In a groundbreaking advancement at the intersection of cognitive neuroscience and visual perception, researchers have unlocked the neural code underlying dynamic shape perception during complex visual tasks. The study, led by Merkel, Merkel, Hopf, and colleagues and published in Communications Psychology (2026), probes the enigmatic world of illusory contours—perceived shapes without clear physical boundaries—and reveals how our brain deciphers their shape transitions in real-time during multiple-object tracking (MOT). This revelation, accomplished through the analysis of ongoing electroencephalography (EEG) signals, paves the way for new understandings of perceptual organization, attention, and neural processing in cluttered environments.
Illusory contours have long fascinated vision scientists because they challenge the brain’s capacity to infer edges and shapes where none objectively exist. These phantom lines are revealed in classic visual illusions such as the Kanizsa triangle, where the mind assembles fragmented cues into a coherent figure. However, previous investigations predominantly examined static illusions, limiting insight into how the brain copes with dynamically changing or morphing illusory forms amidst competing stimuli. Merkel and colleagues pushed these boundaries by exploring shape transitions—gradual morphing between different illusory contours—while participants engaged in the rigorous attentional task of multiple-object tracking.
Multiple-object tracking is a demanding cognitive endeavor requiring the concurrent surveillance of several moving targets amongst distractors in a busy visual scene. This paradigm probes selective attention and working memory, encapsulating real-world perceptual challenges like monitoring vehicles in traffic or players on a sports field. Including morphing illusory contours within MOT contexts compounded task complexity, challenging the brain to decode continuously evolving shape info while filtering out irrelevant motion and form cues. The researchers hypothesized that distinct neural signatures corresponding to the moment-to-moment transitions of these illusory shapes would be extractable from EEG recordings, shedding light on how dynamic visual forms are represented at the neurophysiological level.
To pursue this, participants observed displays containing multiple moving objects, some of which generated illusory contours that subtly morphed through space and time. EEG was recorded non-invasively, capturing the brain’s electric activity from dozens of scalp electrodes with millisecond temporal resolution. The team employed sophisticated decoding and machine learning algorithms, training classifiers to differentiate EEG patterns corresponding to different shapes and morphing stages of the illusory contours. Remarkably, the classifiers could reliably reconstruct which shape transition the participant’s brain was processing, despite the complex and noisy environment imposed by multiple-object tracking.
These findings resonate deeply within vision science, providing compelling evidence that the human brain represents the shape of illusory contours dynamically and with high fidelity even under conditions of divided attention and motion-rich scenes. The ability to decode ongoing, time-resolved shape transitions from scalp-recorded EEG defies prior assumptions that such abstract perceptual constructs might be too subtle or transient to detect non-invasively. Instead, the data suggest that early and mid-level visual cortical areas continuously track and update the perceptual organization of forms in a manner accessible to scalp EEG.
Importantly, this work sheds light on how attentional mechanisms interface with perceptual grouping processes. Multiple-object tracking imposes a heavy attentional load, yet the brain still preserves rich neural representations of dynamically changing illusory contours. Such a robust capacity likely facilitates real-world object recognition and scene parsing, where objects often appear partially occluded, and boundaries may be ambiguous or absent. This neural resilience underscores the brain’s remarkable efficiency in filtering, integrating, and encoding relevant visual information amidst clutter and distraction.
The methodological innovations employed in this study also merit emphasis. Encoding continuous shape transitions in illusory contours within an MOT framework, combined with advanced EEG decoding techniques, represents a significant technical leap in experimental design and neural signal analysis. The approach relies heavily on time-resolved multivariate pattern analysis (MVPA), aligning EEG signals with perceptual state changes on the scale of tens of milliseconds. This temporal precision enables researchers to parse the cortical dynamics supporting perceptual formation and shape morphing as they unfold in real time.
From a theoretical perspective, these findings invigorate models of visual perception that emphasize predictive coding and hierarchical inference. The brain actively generates hypotheses about shape and form, updating its internal models as sensory input fluctuates over time. Dynamic illusory contours challenge such models because the sensory evidence is incomplete, demanding integration of contextual cues and prior expectations to maintain a stable percept. Merkel et al.’s demonstration that such internal representations can be tracked non-invasively offers exciting avenues to probe these computational processes in health and disease.
This work also opens intriguing translational possibilities. Understanding how the brain encodes complex, evolving visual forms may inform assistive technologies for patients with visual or attentional deficits, enabling brain-computer interfaces to decode and reconstruct perceptual content. It may also inspire novel machine vision systems mimicking human perceptual flexibility, particularly in recognizing occluded or morphing objects under variable conditions. By bridging neuroscience, psychology, and computer science, this research contributes to the development of intelligent systems grounded in the neural principles of visual cognition.
Critically, the study bridges a longstanding gap between low-level visual signal processing and complex perceptual experience. It highlights how subtle cues—often thought too ethereal for direct measurement—penetrate the neural signal landscape during active perception. Future research building on these insights may elucidate how other elusive perceptual phenomena, including subjective contours, illusory surfaces, and figure-ground segregation, manifest dynamically within brain activity. Ultimately, this work enhances our comprehension of how the brain negotiates the ambiguous and often incomplete visual environment.
Looking forward, the authors suggest multiple potential directions to expand their findings. Complementary neuroimaging approaches such as magnetoencephalography (MEG) or intracranial recordings could enrich spatial localization of the neural sources generating shape transition signals. Combining EEG decoding with behavior linked directly to perceptual reports would clarify how neural representations correlate with subjective experience. Additionally, exploring clinical populations with perceptual or attentional impairments could reveal how these mechanisms deteriorate or compensate, informing rehabilitation strategies.
The implications stretch beyond pure vision science. Dynamic, morphing illusory contours serve as a model system for studying how the brain integrates temporally evolving patterns, relevant for auditory perception, language processing, and multisensory integration. The capacity to decode continuous changes in perceptual content may eventually apply across sensory modalities, offering a universal framework to investigate brain dynamics underlying real-time cognition and consciousness.
In sum, this landmark study by Merkel, Merkel, Hopf et al. elucidates a previously inaccessible dimension of human visual perception. It demonstrates, for the first time, the feasibility of decoding continuous shape transformations of morphing illusory contours from ongoing EEG signals during the demanding task of multiple-object tracking. This dual achievement—both conceptual and technical—marks a transformative step toward unraveling the neural code of dynamic and complex perceptual phenomena.
The study exemplifies the power of combining advanced experimental paradigms with computational decoding methods to reveal hidden layers of neural processing. It challenges us to rethink traditional boundaries between perception and attention, between stable form and fleeting shape, and between the objective and the illusory. As research continues to delve into these mysteries, the brain’s remarkable adaptability and creativity in constructing our visual reality become ever clearer.
With these insights, the future of perceptual neuroscience looks bright and boundless. This work not only advances fundamental science but also inspires new applications that harness the brain’s dynamic perceptual machinery. Through continuous exploration and innovation, researchers edge closer to decoding the ultimate enigma: how the brain creates and continuously revises the images that define our conscious visual world.
Subject of Research: Neural decoding of dynamic shape transitions in morphing illusory contours during multiple-object tracking using EEG.
Article Title: Shape-transitions of a morphing illusory contour can be decoded during multiple-object tracking from the ongoing EEG.
Article References:
Merkel, C., Merkel, M., Hopf, JM. et al. Shape-transitions of a morphing illusory contour can be decoded during multiple-object tracking from the ongoing EEG. Commun Psychol (2026). https://doi.org/10.1038/s44271-026-00427-6
Image Credits: AI Generated
