Quantifying Individual Recognition Sensitivity to Static and Dynamic Facial Expressions

Dynamic facial expressions of emotion are typically recognized more accurately than static expressions, especially among individuals with immature, vulnerable or impaired facial expression recognition (FER) systems, such as children, older adults, and individuals with clinical conditions. These findings underscore the need of assessing both dynamic and static stimulus formats and suggest that the dynamic advantage could serve as a potential individual-level marker of FER impairment. However, previous research has primarily focused on group-level effects, often overlooking critical individual differences. Here, we tested whether the QUEST threshold-seeking algorithm can efficiently estimate the minimal signal required for accurate recognition of static and dynamic facial expressions at the individual level. We also quantified the minimum number of trials needed to obtain stable threshold estimates. To evaluate its sensitivity, we compared FER thresholds across neurotypical young adults, older adults, and a well-documented case of acquired prosopagnosia. Our findings demonstrate that the QUEST algorithm is a robust and efficient tool for rapidly estimating meaningful FER thresholds at the individual level. We also provide guidance on the minimum number of trials required to obtain stable threshold estimates and identify the facial expressions that serve as the most sensitive markers for probing the dynamic advantage. This psychophysical approach is particularly well-suited for single-subject analyses, assessment of FER in populations with limited capacities, and inclusion in comprehensive testing batteries. Collectively, this psychophysical approach enables scalable, time-efficient screening and longitudinal monitoring of FER, facilitating cross-individual and population comparisons in both research and clinical settings.