Abstract
Exciting work in the working memory literature has demonstrated that hidden, or so-called ‘activity-silent’, memory representations can be inferred through external perturbation. Here we explored whether the same technique can be used to visualise the landscape of spatial priority maps. It is generally assumed that statistical learning, for example about high-probability target locations in space, affects weights within a theoretical spatial priority map. We hypothesised that these maps may be hidden from techniques measuring active neural activity because they are not mediated by active neural firing but rather by changes in synaptic weights leading to biased re-activation potentials, akin to reactivation of latent memory representations following external perturbation. We thus sought to observe whether perturbation of the visual system with visual noise (i.e., presenting a high contrast visual ping) would lead to a visualisation of the learned attentional priority map. We tested this using the additional singleton paradigm, wherein participants were implicitly trained to expect search targets to appear in certain locations in space. This high-probability target location systematically shifted across the display in a blocked design allowing for a multivariate decoding approach. Critically, in the inter-trial period we occasionally presented high-contrast visual ‘pings’ similar to those used to reveal activity-silent working memory contents. Using multivariate pattern analysis on raw and time-frequency filtered EEG data, we show robust anticipatory decoding of the high probability target location before stimulus onsets, but critically only on trials containing a ‘ping’ prior to search display onset. Accompanying analyses of eye-tracking data preclude eye movements as an explanation of our results and indicate that our findings are the reflection of a latent attentional priority map. Our findings thus highlight that dynamic coding offers a plausible mechanical explanation for how statistical learning arises, as well as offering a new, striking method of revealing learned attentional priority.