Artificial intelligence (AI) has developed significantly, from powering self -propelled cars to help in medical diagnoses. However, one important question remains: Can AI ever pass a cognitive test designed for people? While AI has achieved impressive results in areas such as language processing and problem solving, he is still trying to repeat the complexity of human thought.
AI models like Chatgpt It can generate text and effectively solve problems, but they do not work so well in the face of cognitive tests such as Montreal cognitive assessment (MACA)designed to measure human intelligence.
This gap between AI technical achievements and cognitive restrictions emphasizes significant challenges regarding its potential. AI must still match human thinking, especially in tasks requiring abstract reasoning, emotional understanding and contextual awareness.
Understanding cognitive tests and their role in the AI assessment
Cognitive tests such as power are necessary to measure various aspects of human intelligence, including memory, reasoning, problem solving and spatial awareness. These tests are widely used in clinical conditions to diagnose conditions such as Alzheimer and Dementia, offering an insight into how the brain functions in various scenarios. Tasks such as recalling words, attracting the clock and recognition of patterns assess the brain's ability to navigate complex environments, skills that are necessary in everyday life.
However, after applying to AI) the results are completely different. AI models, such as Chatgpt or Google's Gemini, can lead in tasks such as recognition of patterns and text generation, but struggle with the aspects of cognition that require a deeper understanding. For example, while artificial intelligence can comply with clear instructions for the task, it is not possible to understand abstract, interpretation of emotions or the use of context, which are the basic elements of human thinking.
Cognitive tests are therefore served by a double goal when assessing AI. On the one hand, they emphasize the strengths of AI in data processing and effective solving of structural problems. On the other hand, they reveal significant gaps in AI's ability to recreate the full range of human cognitive functions, especially those including complex decision making, emotional intelligence and contextual awareness.
With the widespread use of artificial intelligence in areas such as healthcare and autonomous systems, they require something more than just performing tasks. Cognitive tests are a reference point for assessing whether AI can cope with tasks requiring abstract reasoning and emotional understanding, key features for human intelligence. For example, in healthcare, while AI can analyze medical data and predict diseases, it cannot provide emotional support or make refined decisions depending on understanding the unique situation of the patient. Similarly, in autonomous systems such as self -propelled cars, the interpretation of unpredictable scenarios often requires intuition similar to man who lack current AI models.
By using cognitive tests designed for people, scientists can identify areas in which AI requires improvement and develop more advanced systems. These assessments also help to determine realistic expectations as to what AI can achieve and emphasize where human involvement is still necessary.
AI restrictions in cognitive tests
AI models have made impressive progress in data processing and recognition of patterns. However, these models encounter significant restrictions when it comes to tasks requiring abstract reasoning, spatial awareness and emotional understanding. AND Last examination This tested several AI systems using Montreal cognitive assessment (MACA), tools designed to measure human cognitive abilities, revealed a clear gap between AI forces in structured tasks and its struggles with more complex cognitive functions.
In this study, Chatgpt 4o obtained 26 out of 30, which indicates mild cognitive disorders, while Google Gemini obtained only 16 out of 30, reflecting serious cognitive disorders. One of the most important AI challenges were visual and spacious tasks, such as scratching clock or replicating geometric shapes. These tasks that require understanding of spatial relationships and organizing visual information are areas in which people stand out intuitively. Despite receiving clear instructions, AI models fought for thorough performance of these tasks.
Human cognition integrates sensory contribution, memories and emotions, enabling adaptive decision making. People rely on intuition, creativity and context when solving problems, especially in ambiguous situations. This ability to think abstractly and use emotional intelligence in making decisions is a key feature of human cognition, and therefore enables individuals to move with complex and dynamic scenarios.
However, AI works through data processing through algorithms and statistical patterns. Although it can generate answers based on learned patterns, he doesn't really understand the context or the meaning of data. This lack of understanding makes artificial intelligence to perform tasks requiring abstract thinking or emotional understanding, which is necessary in tasks such as cognitive tests.
Interestingly, cognitive restrictions observed in AI models have similarities to impairment observed in neurodegenerative diseases such as Alzheimer. In the study, when AI was asked about spatial awareness, its answers were too simplified and dependent on the context, reminiscent of the answers of people about cognitive fall. These discoveries emphasize that although AI is distinguished by the processing of structured and forecasting data, they lack the depth of understanding required for more refined decision making. This restriction applies especially to healthcare and autonomous systems in which judgment and reasoning are critical.
Despite these restrictions, there is a potential for improvement. Newer versions of AI models, such as ChatgPT 4O, showed progress in reasoning and decision -making tasks. However, recreation of human -like cognition will require improvement of AI design, potentially by quantum calculations or more advanced neural networks.
AI fights with complex cognitive functions
Despite the progress in AI technology, it remains far from the transition of cognitive tests intended for people. While AI is leading in solving organized problems, it is not in the scope of more refined cognitive functions.
For example, AI models often hit a sign when they were asked to draw geometric shapes or interpret spatial data. People naturally understand and organize visual information that AI tries to do effectively. This emphasizes the fundamental issue: AI's ability to process data does not mean understanding how human minds work.
At the basis of AI restrictions, the algorithm is based on the algorithm. AI models work by identifying patterns in data, but they lack contextual awareness and emotional intelligence that people use to make decisions. Although AI can effectively generate outputs based on what he has been trained on, he does not understand the meaning of the results that man does. This inability to engage in abstract thinking, combined with a lack of empathy, prevents AI from performing tasks requiring deeper cognitive functions.
This gap between AI and human cognition is visible in healthcare. AI can help in tasks such as analysis of medical scans or anticipating diseases. Despite this, he cannot replace human judgment in making decisions that include understanding the circumstances of the patient. Similarly, in systems such as autonomous vehicles AI can process huge amounts of data to detect obstacles. Despite this, he cannot repeat the intuitions that people rely on when making decisions in a split second in unexpected situations.
Despite these challenges, AI showed the potential of improvement. Newer AI models begin to support more advanced tasks including reasoning and basic decision making. However, despite the fact that these models are far from matching the wide range of human cognitive abilities required to pass cognitive tests intended for people.
Lower line
To sum up, artificial intelligence has made impressive progress in many areas, but still has a long way before the opinion of cognitive tests intended for people. While it can handle tasks such as data processing and problem solving, and fights tasks that require abstract thinking, empathy and contextual understanding.
Despite the improvements, AI is still fighting tasks such as spatial awareness and decision making. Although AI has the promise of the future, especially in the case of technological progress, it is not a recreation of human cognition.
