Investigating spatial predictive context using rapid invisible frequency tagging (RIFT)

This is a MEG & behavioral data set belonging to a that investigated the role of spatial predictive context during visual search, by means of rapid invisible frequency tagging (RIFT for short). The study was approved by the local ethics committee (CMO 2014/288; CMO Arnhem-Nijmegen, the Netherlands). MEG activity was recorded using a 275-channel axial gradiometer CTF MEG system.
Within visual search, contextual cueing demonstrates how implicit knowledge of scenes can improve search performance: repetitions of search scenes speeds up finding a target, even though participants are unaware of these repetitions.
This is commonly interpreted as spatial context in the scenes becoming predictive of the target location, which leads to a more efficient guidance of attention during search. However, what drives this enhanced guidance is unknown. In this project we investigated both the extent of context involved in contextual learning and the neural mechanisms underlying the behavioral improvement.
We did so using Rapid Invisible Frequency Tagging (RIFT). We tagged the target, a near standing distractor and a far standing distractor with unique frequencies (60, 64 and 68 Hz). We found that the improved performance when searching implicitly familiar scenes was accompanied by a stronger neural representation of the target stimulus, at the cost specifically of those distractors directly surrounding the target. Crucially, this biasing of local attentional competition was behaviorally relevant when searching familiar scenes. Taken together, we conclude that implicitly learned spatial predictive context improves how we search our environment by sharpening the attentional field.