility. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn healthy adults during a combined attention/learning task, DCM for fMRI and the HGF showed that functional coupling between temporal and frontal regions was modulated by trial-wise estimates of attention (precision of the predictability of targets).
“Vossel et al. (137), for example, have demonstrated in healthy adults, using DCM for fMRI and the HGF, that during a combined attention/learning task (Posner’s paradigm), the functional coupling between temporal and frontal regions was modulated by trial-wise estimates of attention (precision of the predictability of targets).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [137]
Dynamic causal modeling allows for incorporating trial-wise computational quantities obtained from generative models applied to behavioral task data, such as precision-weighted prediction errors.
“Dynamic causal modeling also allows for incorporating trial-wise computational quantities obtained from generative models applied to behavioral task data (e.g., precision-weighted prediction errors).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [137]
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- Authors
- Vossel, S., Mathys, C., Stephan, K. E., & Friston, K. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2015-08-19
- Volume
- 35
- Issue
- 33
- Pages
- 11532-11542
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC4540794
- OpenAlex
- W1796149648
- Cited by
- 99
- References
- 65
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 4 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 15 · 2019: 9 · 2018: 10 · 2017: 15 · 2016: 9 · 2015: 2
- FWCI
- 7.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- SDL (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Wellcome Trust (091593, 091593/Z/10/Z, 088130)
- Deutsche Forschungsgemeinschaft (091593/Z/10/Z.)
- Bundesministerium für Bildung und Forschung (01GQ1401)
- Universität Zürich
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visuospatial attention, saccade programming, Bayesian belief updating, attentional reorienting, cue validity, temporoparietal junction, frontal eye field, putamen, cortical connectivity, dynamic causal modeling, Bayesian hierarchical model, probabilistic inference
- MeSH terms
- Adult, Attention, Bayes Theorem, Computer Simulation, Cues, Female, Humans, Models, Neurological, Neuronal Plasticity, Putamen, Saccades, Visual Fields, Young Adult, Spatial Processing
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