High-level cognition is supported by information-rich but compressible brain activity patterns

Abstract

We applied dimensionality reduction algorithms and pattern classifiers to functional neuroimaging data collected as participants listened to a story, temporally scrambled versions of the story, or underwent a resting state scanning session. These experimental conditions were intended to require different depths of processing and inspire different levels of cognitive engagement. We considered two primary aspects of the data. First, we treated the maximum achievable decoding accuracy across participants as an indicator of the "informativeness" of the recorded patterns. Second, we treated the number of features (components) required to achieve a threshold decoding accuracy as a proxy for the "compressibility" of the neural patterns (where fewer components indicate greater compression). Overall, we found that the peak decoding accuracy (achievable without restricting the numbers of features) was highest in the intact (unscrambled) story listening condition. However, the number of features required to achieve comparable classification accuracy was also lowest in the intact story listening condition. Taken together, our work suggests that our brain networks flexibly reconfigure according to ongoing task demands, and that the activity patterns associated with higher-order cognition and high engagement are both more informative and more compressible than the activity patterns associated with lower-order tasks and lower levels of engagement.

Preserved spontaneous mentalizing amid reduced intersubject variability in autism during a movie narrative

“…However, identifying how this narrative processing occurs in unstructured settings is challenging, as it likely unfolds differently over time among individuals exposed to the same stimuli. We reasoned that it could manifest as intersubject variability in responses to movie stimuli, especially among viewers inclined towards exploratory analysis (Chang et al, 2021; Finn et al, 2018; Owen & Manning, 2023; Zhang et al, 2022). We investigated this possibility using intersubject correlation analysis (Hasson et al, 2004), applying it to two complementary methods for assessing cognitive processing; brain imaging and pupil size measurements (Beatty, 1982).…”