New ERP Decoding Paper: Reactivation of Previous Experiences in a Working Memory Task
/Bae, G.-Y., & Luck, S. J. (in press). Reactivation of Previous Experiences in a Working Memory Task. Psychological Science. https://doi.org/10.1177/0956797619830398
Gi-Yeul Bae and I have previously shown that the ERP scalp distribution can be used to decode which of 16 orientations is currently being stored in visual working memory (VWM). In this new paper, we reanalyze those data and show that we can also decode the orientation of the stimulus from the previous trial. It’s amazing that this much information is present in the pattern of voltage on the surface of the scalp!
Here’s the scientific background: There are many ways in which previously presented information can automatically impact our current cognitive processing and behavior (e.g., semantic priming, perceptual priming, negative priming, proactive interference). An example of this that has received considerable attention recently is the serial dependence effect in visual perception (see, e.g., Fischer & Whitney, 2014). When observers perform a perceptual task on a series of trials, the reported target value on one trial is biased by the target value from the preceding trial.
We also find this trial-to-trial dependency in visual working memory experiments: The reported orientation on one trial is biased away from the stimulus orientation on the previous trial. On each trial (see figure below), subjects see an oriented teardrop and, after a brief delay, report the remembered orientation by adjusting a new teardrop to match the original teardrop’s orientation. Each trial is independent, and yet the reported orientation on one trial (indicated by the blue circle in the figure) is biased away from the orientation on the previous trial (indicated by the red circle in the figure; note that the circles were not actually colored in the actual experiment).
These effects imply that a memory is stored of the previous-trial target, and this memory impacts the processing of the target on the current trial. But what is the nature of this memory?
We considered three possibilities: 1) An active representation from the previous trial is still present on the current trial; 2) The representation from the previous trial is stored in some kind of “activity-silent” synaptic form that influences the flow of information on the current trial; and 3) An activity-silent representation of the previous trial is reactivated when the current trial begins. We found evidence in favor of this third possibility by decoding the previous-trial orientation from the current-trial scalp ERP. That is, we used the ERP scalp distribution at each time point on the current trial to “predict” the orientation on the previous trial.
This previous-trial decoding is shown for two separate experiments in the figure below. Time zero represents the onset of the sample stimulus on the current trial. In both experiments, we could decode the orientation from the previous trial in the period following the onset of the current-trial sample stimulus (gray regions are statistically significant after controlling for multiple comparisons; chance = 1/16).
These results indicate that a representation of the previous-trial orientation was activated (and therefore decodable) by the onset of the current-trial stimulus. We can’t prove that this reactivation was actually responsible for the behavioral priming effect, but this at least establishes the plausibility of reactivation as a mechanism of priming (as hypothesized many years ago by Gordon Logan).
This study also demonstrates the power of applying decoding methods to ERP data. These methods allow us to track the information that is currently being represented by the brain, and they have amazing sensitivity to quite subtle effects. Frankly, I was quite surprised when Gi-Yeul first showed me that he could decode the orientation of the previous-trial target. And I wouldn’t have believed it if he hadn’t shown that he replicated the result in an independent set of data.
Gi-Yeul has made the data and code available at https://osf.io/dbgh6/. Please take his code and apply it to your own data!