Copyright © 2026 All Rights Reserved
3 Min
As AI continues to develop rapidly, there is a growing curiosity about whether its capabilities will surpass those of humans. Many are wondering if there is already an equation between the human mind and AI machines. However, the reality is that AI capabilities remain limited.
There is one particular example to demonstrate it. According to a recent study conducted by a psychology and data science team at NYU, infants perform better than artificial intelligence in identifying the motivations behind other people’s actions.
To gain insight into the differences between human and AI capabilities, a group of researchers conducted a series of experiments comparing the responses of 11-month-old infants to those of state-of-the-art neural network models driven by machine learning. The researchers used the “Baby Intuitions Benchmark” (BIB), a set of six tasks designed to probe common psychology to test infant and machine intelligence. This approach allowed for comparing performance between the two and, importantly, provided an empirical foundation for building AI that mimics human intelligence.
During the experiments, infants watched videos of simple animated shapes moving in ways that mimicked human behavior. Researchers also showed these videos to neural network models. The results revealed that infants could recognize human-like motivations and predict consistent goals driving these actions, even with simplified shapes.
Infants demonstrated this by looking longer at events that defied their predictions. This method effectively measures their understanding. In contrast, the neural network models showed no signs of grasping these motivations. This suggests they lack the foundational principles of commonsense psychology that infants have.
Infants are well-known for their fascination with others, shown by their keen observation and social interactions. Research on infants’ “commonsense psychology” reveals that they can understand intentions, goals, and actions, anticipating others’ behavior effectively. These predictive skills are vital to human social intelligence.
Lead author Sara Dillon commented that the human infant’s foundational knowledge is limited and abstract and reflects our evolutionary inheritance. Yet, it can accommodate any context or culture in which that infant might live and learn. The paper’s co-authors were Gala Stojnić, Kanishk Gandhi, and Shannon Yasuda, all affiliated with NYU during the study.
Moira Dillon, an assistant professor in New York University’s Department of Psychology and the paper‘s senior author, which appears in the journal Cognition, explains:
“Adults and even infants can easily make reliable inferences about what drives other people’s actions. Current AI finds these inferences challenging to make. The novel idea of putting infants and AI head-to-head on the same tasks is allowing researchers better to describe infants’ intuitive knowledge about other people and suggest ways of integrating that knowledge into AI”.
The study’s findings underscore the fundamental distinctions between Cognition and computation. They also highlight the inadequacies in today’s technologies and indicate areas where AI needs to be improved to replicate human behavior more comprehensively. The current AI models have limitations in replicating human behavior comprehensively because human behavior is complex and varies depending on the situation, context, and individual perspectives.