Fig 1. Temporal dynamics object recognition under various levels of occlusion.

(a) Multivariate pattern classification of MEG data. We extracted MEG signals from -200 ms to 700 ms relative to the stimulus onset. At each time point (ms resolution), we computed average pairwise classification accuracy between all exemplars. (b) Time courses of pairwise decoding accuracies for the three different occlusion levels (without backward masking) averaged across 15 subjects. Thicker lines indicate a decoding accuracy significantly above chance (right-sided signrank test, FDR corrected across time, p < 0.05), showing that MEG signals can discriminate between object exemplars. Shaded error bars represent standard error of the mean (SEM). The two vertical shaded areas show the time from onset to peak, for 60% occluded and 0% occluded objects, which are largely non-overlapping. The onset latency is 79±3 ms (mean ± SD) in the no-occlusion condition; and 123±15 ms in the 60% occlusion; the difference between onset latencies is significant (p<10⁻⁴, two-sided signrank test). Arrows above the curves indicate peak latencies. The peak latencies are 139±1ms and 199±3ms for the 0% occluded and partially occluded (60%) objects respectively. The difference between the peak latencies is also statistically significant (p < 10⁻⁴). Images shown to participants are available from here: https://github.com/krajaei/Megocclusion/blob/master/Sample_occlusion_dataset.png.