EEG and Aperiodic Neural Activity: Learning from Feedback Even Without Visual Awareness

We usually think that learning from feedback requires clear awareness: seeing the error, understanding the success, perceiving the reward, and adjusting the next action. But the study by Liu, Jia, Sun, Colzato, and Hommel shows something deeper: behavior can be adjusted by feedback even when that feedback does not reach visual awareness.

The scientific question of the article is excellent: does feedback-based learning depend on conscious visibility, or can it occur even when the visual feedback is invisible? To answer this, the researchers combined a time-estimation task with EEG and a continuous flash suppression — CFS paradigm, which can make visual images invisible to conscious perception.

The study deserves praise because it addresses a delicate and important question: separating what the brain uses to learn from what the person consciously perceives. Instead of assuming that we only learn when we “see” feedback, the authors tested whether invisible rewards and errors could still modify the next response.

The sample included 60 young participants, and 2 were excluded because they were able to perceive the invisible feedback during the awareness-check task. The experimental design was 2 × 2: positive or negative feedback, visible or invisible. Participants performed a 1-second time-estimation task and received feedback through facial expressions: a happy face as positive feedback and a fearful face as negative feedback.

In the visible-feedback condition, the same facial expression was presented to both eyes. In the invisible-feedback condition, the dominant eye received dynamic Mondrian masks, while the other eye received the facial expression. This CFS procedure suppressed visual awareness of the face while preserving the temporal structure of the feedback. Figure 1 of the article shows the task sequence, and Figure 2 shows the test used to verify whether participants truly could not see the face.

EEG was recorded with a 64-channel BrainAmp MR system from Brain Products, using the 10/20 electrode system, online reference at FCz, ground at AFz, mastoid electrodes, HEOG, and VEOG. The sampling rate was 1000 Hz, with an online bandpass filter from 0.016 to 70 Hz. The data were later processed with EEGLAB, MNE, and FOOOF/SpecParam, separating periodic and aperiodic neural activity. This is especially relevant for BrainLatam/Brain Support because it shows an advanced EEG design: not only ERP analysis, but also modern spectral parameterization.

The authors analyzed classic feedback markers: REWP, between 240 and 300 ms at FCz; P3a, between 300 and 450 ms at FCz; theta power; aperiodic-adjusted theta; and the aperiodic exponent, extracted between 3 and 40 Hz with FOOOF. This separation is central because many traditional EEG analyses mix oscillatory activity with the aperiodic background of the signal.

The behavioral results were strong. After positive feedback, participants showed smaller time-estimation error on the next trial. After negative feedback, they made larger adjustments. The central point is that this happened after both visible and invisible feedback. In other words: behavior learned from feedback even when the person had no visual awareness of it.

But the electrophysiological results revealed an important dissociation. REWP, P3a, theta, and the aperiodic exponent showed feedback-valence effects mainly when the feedback was visible. When feedback was invisible, these markers did not show the same clear sensitivity to valence. This means that visual awareness modulated the electrical response to feedback, but it was not necessary for behavioral adjustment.

The most interesting finding appears in the correlations with performance. Among all electrophysiological markers, only the aperiodic exponent consistently predicted time-estimation error on the following trial. The authors interpret this as evidence that the general neural state — especially a state with lower cortical noise — provides better conditions for learning from feedback.

From the BrainLatam2026 perspective, this is very powerful. Learning does not happen only when a person verbally understands what happened. Before conscious explanation, there is a body-brain system adjusting energy, probability, error, expectation, control, and action. Visual awareness may help, but it is not the only doorway into learning.

Here, the Damasian Mind becomes central: interoception and proprioception as the living basis of learning. Feedback is not merely information on a screen. It changes the body state, expectation, tension, readiness to try again, and the way the nervous system prepares the next response.

The avatar-lens for this blog can be Iam with Brainlly. Iam perceives the “self” that learns before explaining. Brainlly protects methodological rigor: it is not enough to say that unconscious learning happened; we need to measure EEG, separate periodic and aperiodic activity, model behavior, and avoid magical interpretations.

This study also speaks directly to Tensional Selves. When a person receives feedback, even invisible feedback, the system can reorganize a small Tensional Self: continue, change, repeat, correct, wait. This Tensional Self does not need to begin as a conscious sentence. It can begin as a bioelectrical and behavioral adjustment.

It also connects with Zones 1, 2, and 3. In Zone 1, the person performs the task and adjusts behavior. In Zone 2, there is the possibility of more flexible updating, with Fruition and Metacognition. In Zone 3, feedback can be captured by rigidity, fear, punishment, or ideology. The article helps us think that the background neural state may influence whether feedback becomes learning or merely defensive repetition.

The generous decolonial critique is that neuroscience often treats feedback as an isolated event: a face, a light, a word, a correct answer, an error. But in real life, feedback is loaded with context: teacher, family, shame, belonging, competition, anxiety, poverty, algorithm, punishment, and hope. Feedback does not enter a neutral brain. It enters a body-territory.

It is also important to respect the limits pointed out by the article itself. The authors caution that facial expressions are not the most classic stimuli for isolating REWP/FRN because happy and fearful faces also activate emotional and perceptual processes. In addition, the meanings of the faces were not counterbalanced. This does not weaken the central behavioral finding, but it requires care when interpreting the ERP results.

The BrainLatam2026 question would be: when invisible feedback changes behavior, is it producing real learning, automatic adjustment, or a broader reorganization of the bodily state? To answer this, we could combine EEG + fNIRS + eye-tracking + HRV/RMSSD + respiration + GSR + EMG. EEG would separate periodic and aperiodic activity; fNIRS would observe prefrontal demand; eye-tracking would show micro-attention; HRV and respiration would indicate autonomic regulation; GSR would show salience; and EMG would reveal bodily micro-preparation.

A Latin American experimental design could compare visible, partially visible, and invisible feedback in school learning tasks, cognitive games, social decision-making, or motor training. The question would be: do children and adolescents in more regulated bodily states learn better from subtle feedback than children under stress, fear, or hypervigilance?

The bridge with DREX Cidadão appears when we think about public policy. A country that wants real learning cannot depend only on exams, grades, and punishment. It needs to create bodily conditions for learning: sleep, food, safety, belonging, time, living schools, and less social anergy. If the background neural state influences learning, then inequality also enters the EEG.

Closing
This study shows that learning from feedback can happen even without full visual awareness, but it does not happen in a vacuum. The brain learns from a background neural state, and aperiodic activity may reveal this deep availability for behavioral adjustment. For BrainLatam2026, this opens an essential question: how can we create bodies, schools, and territories in Zone 2, where feedback is not punishment, but an opportunity to reorganize life with more freedom, criticality, and belonging?

Single Reference
Liu, D., Jia, S., Sun, Y., Colzato, L., & Hommel, B. (2026). Learning from feedback is independent from feedback visibility, but supported by aperiodic neural activity. NeuroImage, 331, 121894. doi:10.1016/j.neuroimage.2026.121894.