Article Figures & Data
Figures
- Figure 1.
Paradigm and behavioral data. A, Participants discriminated the direction of visual random-dot motion (800 ms stimulus duration). Across three conditions, the visual motion was accompanied by different auxiliary cues: simultaneous acoustic motion, a visual symbolic cue preceding the visual motion, or a visual symbolic cue following the motion stimulus. Each cue was congruent with the random-dot motion on 66% of trials. B, For each cue type, response accuracy was higher for congruent trials, while reaction times (relative to a response cue) were not affected by congruency (mean and SEM across participants; dots represent single-participant data). C, Psychophysical response templates (weights) were calculated to quantify the influence of the moment-by-moment motion energy on behavior. These templates were significant for most time points (top; thick black line, grand average; thin black line, 5% bootstrap confidence interval across conditions; thick colored lines, condition-wise group means), but were not affected by cue–stimulus congruence (bottom; think lines, condition wise group means; dashed lines, 5% bootstrap confidence intervals).
- Figure 2.
Electrophysiological correlates of cue–stimulus congruency. A, Single-trial LDA was used to determine EEG components sensitive to the direction of random-dot motion, that is, sensitive to the task-relevant visual information. The curve shows the classifier performance as the area under the curve of the receiver operating characteristic (mean and SEM across participants), which was significant (cluster-based permutation test, p < 0.05) ∼300 ms (peak at P2). The topographies show the forward models of the LDA classifiers at three local peaks. B, Effect of cue–stimulus congruency on the single-trial LDA evidence in the component derived at time P2, separately for each cue condition and shown as the group-level t value. Epochs with a congruency effect are indicated (cluster-based permutation test, p < 0.05). C, Congruency effects on LDA evidence at the time points of the respective global peaks (mean and SEM across participants). D, Test for a latency difference of the congruency effect in the acoustic and visual preconditions. The graph shows the group-level bootstrap distribution (2000 samples) of the latency difference between the congruency effects. E, Source localization of the LDA component (at P2) determined as source-level correlation (z-scored) between gridwise activity and the LDA activity (group averaged; shown at MNI coordinate z = −5).
- Figure 3.
Time–frequency analysis of prestimulus influences on behavior. A, For each condition, we modeled a single-trial choice based on the visual stimulus, the cue, their interaction with each other, and the interaction of each with prestimulus power. Graphs display the group-level t values obtained from the respective single-participant regression betas for the interaction of power with visual motion and the cue. Prestimulus activity was analyzed within the LDA component derived at P2 (Fig. 2). The topographies (insets) show the electrode-wise t values for the alpha band cluster in the respective panel obtained from single-electrode regression models. Cluster-averaged regression coefficients (betas) for individual participants are shown on the right. Black outlines indicate significant clusters (cluster-based permutation test, p < 0.05). B, Same as in A, but for a regression model of performance (response accuracy) based on stimulus–cue congruency, prestimulus power, and the interaction of power with congruency.
In this issue
