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There exist very few ways to isolate cognitive processes, historicallydefined via highly controlled laboratory studies, in more ecologically validcontexts. Specifically, it remains unclear as to what extent patterns of neuralactivity observed under such constraints actually manifest outside thelaboratory in a manner that can be used to make an accurate inference about thelatent state, associated cognitive process, or proximal behavior of theindividual. Improving our understanding of when and how specific patterns ofneural activity manifest in ecologically valid scenarios would providevalidation for laboratory-based approaches that study similar neural phenomenain isolation and meaningful insight into the latent states that occur duringcomplex tasks. We argue that domain generalization methods from thebrain-computer interface community have the potential to address thischallenge. We previously used such an approach to decode phasic neuralresponses associated with visual target discrimination. Here, we extend thatwork to more tonic phenomena such as internal latent states. We use data fromtwo highly controlled laboratory paradigms to train two separatedomain-generalized models. We apply the trained models to an ecologically validparadigm in which participants performed multiple, concurrent driving-relatedtasks. Using the pretrained models, we derive estimates of the underlyinglatent state and associated patterns of neural activity. Importantly, as thepatterns of neural activity change along the axis defined by the originaltraining data, we find changes in behavior and task performance consistent withthe observations from the original, laboratory paradigms. We argue that theseresults lend ecological validity to those experimental designs and provide amethodology for understanding the relationship between observed neural activityand behavior during complex tasks.

Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts