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.

翻译：认知能力的量化依赖于识别依赖这些能力的特定行为任务。这种依赖性无法得到保证，因为任务所调用的能力无法通过实验预先控制或约束，导致其对特异性和普适性失效的脆弱性未知。通过评估雷文渐进矩阵（RAPM）——一种广泛用于流体智能临床测试的简化版本——我们展示了LaMa模型的表现：该模型是一种自监督人工神经网络，仅通过完成自然场景图像的部分遮挡任务进行训练，在无任何任务特定归纳偏置或训练的情况下，即可首次达到人类水平的测试得分。与健康受试者和局灶性脑损伤患者队列相比，LaMa在题目难度上呈现出与人类相似的变异，且在其整合全局空间模式能力退化时，会产生与右侧额叶损伤特征相符的错误。LaMa狭窄的训练范围和有限容量（与果蝇神经系统相当）表明，RAPM可能对无需抽象推理的简单计算解决方案持开放态度。