Time-resolved decoding of uncertainty about stimulus features from human EEG

Abstract

Internal and external noise produce uncertainty in the neural representations of sensory stimuli. Uncertainty about basic stimulus features can be decoded from responses in sensory brain regions by estimating full probability distributions over stimulus features, rather than point estimates. Such decoded uncertainty correlates with subjective uncertainty reports, providing insight into the neural basis of metacognitive judgments. However, in humans, probabilistic decoding has only been applied to functional magnetic resonance imaging (fMRI) data, which has low temporal resolution and thus can give only limited insight into the dynamics of uncertainty in the brain. Here, we assessed whether probabilistic decoding of uncertainty about stimulus features could be extended to electroencephalography (EEG) data, which has higher temporal resolution but lower spatial resolution and different noise properties compared to fMRI. Participants performed a spatial location estimation task and provided subjective uncertainty reports. We found that time-resolved probabilistic decoding in EEG was feasible, as decoders produced accurate predictions of stimulus location following stimulus onset, and decoding error correlated trial-by-trial with decoded uncertainty. The choice of noise covariance structure critically impacted these metrics. Further, decoded uncertainty was a more reliable trial-by-trial indicator of stimulus information than decoding error, illustrating the advantages of probabilistic decoding over standard decoding approaches. However, uncertainty decoded from EEG had no significant trial-by-trial correlation with subjective uncertainty at any time point. Based on these results, uncertainty decoded from EEG provides a time-resolved estimate of stimulus information available from the brain signal on a single trial but may not relate to metacognitive reports.