Xiaoyang Huang, Sihua Xu
Excessive choice can overwhelm cognitive resources and trigger choice overload, yet its neurophysiological basis-particularly the moderating role of overall preference level-remains underexplored. This study employed a two-stage experimental paradigm manipulating choice set size (large vs. small) and overall preference level (high vs. low). We integrated event-related potentials (ERPs), multivariate pattern analysis (MVPA), and hierarchical drift diffusion modeling (HDDM) to investigate how these factors interactively shape decision processes. Behavioral and computational modeling results revealed that high-preference conditions enhanced participants' ability to identify satisfactory options, with this advantage persisting and significantly accelerating final selection speed, particularly for large choice sets. Conversely, low-preference conditions amplified choice set size effects, with large sets exacerbating choice overload. ERP analyses showed larger P2 amplitudes for small choice sets, indicating greater early attentional allocation. More negative N2 amplitudes consistently appeared for small sets across both overall preference levels, reflecting elevated conflict and cognitive control demands. Small-set/low-preference conditions elicited the largest P3 amplitudes, suggesting small sets triggered compensatory attentional allocation under low-preference conditions. MVPA identified stable and distinct neural representation patterns across all experimental conditions, confirming that overall preference level modulates neural encoding of choice overload. These findings demonstrate that subjective preference strength functions as a key regulatory factor in mitigating choice overload. Our multimodal approach advances theoretical accounts of value-based decision-making by revealing how internal preferences interact with external complexity to shape the temporal and computational architecture of cognitive control.