  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.
