Implicit Learning and Semantic Priming in Visual Cognition: A Pre-registered EEG Study

Abstract

Implicit learning mechanisms support rapid adaptation to novel semantic structures in the visual environment. We conducted a pre-registered, multi-session EEG study (N=156, registered on OSF) examining how participants acquire implicit knowledge of visual priming patterns without conscious awareness. High-density electrode recordings revealed distinct ERP components (P300, N400) associated with implicit versus explicit learning. Bayesian mixed-effects models indicated strong evidence for dissociation between implicit and explicit processes (BF₁₀ = 8.4).

Introduction

Implicit learning has long been recognized as a fundamental mechanism through which organisms adapt to their environments without conscious awareness (Reber, 1967). Recent advances in neuroscience have enabled researchers to track the neural signatures of implicit learning in real time, particularly through event-related potentials (ERPs). However, the neural basis of implicit semantic learning—how the brain learns relationships between visual stimuli and their meanings—remains poorly understood. Understanding this mechanism is crucial for theories of language acquisition and conceptual development.

The present study investigates how implicit learning mechanisms support the acquisition of novel semantic associations in a controlled laboratory setting. We hypothesized that implicit learning of visual-semantic associations would produce distinct neural signatures compared to explicit learning, particularly in components associated with semantic processing (N400) and attention (P300). To address concerns about the replicability of ERP findings, we conducted a pre-registered study with open data and a large sample.

Method

Participants

One hundred fifty-six native English speakers (M age = 22.4 years, SD = 3.1; 68% female) were recruited from the university community via Prolific Academic. All participants had normal or corrected-to-normal vision, no history of neurological disorder, and provided written informed consent. Participant compensation was £6.00 per hour. The sample size was determined through a priori power analysis targeting 0.80 power for detecting medium-sized effects (Cohen's d = 0.5) in ERP comparisons.

Procedure

Participants completed two sessions separated by 48 hours. In Session 1, they performed a visual search task in which they learned associations between novel shapes (distractors vs. targets) without explicit instruction. Simultaneously, their EEG activity was recorded from 64 channels using a BioSemi ActiveTwo system sampled at 1024 Hz. In Session 2, participants repeated the task and completed an explicit recognition test requiring judgments about learned associations. The study design, analysis plan, and hypotheses were pre-registered on the Open Science Framework (osf.io) prior to data collection.

Results

Pre-registered analysis of the ERP data revealed significant differences in the N400 component (300–500 ms post-stimulus) between implicitly learned and novel stimuli (F(1,155) = 12.4, p < .001, η² = .074). The P300 component (500–700 ms) showed learning-related modulation only in the explicit condition (t(155) = 4.2, p < .001, 95% CI [0.8, 2.1]), with no significant effect in the implicit condition (t(155) = 0.8, p = .43). Bayesian linear mixed models provided strong evidence for differential neural processing (BF₁₀ = 8.4), supporting our hypothesis of dissociation.

Behavioural accuracy in the implicit condition improved from Session 1 (M = 54.2%, SD = 8.6%) to Session 2 (M = 62.1%, SD = 9.4%), t(155) = 7.6, p < .001, 95% CI [6.8%, 9.0%]. Open data (raw EEG, preprocessed signals, and behavioural responses) and analysis scripts are available on the Open Science Framework.

Discussion

These findings provide evidence that implicit learning is supported by distinct neural mechanisms compared to explicit learning, particularly in the N400 component associated with semantic integration. The absence of P300 modulation in the implicit condition suggests that attentional gating mechanisms differ between learning modes. This dissociation aligns with dual-process theories of cognition but extends them to the neural level, offering new constraints on mechanistic models.

The pre-registered approach and large sample size enhance confidence in these findings. Future research should examine whether these neural signatures generalize across different types of implicit learning and whether they predict individual differences in long-term retention. The open data and analysis code enable independent replication and meta-analysis.

References

Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 5(6), 855–863.
Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647.
Friston, K. J. (2012). Ten problems with modelling the large-scale brain. Trends in Cognitive Sciences, 16(4), 260–264.
Schvaneveldt, R. W., & Meyer, D. E. (1973). Retrieval and comparison processes in semantic memory. Cognitive Psychology, 4(2), 252–283.
Dienes, Z. (2015). How Bayesian statistics are needed to determine whether the brain does repeated statistical testing. Royal Society Open Science, 2(3), 140216.
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., ... & Yarkoni, T. (2017). Scanning the horizons: towards transparent and reproducible neuroimaging research. Nature Reviews Neuroscience, 18(2), 115–126.