Algorithmic Zone 3 - EEG Eye-tracking fNIRS

FESBE 2026, AI, EEG, eye-tracking, and fNIRS in the battle for attention

Before talking about artificial intelligence, algorithms, or social media, we return to the body. Eyes. Breathing. Jaw. Thumb. Neck tilted forward. Chest slightly held. Fragmented attention. The body knows it is being pulled before consciousness fully notices.

This blog begins with a central question:

what happens to the tensional selves when attention is captured by digital environments designed to keep the body in alertness, comparison, and desire?

The FESBE 2026 program opens space for this discussion by bringing themes such as artificial intelligence, open science, neuroscience of learning, biological rhythms, advanced technologies, scientific education, and mental health.

In the BrainLatam2026 language, Algorithmic Zone 3 is the state in which the body stops perceiving real territory and begins responding to digital stimuli that hijack attention, emotion, and belonging. It is not just “using the phone too much.” It is when the algorithm begins organizing the tensional selves: the self that must perform, the self that must appear, the self that must consume, the self that must prove value, the self that must hate in order to belong.

Memory, in this context, no longer serves only creative doing. It begins to sustain digital characters. The subject recruits affective memories, fears, beliefs, insecurities, and desires to maintain an online social figure. This is the critical point: the body is still trying to belong, but the perceived territory has been replaced by an architecture of stimuli.

AI does not need to “dominate” the mind in a magical way. It only needs to repeatedly modulate attention, reward, social comparison, and predictability. Recent reviews suggest that intense social media use may be related to neurocognitive changes involving attention, emotional response, and brain activity patterns measured by EEG.

Here, eye-tracking becomes essential. It allows us to observe where the gaze goes, how long it fixates, what it avoids, what it seeks, and what captures salience. In digital environments, this helps measure how images, faces, notifications, social metrics, and visual calls compete for the attentional field.

EEG helps observe the fast dynamics of attention: vigilance, surprise, error, conflict, fatigue, impulsivity, expectation, and response to stimuli. If the algorithm operates in milliseconds, EEG is one of the most coherent tools for listening to that temporality.

fNIRS/NIRS becomes relevant when the question involves the prefrontal cortex, cognitive load, control, decision-making, and more ecological tasks. Recent studies point to the use of fNIRS combined with eye-tracking to measure cognitive load and attention in educational and digital contexts.

The BrainLatam2026 hypothesis would be:

Algorithmic Zone 3 appears when attention no longer serves the perception of body-territory and begins sustaining digital characters of performative belonging.

In this state, APUS weakens: the body perceives less space, posture, breathing, and territory. Tekoha becomes confused: anxiety becomes urgency, comparison becomes desire, fear becomes engagement, loneliness becomes consumption. Jiwasa is also distorted: collective synchrony becomes algorithmic affective contagion, not real belonging.

A possible experimental design:

Compare adolescents or young adults in three conditions:

1. calm reading in an environment without notifications;

2. short-feed use with neutral content;

3. short-feed use with high social salience: metrics, conflict, comparison, approval, and urgency.

Possible measures:

• EEG for fast attention, surprise, and control;

• fNIRS for prefrontal load;

• eye-tracking for visual capture;

• HRV/RMSSD for autonomic regulation;

• breathing for bodily rhythm;

• GSR for emotional activation;

• jaw EMG for tensional self-expression;

• brief self-reports on belonging, anxiety, and comparison.

The question would not be “are social networks bad?” That would be too poor. The serious question would be:

which digital architectures push the body into Zone 3, and which allow a return to Zone 2?

The decolonial critique is essential. Latin American youth do not use social media in a vacuum. They use it within contexts of inequality, precarious schools, symbolic violence, racism, aesthetic pressure, unemployment, religion, politics, consumption, and the search for belonging. Algorithmic Zone 3 is not only technological. It is social, economic, and territorial.

That is why Brainlly is a central avatar here: translating this discussion for adolescents without moralism. Tekoha helps perceive what happens inside the body. APUS reminds us that real territory needs to be felt again. Jiwasa asks whether there is true belonging or only synchronization through pressure. Math/Hep demands method: one hypothesis at a time, without turning cultural critique into automatic conclusion.

DREX Cidadão also enters this debate. If the attention economy profits from insecure, comparable, and constantly lacking bodies, a policy of belonging and citizen metabolism could reduce basal vulnerability to algorithmic capture. A body less pressured by survival may need to perform less, consume less identity, and hate less in order to belong.

In the end, Algorithmic Zone 3 is not the phone. It is not AI. It is not the screen alone. It is the encounter between persuasive technology, bodily insecurity, affective memory, social inequality, and digital characters that cost too much for the body.

The BrainLatam2026 question becomes:

who is educating the attention of our tensional selves: the body-territory or the algorithm?

Recent References Supporting This Text

1. Satani et al. (2025) — Study on the neurocognitive impact of social media using EEG to assess brain activity patterns and cognitive/emotional responses.

2. Cha (2026) — Study using eye-tracking and fNIRS to investigate visual attention and prefrontal cognitive responses to AI-generated characters.

3. Pinheiro et al. (2024) — Article on eye-tracking and fNIRS as neuroscientific tools to investigate attention, memory, and cognitive processes in learning.

4. Chen et al. (2025) — Study integrating fNIRS and eye-tracking to predict individual cognitive load using machine learning models.

5. Li et al. (2025) — Bibliometric review on mobile eye-tracking and neuroimaging technologies, highlighting the role of fNIRS in social learning and real-world environments.

6. Social Attention Research (2023) — Article on eye-tracking in social attention and second-person interaction research.

7. Rivas-Vidal et al. (2026) — Review on EEG and eye-tracking integration to assess attention, perception, and situational awareness.