Article Figures & Data
Figures
- Figure 1.
Task schematics for example set size 3 and set size 2 trials in the whole-field change detection task used in both experiments. In Experiment 1 (a), subjects were cued at the beginning of each block to either remember the color or orientation of the memory items while ignoring the gray distractor items. In Experiment 2 (b), subjects were cued at the beginning of each block to either remember the color or the motion coherence of the memory items while ignoring the gray distractor items. During change trials, the attended feature changes 50% of the time. The Experiment 1 example depicts a no-change trials, while the Experiment 2 trials depicts a change trial (change in motion coherence).
- Figure 2.
Change detection accuracy for each set size in both experiments. Black dots indicate individual data, white dots indicate means, and shaded regions indicate the density of the data.
- Figure 3.
Event-related potentials (ERPs) for each condition in both experiments at parietal/occipital, central, and frontal electrodes. The gray region indicates stimulus presentation.
- Figure 4.
Decodability of attended feature in (a) Experiment 1 and (b) Experiment 2. Top row, The blue line represents decodability for which feature the subject is attending to (and subsequently remembering) in a given trial. The gray line indicates empirical chance hyperplane contrast. The blue squares represent times when the hyperplane contrast is significantly greater than empirical chance (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure). The gray vertical rectangle indicates the time period during which the memory array was displayed. The shuffle condition reveals empirical chance accuracy, obtained by training the model on nonpermuted data then testing on data with permuted trial labels. Middle row, Same as top row, but the model is trained and tested on eye tracking data. Bottom row, The correlation between the EEG-trained model and the eye-trained model of hyperplane contrasts across permutations, transformed to Fisher's Z. The blue squares represent times when the correlation is significantly above 0 (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure).
- Figure 5.
Decodability of motion coherence, within attend-motion blocks. The left column presents results from contrasting pairs of conditions that differ only in the coherence level of distractors; the right column presents results from contrasting the target coherence level within set size 1 trials. Top row, Colored lines represent decodability of coherence level via EEG. The gray lines indicate empirical chance hyperplane contrast. The colored squares represent times when decodability is significantly above empirical chance (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure). The gray vertical rectangle indicates the time period during which the memory array was displayed. Middle row, Same as top row, but the model is trained and tested on eye tracking data. Bottom row, The correlation between the EEG-trained model and the eye-trained model of hyperplane contrasts across permutations, transformed to Fisher's Z.
- Figure 6.
Decodability of WM load in Experiment 1, within and across attended features. The left column presents results from training on color data; right column presents results from training on orientation data. Top row, Colored lines represent decodability of WM load. The gray lines indicate empirical chance hyperplane contrast. The colored squares represent times when decodability is significantly above empirical chance (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure). The gray vertical rectangle indicates the time period during which the memory array was displayed. Middle row, Same as top row, but the model is trained and tested on eye tracking data. Bottom row, The correlation between the EEG-trained model and the eye-trained model of hyperplane contrasts across permutations, transformed to Fisher's Z. The colored squares represent times when the correlation is significantly above 0 (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure).
- Figure 7.
Decodability of WM load in Experiment 2, within and across attended features. The left column presents results from training on color data; right column presents results from training on orientation data. Top row, Colored lines represent decodability of WM load. The gray lines indicate empirical chance hyperplane contrast. The colored squares represent times when decodability is significantly above empirical chance (corrected p < 0.05, FDR = 0.05 with Benjamini–Hochberg procedure). Black squares indicate time periods when the within-feature decodability was greater than across-feature decodability. The gray vertical rectangle indicates the time period during which the memory array was displayed. Middle row, Same as top row, but the model is trained and tested on eye tracking data. Bottom row, The correlation between the EEG-trained model and the eye-trained model of hyperplane contrasts across permutations, transformed to Fisher's Z.
- Figure 8.
Comparison of EEG-based and Eye-based decodability across individuals. Dots reflect the average across-feature decodability during the last 500 ms for a given individual. Purple dots reflect participants in Experiment 1; green reflect Experiment 2.
- Figure 9.
RSA results. Top, The Empirical RDM, averaged across the delay period and across subjects, along with representations of the eight regressors. All RDMs are organized by (1) attended feature (color, motion), (2) set size (1, 2), and number of coherent targets (0,1 for set size 1; 0, 1, 2 for set size 2). The content-independent load, attended feature, and feature-specific load RDMs are all based on theoretical predictions, while the coherence-level RDM is based on a hyperplane analysis (see Materials and Methods), the accuracy RDM is based on the condition-level behavioral performance, and the pupil and accuracy*pupil RDMs are based on the average normalized pupil sizes across conditions. Note that the bottom four RDMs visualized here reflect the average of all subjects, averaged across the delay period, but the coherence, pupil, and accuracy*pupil RDMs were computed separately in each time window, and the coherence RDM for each subject exclude their own data as it was also based on EEG data. Bottom, The semipartial correlations between the theoretical factors and the empirical RDMs estimated at each time window, averaged across subjects. Colored lines reflect the semipartial correlations of a given theoretical factor. The colored squares, and thicker segments of the corresponding line, indicate that the correlation for that factor is significantly greater than 0 (one-tailed Wilcoxon sign-rank test) after FDR correction via the Benjamini–Hochberg procedure.
- Figure 10.
Spatial decodability across attended features, set sizes. Time courses reflect the average decodability (cross-validated d’; see Materials and Methods) between pairs of locations. Darker traces reflect decoding of set size 1 target locations, and lighter traces reflect decoding of the placeholder location at higher set sizes. Corresponding colored squares reflect above-change decodability, after FDR correction via the Benjamini–Hochberg procedure. Additional colored squares reflect the significant of ANOVA results, also after correction. The blue squares reflect a main effect of attended feature. Red squares reflect of main effect of set size. The black squares would reflect an interaction between attended feature and set size; this interaction was never significant.
