Mechanisms and measurement of attention in laboratory tasks versus naturalistic learning contexts

Translation of laboratory-derived metrics to quantify learning in naturalistic settings is an important goal in neuroscience. In the current study we tested if a common neural metric of visual attention, alpha-range (8–12 Hz) oscillations, generalizes from laboratory to naturalistic learning. We recorded electroencephalography (EEG) in eighty 6–10-year-old children while they participated in naturalistic learning activities and during a laboratory spatial working memory task (n = 51 final sample).

We found that alpha power during the activities did not correlate with event-related alpha power modulations (ERMs) during stimulus processing in the laboratory task, a common indicator of visual attention. By contrast, multiple control analyses showed the expected relationships among baseline activity, task-related modulation and behavioral performance. These results suggest that alpha ERMs during task processing reflect different neural processes of visual attention than alpha power during learning activities – potentially reflecting additional contributions from arousal, motivation or motor activity in naturalistic environments.

These findings suggest that laboratory-derived neural metrics of visual attention may not directly generalize to naturalistic learning, highlighting the need to consider multiple contributing processes when translating laboratory-derived neural metrics to naturalistic learning. This work was supported by the National Institutes of Health (R21MH119448). We thank the participants and their caregivers for their involvement.

We acknowledge Remi Torres for designing the learning activities; Amna Ali, Haifa Al-Bassam, Kyle Espiritu, and Minji Kim for data collection at UCLA; Alexandra Baez, Lilly Bay, Alexa Gromada, Jordan Meyer, Sydney Saviano, and Meixin Yu for data collection and behavioral video coding at UVA; Anjali Amazigo for data collection at UVA; Chaerin Noh for EEG preprocessing support at UCLA; and Jalal Sadek for CBCL data entry and behavioral coding, and Simran Tawari and Emily Zhu for behavioral coding at UCLA. Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USA Fang Yu Chang, Maya Khalil, Andrea Dillon, Sandra K. Loo & Agatha Lenartowicz School of Education and Information Studies, University of California, Los Angeles, USA Department of Psychology, University of Central Florida, Orlando, USA Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA Andrea Dillon, Sandra K.

Loo & Agatha Lenartowicz Correspondence to Fang Yu Chang or Agatha Lenartowicz. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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