Functional division and synergy of cognitive control and salience processing in category-based attentional selection: Evidence from fMRI

Category-based attentional selection (CAS) enables the visual system to prioritize objects that share an abstract, semantic label. For example, “tools,” “letters,” or “animals.” Yet how cognitive load and salience processing jointly sculpt this high-level form of attention remains unclear. Here we combined a Majority Function Task (MFT) with a visual Oddball manipulation in a fully crossed 2 (load: low 3:0 vs. high 2:1 ratio) × 2 (salience level: standard 80 % vs. Oddball 20 %) × 2 (salience relevance: task-relevant vs. task-irrelevant) design. Twenty-nine right-handed adults (24 women; 18−27 yrs) performed 768 trials while BOLD signals were recorded in a 3 T scanner; eye position was concurrently monitored to rule out overt shifts. Inverse-efficiency scores (IES = RT / accuracy) confirmed the expected main effect of load, but also revealed a three-way interaction: under high load, task-relevant Oddballs produced the largest cost (Cohen’s d = 0.81), whereas task-irrelevant Oddballs caused a moderate, load-dependent slowdown. This pattern supports a resource-competition account in which maintaining a category template and suppressing conspicuous distractors draw on a common, finite pool. Whole-brain GLM revealed a functional division of effects. Cognitive load (high > low) boosted activity throughout the dorsal control network, including bilateral superior parietal lobule (SPL), dorsal lateral prefrontal cortex (DLPFC) and insula, whereas salience level (Oddball > standard) preferentially recruited ventral salience nodes, including right angular gyrus, bilateral anterior insula and caudate nucleus. By contrast, salience relevance (task-relevant vs. task-irrelevant) produced no reliable univariate clusters, mirroring the absence of a pure relevance main effect in local BOLD amplitude. To test whether relevance information was nonetheless encoded in spatial patterns, we performed multivariate pattern analysis (MVPA). A linear support-vector machine trained on voxels that were jointly responsive to load and salience distinguished the eight experimental conditions with 86.83 % accuracy (t= 73.57, p These converging results indicate that CAS operates through a layered priority architecture: dorsal control regions inject goal-related gain, ventral salience regions register statistical deviance, and rPOJ/FEF synergistically re-weights both streams to rebalance priority values when resources are scarce. Taken together, our findings extend priority-map theory into the semantic domain and demonstrate that cognitive load is a key moderator of how salience relevance shapes the competition between dorsal and ventral attention systems. By isolating where (dorsal vs. ventral) and how (pattern vs. amplitude) cognitive load and salience relevance interact, the study refines dual-route models of attention and identifies rPOJ and FEF as pivotal hubs for balancing task demands against environmental conspicuity, that is, a mechanism likely critical for real-world scenarios that call for rapid category-based decisions under pressure.