The research group led by Song Yan published a paper in PLoS Biology revealing the neural mechanisms underlying the suppression of visual-spatial interference.
Release time:
2025-10-20
When we come to a fork in the road on our way home and see a sign that reads, “Beware of the polar bear on the left,” our attempt to suppress the distracting information actually makes it easier for us to notice it—this is the classic “polar bear effect” in attentional processing (Figure 1). How does our brain handle the spatial “polar bear effect” to help us better achieve spatial goal-directed search? On March 8, 2023, the research group led by Professor Yan Song from the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University published online in the journal PLOS Biology a paper titled “Suppression of distracting inputs by visual-spatial cues is driven by anticipatory alpha activity.” In this study, using three experiments, the researchers collected EEG and behavioral data from 110 healthy adults performing visual search tasks under conditions of distractor prompts, thereby revealing the neural mechanisms underlying visual-spatial interference.
When we come to a fork in the road on our way home and see a sign that reads, “Beware of the polar bear on the left,” our attempt to suppress this distracting information actually makes it easier for us to notice it—this is the classic “polar bear effect” in attentional psychology (Figure 1). How does our brain process the spatial “polar bear effect” to help us better achieve spatial goal-directed search? On March 8, 2023, the research group led by Professor Yan Song from the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University published online in the journal PLOS Biology a paper titled “Suppression of distracting inputs by visual-spatial cues is driven by anticipatory alpha activity.” In this study, using three experiments, the researchers collected EEG and behavioral data from 110 healthy adults performing visual search tasks under conditions of distractor cues, thereby revealing the neural mechanisms underlying the brain’s active suppression of visual-spatial distractions.
Figure 1. The White Bear Effect of Attention and Alpha-Energy Modulation
Everyday life requires us to selectively attend to information at different locations within a rich visual space. Since the brain’s attentional resources are limited, when spatial cues indicate the presence of distractors, we often employ an “active inhibition” mechanism to preemptively suppress these distractors in the visual space, thereby better enabling us to select a task-relevant target from among the surrounding distractions.
In Experiment 1, the researchers presented participants with either valid or invalid spatial-location cues prior to a visual search task. The results showed that valid interference cues influenced the subsequent distribution of spatial-gradient effects on various behavioral measures (Figure 2): Cues indicating the locations of distractors farther from the target enhanced participants’ behavioral responses, whereas cues indicating distractor locations closer to the target actually impaired participants’ behavioral responses. These findings demonstrate that active inhibition of interference can significantly modulate the spatial proximity effect in behavior, thereby introducing a new behavioral metric for the study of spatial interference inhibition.
Figure 2. The spatial information of the distractor cues influenced the distribution of interference gradients across different behavioral measures in subsequent participants.
Meanwhile, using a spatial coding model, the researchers reconstructed the activation representations (channel-tuning functions [CTFs]) for specific stimuli based on the scalp distribution of alpha-band power (8–12 Hz). They found that these CTFs could dynamically represent interference suppression during the anticipation phase (Figure 2). Moreover, approximately 1,200 ms after the onset of the cue, a clear interference-cue effect was observed in the slope of the CTFs: specifically, under conditions where the interference cue was effective, the slope of the CTFs became more negative. As shown in Figure 2D, the response of contralateral channels—those located on the side opposite to the cued interference location—increased relatively, whereas the response of ipsilateral channels—those on the same side as the cued interference location—decreased relatively. The researchers further discovered that the changes in CTFs between effective and ineffective interference-cue conditions showed a significant negative correlation with corresponding behavioral changes. This suggests that the spatial gradient distribution of alpha-power induced by the anticipated interference can account for the subsequent spatial proximity effect observed in behavior.
Figure 3. Spatial information about the distractor prompts changes in the expected modulation level of the alpha frequency band and influences subsequent behavioral performance.
The experimental results also revealed that effective distractor cues elicit a late negative alpha lateralization modulation (alpha modulation index [alpha MI]) during the expectation phase, whereas ineffective cues do not. In the subsequent visual search phase, effective distractor cues fail to elicit the ERP component—PD—that typically reflects interference suppression, while ineffective cues do elicit this component. These findings suggest that effective distractor cues reduce the attentional capture triggered by the appearance of distractors (Figure 4).
Figure 4. Spatial information about the distractor impairs alpha-band lateralization during the anticipatory phase and subsequently modulates ERP components associated with interference suppression.
To further validate the above findings and rule out potential confounding factors, as shown in Figure 5A, in Experiment 2, the researchers manipulated the spatial probability of the distractor cues. The results revealed that the spatial validity of the distractor cues also influenced behavioral performance, alpha-band lateralization, and the ERP component PD. Moreover, when the spatial information provided by the distractor cues was highly valid, a significant correlation was observed both between inter-subject and intra-subject levels between the expected alpha-band lateralization and the subsequent PD amplitude; this correlation was absent, however, when the spatial validity of the distractor cues was low. In Experiment 3, the researchers further modified the distractor cues from fan-shaped to arrow-shaped indications and once again replicated the finding that the spatial information provided by the distractor cues could predict subsequent changes in the level of distractor inhibition via the expected alpha-band lateralization.
Figure 5. The effectiveness of spatial information provided by distractors influences behavioral performance, as reflected in the lateralization of the alpha band during the anticipation phase and subsequent ERP components associated with interference suppression.
In the 1920s, Hans Berger first discovered oscillatory activity in the alpha band. In 1996, Pfurtscheller interpreted alpha power as reflecting “cortical idling.” In 2010, Jensen proposed that the suppression hypothesis of alpha activity could be understood in terms of gating. To date, the relevant paper, “Shaping functional architecture by oscillatory alpha activity: gating by inhibition,” has been widely accepted and cited 2,245 times. However, the absence of alpha activity associated with distractors has cast doubt on this theory. Some studies have suggested that the evidence for alpha energy serving as an inhibitory gate is limited. This issue has remained like a “dark cloud” hanging over the development of the inhibition-gating theory (as shown in Figure 6). Our research sheds light on the underlying neural mechanisms of active inhibition, demonstrating that alpha-band energy during the preparatory phase plays a crucial role in reducing interference and exhibits a close relationship with ERP components elicited by distractors. These findings provide strong support for the theory that alpha activity functions as a gate for active inhibition.
Figure 6. The “Gate Control” Theory and This Study
This research was supported by projects including the National Natural Science Foundation of China (32271094, 31871099, 62201064, 32200870) and the Science and Technology Innovation 2030 Initiative (2021ZD0204300, 2022ZD0211300). Zhao Chengguang, a postdoctoral fellow, and Kong Yuanjun, a doctoral student, at Beijing Normal University are the first authors of the paper; the corresponding author is Professor Song Yan from BNU. Dr. Li Dongwei, a doctoral student at BNU, and graduate student Kong Lujiao also contributed to this work. Professor Li Xiaoli and Associate Researcher Huang Jing, both from BNU, as well as Professor Ole Jensen from the University of Birmingham, also made significant contributions to this study.
Paper link:
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002014
Relevant research findings:
1. Zhao, C., Li, D., Guo, J., Li, B., Kong, Y., Hu, Y., Du, B., Ding, Y., Li, X., Liu, H., & Song, Y. (2022). The neurovascular couplings between electrophysiological and hemodynamic activities in anticipatory selective attention. Cerebral Cortex, bhab525.
2. Zhao, C., Guo, J., Li, D., Tao, Y., Ding, Y., Liu, H., & Song, Y. (2019). Anticipatory alpha oscillation predicts attentional selection and hemodynamic response. Human Brain Mapping, 40, 3606–3619.
3. Huang, J., Wang, F., Ding, Y., Niu, H., Tian, F., Liu, H., & Song, Y. (2015). Predicting the N2pc from anticipatory HbO activity during sustained visuospatial attention: A concurrent fNIRS-ERP study. NeuroImage, 113, 225–234.
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