Toward individual-level calibration in affect recognition with perceptual adjustment queries

Abstract: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.

14 pages, 13 figures. Accepted as a poster at IEEE ACII 2026

Machine Learning (cs.LG)

arXiv:2609.21073 [cs.LG]

(or arXiv:2609.21073v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.21073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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