  Relations are basic building blocks of human cognition. Classic and recent
work suggests that many relations are early developing, and quickly perceived.
Machine models that aspire to human-level perception and reasoning should
reflect the ability to recognize and reason generatively about relations. We
report a systematic empirical examination of a recent text-guided image
generation model (DALL-E 2), using a set of 15 basic physical and social
relations studied or proposed in the literature, and judgements from human
participants (N = 169). Overall, we find that only ~22% of images matched basic
relation prompts. Based on a quantitative examination of people's judgments, we
suggest that current image generation models do not yet have a grasp of even
basic relations involving simple objects and agents. We examine reasons for
model successes and failures, and suggest possible improvements based on
computations observed in biological intelligence.
