Time Course of Predictions Adjustment to Visual Perception: Evidence from EEG

Fu Chunye; Li Aixin; Lyu Yong

doi:10.16719/j.cnki.1671-6981.20260105

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Journal of Psychological Science ›› 2026, Vol. 49 ›› Issue (1) : 35-44. DOI: 10.16719/j.cnki.1671-6981.20260105

General Psychology，Experimental Psychology & Ergonomics

Time Course of Predictions Adjustment to Visual Perception: Evidence from EEG

Fu Chunye^1,2, Li Aixin², Lyu Yong¹

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Abstract

Visual predictive coding theory posits that the brain actively generates predictions about incoming sensory input and computes prediction errors when the input deviates from expectations. Numerous studies have investigated the neural correlates of predictive coding by comparing brain responses to expected and unexpected stimuli. However, most research has focused on the dichotomy between stimuli that either conform to or violate expectations, neglecting an intermediate stimulus type that falls between these two extremes: stimuli that violate expectations but share perceptual similarity with expected stimuli. Incorporating this novel stimulus type into the predictive coding framework could offer a more nuanced understanding of the neural mechanisms underlying visual perception and the updating of internal models. The present study aimed to investigate the dynamic neural processes underlying visual perception in three conditions - expected stimuli, unexpected-dissimilar stimuli, and unexpected-similar stimuli - by combining event-related potential (ERP) techniques with a visual statistical learning paradigm. We hypothesized that the perceptual similarity between unexpected and expected stimuli would modulate neural activity in a stage-specific manner, revealing the dynamic interplay between expectation and perceptual similarity in shaping visual predictive coding processes.
In this ERP study, human participants were exposed to sequentially presented pairs of visual object stimuli, where the identity of the first object predicted the second object to varying degrees of expectancy based on learned conditional probabilities. On expected trials, the first object effectively predicted the identity of the second object with a 60% probability, whereas on unexpected trials, the first object only predicted the second object with a 20% probability. For unexpected stimuli, perceptual similarity was further manipulated by presenting either two visually similar objects or two perceptually distinct objects. These were referred to as “unexpected-similar stimuli” and “unexpected-dissimilar stimuli”, respectively. The experiment progressed through three phases, including an initial statistical learning phase to implicitly establish predictive relationships between the object pairs, a thresholding phase to calibrate task difficulty and equate baseline performance across participants, and the main experimental phase.
The results revealed clear differences in the pattern of neural activity related to predictive coding over time, demonstrating dynamic influences of predictions on visual processing and consciousness. In the early time window around 100ms, both expected and unexpected-similar stimuli elicited enhanced P1 ERP components. Considering the cognitive functions referred to P1 components, this indicates rapid attentional selection for both stimulus types. In addition, only the unexpected-dissimilar stimuli subsequently elicited a greater N2 component around 200~300ms, which is consistent with neural surprise responses and suggests that the prediction error signal is activated, triggering higher-level processing to update the internal model. Finally, in the later time window around 350~500ms, only the expected stimuli elicited an enhanced P3 component, suggesting facilitated perceptual discrimination and decision-making for expected inputs. Beyond that, the absence of heightened N2 and P3 components in response to unexpected-similar stimuli reflects the presence of intricate mechanisms in predictive coding process. In other words, although violating predictions, unexpected-similar stimuli do not prompt the updating of internal models, and are incapable of forming more accurate visual representations.
By incorporating the novel stimulus type of unexpected stimuli with similarity into the predictive coding framework, this study sheds light on the characteristics and necessary conditions for updating internal models, providing a more comprehensive understanding of visual predictive coding processes. The results highlight the dynamic interplay between expectation and perceptual similarity in shaping neural responses across different stages of visual processing. This research not only advances our theoretical understanding of predictive coding mechanisms but also has practical implications for optimizing the design of brain-inspired artificial intelligence systems. Furthermore, the findings may offer valuable insights into the neural basis of perceptual and cognitive dysfunctions in certain neurological and psychiatric disorders characterized by impaired predictive coding.

Key words

visual perception / predictive coding / perceptual similarity / ERP / visual statistical learning paradigm

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Fu Chunye, Li Aixin, Lyu Yong. Time Course of Predictions Adjustment to Visual Perception: Evidence from EEG[J]. Journal of Psychological Science. 2026, 49(1): 35-44 https://doi.org/10.16719/j.cnki.1671-6981.20260105

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