High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, Russell Square House, Bloomsbury, London, UK.
Department of Neuropsychology, National Hospital for Neurology and Neurosurgery, London, UK; UCL Queen Square Institute of Neurology, London, UK.
Cortex. 2024 Oct;179:62-76. doi: 10.1016/j.cortex.2024.07.003. Epub 2024 Aug 3.
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 representative 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 suggest matrix-style tests may be open to computationally simple solutions that need not necessarily invoke the substrates of reasoning.
认知能力的量化依赖于确定一个依赖于它们的行为任务。这种依赖性不能得到保证，因为任务所调用的能力不能通过实验控制或预先限制，从而导致特异性和通用性失效的未知脆弱性。我们评估了广泛用于流体智力临床测试的 Raven 的高级渐进矩阵（RAPM）的精简版，结果表明，仅通过完成部分遮挡的自然环境场景图像的完成来训练的自我监督人工神经网络 LaMa，无需任何特定任务的归纳偏差或训练，就能立即达到具有代表性的人类水平的测试分数。与健康和局灶性损伤参与者的队列相比，LaMa 表现出与项目难度相似的人类变化，并且在其整合全局空间模式的能力下降时，会产生类似于右额叶损伤的错误。LaMa 狭窄的训练和有限的能力表明，矩阵式测试可能容易受到计算上简单的解决方案的影响，而这些解决方案不一定需要调用推理的基础。
