Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation

Abstract

We tested whether changes in prestimulus neural activity predict behavioral performance (decision time and errors) during a prolonged visual task. The task was to classify ambiguous stimuli—Necker cubes; manipulating the degree of ambiguity from low ambiguity (LA) to high ambiguity (HA) changed the task difficulty. First, we assumed that the observer’s state changes over time, which leads to a change in the prestimulus brain activity. Second, we supposed that the prestimulus state produces a different effect on behavioral performance depending on the task demands. Monitoring behavioral responses, we revealed that the observer’s decision time decreased for both LA and HA stimuli during the task performance. The number of perceptual errors lowered for HA, but not for LA stimuli. EEG analysis revealed an increase in the prestimulus 9–11 Hz EEG power with task time. Finally, we found associations between the behavioral and neural estimates. The prestimulus EEG power negatively correlated with the decision time for LA stimuli and the erroneous responses rate for HA stimuli. The obtained results confirm that monitoring prestimulus EEG power enables predicting perceptual performance on the behavioral level. The observed different time-on-task effects on the LA and HA stimuli processing may shed light on the features of ambiguous perception.

Computational Analysis of Speed Accuracy Tradeoff

“…However, the effect might depend on task difficulty: longer decision time for an easy task and lower rate of erroneous responses in a difficult task (Kuc et al, 2021). Experiments on monkeys performing somatosensory discrimination tasks with a sequential presentation of stimuli indicate that the decrease of alpha power (in premotor and motor regions) correlates with better discrimination performance (Haegens et al, 2011).…”

Computational analysis of speed-accuracy tradeoff

“…Evidence for the relationship between alpha power and decision time comes from the experiment of Paluch, Jurewicz, and Wróbel 42 , who analyzed responses among groups of participants responding fast or slowly to a decision task and showed that fast responders had a lower alpha power during the anticipation period. Another experiment 49 showed that the effect of a large alpha power before stimulus onset might depend on task difficulty: longer decision times for an easy task and lower rates of erroneous responses for a difficult task. Experiments on monkeys performing somatosensory discrimination tasks with a sequential presentation of stimuli indicated that a decrease in alpha power (in premotor and motor regions) correlated with better discrimination performance 50 .…”

Explainable Machine Learning Methods for Classification of Brain States during Visual Perception

“…To obtain the data for each of the above frequency range, we reassigned the EEG signals (1) to the total average, subtracted the mean, and filtered with a fourth-order Butterworth ( f L , f H )-Hz bandpass filter, where f L and f H are the boundaries of the frequency domain of interest [58]. For example, for the α-range f L = 8 Hz and f H = 12 Hz, and we obtained the alpha-band signal x I,α n .…”