The quantification of cognitive powers rests on identifying a behavioural
task that depends on them. Such dependence cannot be assured, for the powers a
task invokes cannot be experimentally controlled or constrained a priori,
resulting in unknown vulnerability to failure of specificity and
generalisability. Evaluating a compact version of Raven's Advanced Progressive
Matrices (RAPM), a widely used clinical test of fluid intelligence, we show
that LaMa, a self-supervised artificial neural network trained solely on the
completion of partially masked images of natural environmental scenes, achieves
human-level test scores a prima vista, without any task-specific inductive bias
or training. Compared with cohorts of healthy and focally lesioned
participants, LaMa exhibits human-like variation with item difficulty, and
produces errors characteristic of right frontal lobe damage under degradation
of its ability to integrate global spatial patterns. LaMa's narrow training and
limited capacity -- comparable to the nervous system of the fruit fly --
suggest RAPM may be open to computationally simple solutions that need not
necessarily invoke abstract reasoning.

通过使用自我监督的人工神经网络 LaMa 对部分遮挡的自然环境场景图像完成的紧凑版本的 Raven Advanced Progressive Matrices（RAPM）测试，我们发现 LaMa 在无任何特定任务的归纳偏差或训练的情况下即可达到类人级测试分数，并呈现出与人类类似的难度变化和右额叶损伤所特有的错误，表明 RAPM 可能对于计算简单的解决方案具有开放性，不一定需要涉及抽象推理。