  A chief goal of artificial intelligence is to build machines that think like
people. Yet it has been argued that deep neural network architectures fail to
accomplish this. Researchers have asserted these models' limitations in the
domains of causal reasoning, intuitive physics, and intuitive psychology. Yet
recent advancements, namely the rise of large language models, particularly
those designed for visual processing, have rekindled interest in the potential
to emulate human-like cognitive abilities. This paper evaluates the current
state of vision-based large language models in the domains of intuitive
physics, causal reasoning, and intuitive psychology. Through a series of
controlled experiments, we investigate the extent to which these modern models
grasp complex physical interactions, causal relationships, and intuitive
understanding of others' preferences. Our findings reveal that, while some of
these models demonstrate a notable proficiency in processing and interpreting
visual data, they still fall short of human capabilities in these areas. Our
results emphasize the need for integrating more robust mechanisms for
understanding causality, physical dynamics, and social cognition into
modern-day, vision-based language models, and point out the importance of
cognitively-inspired benchmarks.
