Testing the Cross-Paradigm Convergence of Behavioral and Neural Measures of Attention

Attention abilities, that are critical for most real-life cognitive task, can vary greatly across individuals. A plethora of behavioral and neural metric are commonly utilized to quantify attention; however, these often yield inconsistent and non-replicable results, raising questions as to which measures reliably account for and capture individual differences. To address this tension, here we employed a within-subject cross-paradigm design and quantified both behavioral and EEG-based neural measures associated with attention functioning, to test which measures converged across a gradient of artificial and ecological tasks. We found Inter-Subject Correlation (ISC), which quantifies the similarity in the pattern of neural response across individuals, captured consistent individual differences across both an artificial (Auditory Oddball) and ecological (Attention to Speech) task, with some correlation with performance. Moreover, we found that P300 neural responses to surprising events were qualitatively similar across artificial and ecological tasks, supporting their generalization across contexts. Interestingly, traditional cognitive-behavioral measures of attention - both from speeded response-time tasks and self-report of ADHD symptoms (ASRS) were not correlated with each other nor did they explain variance in neural metrics, nor were they explained by variation in general cognitive abilities (working memory or fluid intelligence). While these results demonstrate the shortcomings of many established measures of attention to generalize across contexts, they also point to the potential of neural measures, and specifically ISC, to serve as reliable indicators of individual differences and to bridge the gap between artificial and ecological studies of attention functioning.