The minimal computational substrate of fluid intelligence

TL;DR

This study demonstrates that a simple, self-supervised neural network trained on natural images can achieve human-level performance on a fluid intelligence test, challenging assumptions about the necessity of complex reasoning for such tasks.

Contribution

The paper introduces LaMa, a neural network that attains human-like fluid intelligence test scores without task-specific training or inductive biases, suggesting simpler models can solve complex reasoning tasks.

Findings

LaMa achieves human-level scores on RAPM.

LaMa exhibits human-like variation with item difficulty.

Errors in LaMa resemble those of right frontal lobe damage.

Abstract

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.