Disentangling Temporal Dynamics in Attention Bias From Measurement Error: A State-Space Modeling Approach

Temporal dynamics in attention bias (AB) have gained increasing attention in recent years. It has been proposed that AB is variable over trials within a single test session of the dot-probe task, and that the variability in AB is more predictive of psychopathology than the traditional mean AB score. More important. one of the dynamics indices has shown better reliability than the traditional mean AB score. However, it has been also suggested that the dynamics indices are unable to uncouple random measurement error from true variability in AB, which questions the estimation precision of the dynamics indices. To clarify and overcome this issue, the current article introduces a state-space modeling (SSM) approach to estimate trial-level AB more accurately by filtering random measurement error. The estimation error of the extant dynamics indices versus SSM were evaluated by computer simulations with different parameter settings for the temporal variability and between-person variance in AB. Throughout the simulations, SSM showed robustly lower estimation error than the extant dynamics indices. We also applied these indices to real data sets, which revealed that the dynamics indices overestimate within-person variability relative to SSM. Here SSM indicated less temporal dynamics in AB than previously proposed. These findings suggest that SSM might be a better alternative to estimate trial level AB than the extant dynamics indices. However, it is still unclear whether AB has meaningful in-session variability that is predictive of psychopathology.