The time course of encoding and maintenance of task-relevant versus irrelevant object features in working memory

Abstract

Access to WM can be restricted on the basis of goal-relevant properties such as spatial location. However, the extent of voluntary control over which features of an attended multi-feature object are encoded and maintained in WM is debated. Some evidence suggests that attending to an object leads to obligatory storage of all of its features, whereas other evidence suggests that access to WM can be restricted to only goal-relevant features. Another possibility is that all features are initially encoded, but irrelevant features are removed from WM over time. To address these various possibilities, we used pattern classification of EEG signals to track the temporal evolution of representations reflecting the encoding and storage of task-relevant and irrelevant features in WM. In different blocks, participants remembered the orientation, color or both orientation and color of a colored, oriented grating. The color and orientation of the grating was randomly drawn from two distinct feature bins on each trial. To examine trial-specific activity reflecting storage of the object's features, a support vector machine (SVM) classifier was trained to classify what bin the stimulus features came from. Importantly, for orientation, the classifier produced reliably above-chance classification across the delay when orientation was task-relevant but not when it was task-irrelevant. Interestingly, orientation could be accurately classified on trials for which both orientation and color were remembered. Moreover, a separate measure corresponding to the probability of a feature belonging to the correct bin was significantly higher when orientation was task-relevant compared to task-irrelevant during encoding. Above-chance classification for color was only present during the initial 500 ms across all conditions. Our results suggest that although information about all of an object's features is present in the initial stimulus-evoked neural response, information about the task-irrelevant features is attenuated during stimulus encoding and is largely absent throughout the delay.

The Effect of Task on Object Processing revealed by EEG decoding

“…In the current study, however, artificial stimuli were employed, allowing participants to more easily focus on only one dimension in each task as instructed. Bocincova & Johnson (2019) found that classifier performance in the decoding of a part's orientation was higher when it was task-relevant than irrelevant during 100-600ms, whereas we only found this effect of task relevance in 250-340ms. Therefore, task apparently had an earlier influence on the formation of representations in their study.…”

“…The resulting weakening of task manipulation might have contributed to the mixed findings in the literature. Consistently, the two studies using artificial stimuli (Jackson et al, 2017;Bocincova & Johnson, 2019) did show some support for the task-dependent object representations.…”

“…Together this may have led to a relatively earlier task effect manifested in terms of the amount of information carried in the object representations instead of the actual pattern of activations in our study than in Hebart et al (2018). Bocincova & Johnson (2019) found that classifier performance in the decoding of a part's orientation was higher when it was task-relevant than irrelevant during the 100-600ms time window. Therefore, task apparently had an earlier influence on the formation of representations in their study.…”

“…Using magnetoencephalography (MEG), Hebart et al (2018) observed a task effect on object decoding performance only after 542ms, suggesting that task affects object representation only at a late time. However, using EEG and applying time-frequency analyses, decoding performance was higher in a task-relevant than a task-irrelevant condition during a much earlier time frame (100-600ms after stimulus onset), but it was the case only for one of the two tasks included in that study (Bocincova & Johnson, 2019).…”

“…In the current study, participants focused on different dimensions in each trial depending on the cue. Another, perhaps important difference is that Bocincova & Johnson (2019) conducted time-frequency analysis on the EEG data and input the power of frequencies to the classifiers. This time-frequency decomposition involved temporal smearing, and thus it is uncertain how much of the effect found starting at 100ms was contributed by activations afterwards within the 100-600ms time window.…”