Back

cpCST: A New Continuous Performance Test for High-Precision Assessment of Attention Across the Lifespan

MacKay-Brandt, A.; Garcia-Barnett, D.; Gan, K. X.; Ripley, O.; Gazes, Y.; Milham, M.; Colcombe, S. J.

2025-06-25 animal behavior and cognition
10.1101/2025.06.22.660941 bioRxiv
Show abstract

Assessing sustained attention presents methodological challenges, particularly when spanning diverse populations whose baseline sensorimotor functioning may vary significantly. This study introduces the Continuous Performance Critical Stability Task (cpCST), a novel paradigm combining high-density sampling of behavior (30Hz), individualized calibration, and fixed-difficulty assessment to measure attentional control. In a sample of 166 adults (ages 18-76), we evaluated the psychometric properties of the cpCSTs instantaneous reaction time (iRT) metric derived through dynamic time warping. Results demonstrated exceptional reliability (bootstrap split-half r = .999), age invariance, and predictive validity for cognitive performance (flanker and Woodcock-Johnson) and cardiorespiratory fitness (est. VO2max). The cpCST achieved high temporal efficiency, with just two minutes of data correlating at r = .94 with full-task performance, outperforming a standard arrow-based flanker task. The cpCSTs individualized calibration effectively isolated attentional control processes from baseline sensorimotor function, eliminating age-related slowing effects typically observed in reaction time tasks. This approach offers methodological advantages for lifespan studies, clinical populations, integration with neurophysiological measures, and computational modeling approaches while addressing limitations of existing attention assessment paradigms.

Published in Frontiers in Psychology · training set

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.