In this work we explore how neurophysiological correlates related to attention and perception can be used to better understand the image-annotation task. We explore the nature of the highly variable labelling data often seen across annotators. Our results indicate potential issues with regard to ‘how well’ a person manually annotates images and variability across annotators. We propose such issues arise in part as a result of subjectively interpretable instructions that may fail to elicit similar labelling behaviours and decision thresholds across participants. We find instances where an individual’s annotations differ from a group consensus, even though their EEG (Electroencephalography) signals indicate in fact they were likely in consensus with the group. We offer a new perspective on how EEG can be incorporated in an annotation task to reveal information not readily captured using manual annotations alone. As crowd-sourcing resources become more readily available for annotation tasks one can reconsider the quality of such annotations. Furthermore, with the availability of consumer EEG hardware, we speculate that we are approaching a point where it may be feasible to better harness an annotators time and decisions by examining neural responses as part of the process. In this regard, we examine strategies to deal with inter-annotator sources of noise and correlation that can be used to understand the relationship between annotators at a neural level.