1 research outputs found
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts
There exist very few ways to isolate cognitive processes, historically
defined via highly controlled laboratory studies, in more ecologically valid
contexts. Specifically, it remains unclear as to what extent patterns of neural
activity observed under such constraints actually manifest outside the
laboratory in a manner that can be used to make an accurate inference about the
latent state, associated cognitive process, or proximal behavior of the
individual. Improving our understanding of when and how specific patterns of
neural activity manifest in ecologically valid scenarios would provide
validation for laboratory-based approaches that study similar neural phenomena
in isolation and meaningful insight into the latent states that occur during
complex tasks. We argue that domain generalization methods from the
brain-computer interface community have the potential to address this
challenge. We previously used such an approach to decode phasic neural
responses associated with visual target discrimination. Here, we extend that
work to more tonic phenomena such as internal latent states. We use data from
two highly controlled laboratory paradigms to train two separate
domain-generalized models. We apply the trained models to an ecologically valid
paradigm in which participants performed multiple, concurrent driving-related
tasks. Using the pretrained models, we derive estimates of the underlying
latent state and associated patterns of neural activity. Importantly, as the
patterns of neural activity change along the axis defined by the original
training data, we find changes in behavior and task performance consistent with
the observations from the original, laboratory paradigms. We argue that these
results lend ecological validity to those experimental designs and provide a
methodology for understanding the relationship between observed neural activity
and behavior during complex tasks