发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对面部情感识别中个体知觉差异导致任务难度不均的问题，提出基于知觉调整查询（PAQ）估计个体恰可察觉差（JND）以校准刺激距离，实验证明其优于未校准和群体校准，能显著均衡个体难度并降低反应时间。
AI 中文摘要
测量面部情感知觉的行为任务假设，相同的刺激对所有参与者施加等效的知觉难度。然而，这一假设因个体在知觉敏感性上的差异而被系统性违反。以一项情感知觉任务为测试平台，我们提出一个框架来归一化知觉难度，该框架通过认知上轻量的知觉调整查询（PAQs）直接估计每位参与者沿面部情感谱的恰可察觉差（JND）。我们利用这些由PAQ推断出的JND重新表达刺激距离，从而在知觉空间中构建难度均衡的任务。我们在一个二选一强制选择（2AFC）任务中，使用两种互补的行为测量来验证该框架：二元元认知难度判断和反应时间方差分解。我们发现，与未校准基线和群体水平的Weibull校准相比，PAQ校准在个体水平上显著均衡了感知任务难度，同时降低了平均反应时间和反应时间的被试间方差。这些结果确立了PAQ作为面部情感识别中个体化知觉校准的一种有原则且实用的工具。
英文摘要
Behavioral tasks measuring facial affect perception assume that identical stimuli impose equivalent perceptual difficulty across participants. However, this assumption is systematically violated by individual differences in perceptual sensitivity. Using an affective perception task as our testbed, we propose a framework to normalize for perceptual difficulty that directly estimates each participant's Just Noticeable Difference (JND) along the facial affect spectrum via cognitively lightweight perceptual adjustment queries (PAQs). We use these PAQ-inferred JNDs to re-express stimulus distances, constructing difficulty-equated tasks in perceptual space. We validate the framework in a Two-Alternative Forced-Choice (2AFC) task using two complementary behavioral measures: binary metacognitive difficulty judgments and response time variance decomposition. We find that PAQ calibration significantly equalizes perceived task difficulty at an individual level when compared to both the non-calibrated baseline and population-level Weibull calibration, while also reducing mean response time and between-subject variance in response time. These results establish PAQ as a principled and practical instrument for individualized perceptual calibration in facial affect recognition.
Comments14 pages, 13 figures. Accepted as a poster at IEEE ACII 2026
