Implicitly and explicitly encoded features can guide attention in free viewing

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review

The competitive demand for attention is present in our daily lives, and the identification of neural processes in the EEG signals associated with the demand for specific attention can be useful to the individual’s interactions in virtual environments. Since EEG-based devices can be portable, non-invasive, and present high temporal resolution technology for recording neural signal, the interpretations of virtual systems user’s attention, fatigue and cognitive load based on parameters extracted from the EEG signal are relevant for several purposes, such as games, rehabilitation, and therapies. However, despite the large amount of studies on this subject, different methodological forms are highlighted and suggested in this work, relating virtual environments, demand of attention, workload and fatigue applications. In our summarization, we discuss controversies, current research gaps and future directions together with the background and final sections.

Does feature intertrial priming guide attention? The jury is still out

Our search performance is strongly influenced by our past experience. In the lab, this influence has been demonstrated by investigating a variety of phenomena, including intertrial priming, statistical learning, and reward history, and collectively referred to as selection history. The resulting findings have led researchers to claim that selection history guides attention, thereby challenging the prevailing dichotomy, according to which top-down and bottom-up factors alone determine attentional priority. Here, we re-examine this claim with regard to one selection-history phenomenon, feature intertrial priming (aka priming of pop-out). We evaluate the evidence that specifically pertains to the role of feature intertrial priming in attentional guidance, rather than in later selective processes occurring after the target is found. We distinguish between the main experimental rationales, while considering the extent to which feature intertrial priming, as studied through different protocols, shares characteristics of top-down attention. We show that there is strong evidence that feature intertrial priming guides attention when the experimental protocol departs from the canonical paradigm and encourages observers to maintain the critical feature in visual working memory or to form expectations about the upcoming target. By contrast, the current evidence regarding the standard feature intertrial priming phenomenon is inconclusive. We propose directions for future research and suggest that applying the methodology used here in order to re-evaluate of the role of other selection history phenomena in attentional guidance should clarify the mechanisms underlying the strong impact of past experience on visual search performance.