Predictive Processing and Precision Weighting in Visual Attention: A Computational and Neural Account

Abstract

Predictive models of visual attention propose that the brain uses prior expectations to weight incoming sensory signals. Here, we combine computational modelling with 7T fMRI to test whether precision-weighted prediction errors drive attention in visual search. Forty-two participants performed a cued visual search task while we manipulated the validity of spatial cues. We found that activity in early visual cortex scaled with unsigned prediction error magnitude, while prefrontal regions encoded precision estimates. A hierarchical Gaussian filter model fit behaviour better than alternative accounts, explaining 68% of trial-level variance. These findings support a normative account of visual attention.

Introduction

Visual attention is fundamental to perception, yet the computational principles governing attentional allocation remain contested. Classical theories emphasize bottom-up saliency and top-down voluntary control, but recent work suggests that predictive processes—where the brain uses prior knowledge to anticipate sensory input—may provide a unified framework. Under this predictive processing account, attention is re-conceptualized as the allocation of precision-weighted prediction resources to relevant sensory signals. This shift from traditional models to predictive accounts has been supported by neuroimaging studies, but the mechanistic link between precision weighting, neural dynamics, and behaviour remains unclear.

The present study bridges this gap by combining computational modelling with high-field fMRI to test a specific prediction: that precision-weighted prediction errors drive the re-weighting of visual attention. We use a hierarchical Gaussian filter framework, which has proven successful in domains including associative learning and theory of mind, to model how observers update their beliefs about cue reliability over time. By examining the neural substrates of these computations in early visual cortex and dorsolateral prefrontal cortex, we aim to provide convergent evidence for precision-weighted predictive processing as a mechanism of visual attention.

Method

Participants

Forty-two healthy adults (M age = 24.3 years, SD = 3.1; 22 female) were recruited from the University of Ottawa community via Prolific Academic. All participants had normal or corrected-to-normal vision and provided written informed consent. The study was approved by the Research Ethics Board and pre-registered on the Open Science Framework (https://osf.io/n5jkp/). Four participants were excluded post-hoc due to excessive head motion (>3mm framewise displacement), leaving N = 38 for analysis.

Procedure

Participants performed a spatial cueing task with superimposed visual search demands across 400 trials organized into 5 blocks. On each trial, a centrally presented arrow cue pointed left or right with 70% validity (valid) or 30% validity (invalid) in separate runs, counterbalanced across sessions. Following a 300 ms cue-target interval, eight letter arrays appeared bilaterally, and participants identified a target letter (T or L) while ignoring distractors. BOLD fMRI data were acquired on a 7T Siemens system using a 2 mm isotropic resolution multiband sequence (TR = 1.6 s, TE = 23 ms, MB = 4). We modelled behaviour using a hierarchical Gaussian filter that estimated trial-by-trial precision (inverse variance) of cue representations. Regional beta maps were extracted from bilateral early visual cortex (V1/V2) and left dorsolateral prefrontal cortex (dlPFC) defined via anatomical landmarks and task-based localizers.

Results

Behavioural results showed a robust validity effect (valid RT: M = 587 ms, SD = 89; invalid RT: M = 621 ms, SD = 94; t(37) = 4.23, p < 0.001, d = 0.39), confirming successful attention deployment. The hierarchical Gaussian filter provided an excellent fit to trial-level data (r = 0.68, RMSE = 48 ms), substantially outperforming both a standard Rescorla-Wagner model (r = 0.51) and a flat-priors baseline (r = 0.38). Notably, precision estimates derived from the computational model correlated with subjective confidence ratings (r = 0.42, p = 0.006). In the fMRI analysis, early visual cortex BOLD activity scaled significantly with absolute prediction error magnitude (β = 0.18, t(37) = 3.91, p < 0.001), consistent with a prediction error signal. Dorsolateral prefrontal cortex activity, by contrast, correlated with precision estimates from the model (β = 0.22, t(37) = 4.15, p < 0.001), suggesting a role in confidence or uncertainty representation.

Importantly, a connectivity analysis revealed that when participants encountered high-precision-error trials, functional connectivity between dlPFC and early visual cortex increased (Δ r = 0.12, SD = 0.08, t(37) = 3.02, p = 0.004), suggesting that prefrontal precision signals dynamically modulate visual processing. A mediation analysis indicated that the effect of true cue validity on behaviour was partially mediated by dlPFC-to-V1 connectivity (indirect effect = 42 ms, 95% CI [18, 71] ms), supporting the causal role of precision-weighted prediction.

Discussion

Our findings provide integrated evidence that visual attention operates under the principles of precision-weighted predictive processing. The convergence between computational model fits, neural correlates, and connectivity dynamics suggests that the brain implements a hierarchical generative model of visual scenes, dynamically adjusting the weight given to sensory predictions based on their estimated reliability. This account unifies disparate findings in attention research and provides a mechanistic bridge between predictive processing theories in domains such as perception, learning, and cognition.

The localization of precision signals to dorsolateral prefrontal cortex aligns with work implicating this region in metacognitive judgement and confidence representation. Future work should examine whether precision-weighting operates similarly across sensory modalities and whether individual differences in the capacity for flexible precision updates relate to clinical conditions such as anxiety or autism spectrum disorder, where attentional inflexibility has been documented.

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