Human attention works as a filter for the information processing system of the brain. During any multitasking or even more simple activities attention strategically selects and chooses information for any important process. Our limitations in mental cognitive resources dictate some decisiveness for effective task completion and optimization of cognitive effort for remediate problems. Multi-tasking can be effective only in limited degree, time, and conditions for humans. Science examines attention as an essential resource for demand of tasks which must be performed and tries to create models and principles useful to mitigate attention problems. Which business owner or management team does not ask how to improve product to be more attention absorbing item?

Eyes have limited field of view. This is why engineers consider AOI, cognitive processes and human physiology when principles of design for displays must be established.
In Areas of Interest (AOI) which is a wide range of dynamic sources of information which human operators are currently focused on. Scan time percentage in displayed information informs about quality of layout or screen and prevent dangerous situation where time of comprehension of data could be crucial for security of user and environment (for example police radars or information on the road displayed in electronic form). In design and art there is a well-known term – focal point. For each painting, any creator is trying to design an ideal focal point for composition to improve attention for each future recipient of the work. In technology model called SEEV could be the artist substitute who predicts and contributes to design perfect AOI based on calculated net attractiveness in the workspace. I wonder what Leonardo da Vinci would do today by combining the current achievements of technology with his passion for aesthetics?

AOI net attractiveness is based on 4 factors: salience which correlates with intuitive process of decision about importance of object on the background of other things, effort (high effort makes scanning more difficult), expectancy (which is related to event rate of change or appear data in accurate place) and value (the relevance of data or action).
SEEV model found usage in health care machines for our health supervision, flight deck in airplanes industry, Tesla automotive products or robotic control in factories. Attentional capture in the human mind is based on the work of the frontal cortex. Designers working in the technology industry with concentration of visual scanning products are aware of principles on how to create machines with more resistance for interferences.
A lot of applications which express intuitiveness and visibility are based on the application of this model and create attention tunneling in the user engagement for focus on specific channels of information.
Also not without a significance was developing technology for eye-tracking, because to create efficient machines based on improvements of computer vision, it was important look closely on overt attention (type of attention when you move eyes towards something). First experiments about covert and overt attention types analyzed brain signals where the person is watching or paying attention to the object.

Multitasking trend and learning.

In a job environment each day, working memory is bombarded by many bits of information. Function of our working memory with limited capacity is processing all perceptual and linguistic information from the environment. Klaus Kirschke, a researcher attached to artificial intelligence and cognitive modeling, turned to clarification and approximation of the issue of how humans perceive, remember, and learn. Kirschke’s research bridged cognitive psychology and computer science, aiming to simulate human cognitive processes. He made significant advances in understanding pattern recognition, problem-solving, and human-AI interaction.

Kirschke discovery about associative learning driven by rational goal orientation and mistakes redaction impacted on interface design.
He discover that people learned previously one cue, they avoid future errors through choosing a different cue. Even if the result could be more useful, they do not decide to behave differently at this moment.
This learned behavior accustoms users to repetitive behaviors even when they communicate on another device, but with similar elements or structure with known features. For the user, the schematicity of the elements predicts satisfactory results.

Kirschke observed that multitasking in humans often leads to decreased efficiency and increased errors because the brain is not well-suited to handling multiple tasks simultaneously. He noticed that cognitive resources are limited, and when divided among several tasks, each task receives less attention, leading to poorer performance.

If the working memory of a person is strongly overloaded with stress, there is a greater probability of absent-mindedness, lack of focus on the task and committing oversights. This is why multi-tasking with increased effort parameter is less effective for a business than single task created in closed period.

However, predictive learning process during carries out the duties needs predictions errors to be efficient learning process. The human brain learns quicker when tasks representations forms and our forecasts are surprisingly different. The use of this information during the visual campaign of the product can be one of the tactics in the promotion and increase of attention of the customer on the brand. The unexpected events are more novel.

This discovery about attention and learning correlations inproves designing machines that can handle tasks concurrently without the degradation in performance that humans experience. Such systems can allocate resources dynamically and prioritize tasks based on importance, enabling more efficient multitasking in computational models and AI systems. Especially where is the talk about deep learning. Deep learning consists of hierarchical learning algorithms with many layers inspired by the complicated architecture of the mammal's brain and some types of visual system. The choices made during the learning process directly impact the model's accuracy, speed of convergence, and ability to generalize from training data to new, unseen data.

Grossberg’s Adaptive Resonance Theory (ART) focused on brain ability to learn latest information without forgetting previously acquired knowledge According to his research the brain uses a combination of fast learning (plasticity) and slow learning (stability). Memory enhancement depends on performance of resonate state, which is possible when brain firstly recognize patterns, and then will perform a comparison between new sensory information with existing memory representations and eventually they will converge on a certain level. If the input does not match any stored pattern, the brain creates a new memory trace, allowing it to adapt to novel stimuli.

In neural networks, learning added information often leads to changes in the structure or weights of the system, which can by accident change or erase old knowledge—a phenomenon known as "catastrophic forgetting."

In the human brain, stability allows for the retention of long-term memories and learned skills, while plasticity enables learning and adaptation to new experiences and information. Balancing these two aspects is crucial because a system that is too stable would resist learning and become rigid, while one that is too plastic might constantly overwrite existing knowledge with new information, leading to inconsistency and unreliability.

By minimizing unnecessary attention shifts and helping users maintain focus,
Brain-computer interfaces can become more efficient tools for communication, control, and interaction, particularly in applications that require sustained concentration.

Brain-computer interface is founded on artificial intelligence communication system which helps people with severe disabilities contact by machines such as computers, assistive appliances, speech synthesizers or neural protheses with external environment or military service members to operate a drone with processed brain signals.

BCI interface recognizes a set of patterns from brain signals. "Thoughts deciphering" process includes preprocessing, signal enhancement through noise reduction, mapping discriminative information into vectors and signals classification. Due to generated commands from control interface all devices can take intentional action. Referring to the broader field of designing and studying the interaction between humans and computers, it is worth mentioning devices that have opened the world to create an alternative reality and voice recognition.
Human Brain Interface can be developed to reduce cognitive load, ensuring that users can focus on essential tasks without being overwhelmed by irrelevant information. Additionally, attention research helps in optimizing the timing and nature of stimuli presented through these interfaces, making interactions more intuitive and responsive to the user's mental state. This leads to more effective communication between the brain and external devices, paving the way for advanced applications in neuroprosthetics, assistive technologies, and even enhancing cognitive functions in healthy individuals. HCI focuses on making interactions intuitive, efficient, and user-friendly.

Attention research plays a crucial role in enhancing technology correlated with image recognition and particularly human-brain interfaces (HBIs), by providing insights into how the brain selectively processes information. The mechanism of attention allows researchers to design interfaces that align with natural cognitive processes like learning, thereby improving efficiency of computers, other devices, and user experience. The focus on filtering the information also provided solutions for the study of natural language processing. Attention is a scarce resource in the digital age, and the ability of AI to manage and capture it effectively is linked to economic outcomes. As a result, attention has become a key driver of value in the AI industry, influencing everything from product design