Recent studies show evidence for emergent cognitive abilities in Large
Pre-trained Language Models (PLMs). The increasing cognitive alignment of these
models has made them candidates for cognitive science theories. Prior research
into the emergent cognitive abilities of PLMs has largely been path-independent
to model training, i.e., has focused on the final model weights and not the
intermediate steps. However, building plausible models of human cognition using
PLMs would benefit from considering the developmental alignment of their
performance during training to the trajectories of children's thinking. Guided
by psychometric tests of human intelligence, we choose four sets of tasks to
investigate the alignment of ten popular families of PLMs and evaluate their
available intermediate and final training steps. These tasks are Numerical
ability, Linguistic abilities, Conceptual understanding, and Fluid reasoning.
We find a striking regularity: regardless of model size, the developmental
trajectories of PLMs consistently exhibit a window of maximal alignment to
human cognitive development. Before that window, training appears to endow
"blank slate" models with the requisite structure to be poised to rapidly learn
from experience. After that window, training appears to serve the engineering
goal of reducing loss but not the scientific goal of increasing alignment with
human cognition.

经验表明，大型预训练语言模型的新认知能力使其成为认知科学理论的候选模型。本研究通过心理测量学测试选择四组任务，对十个热门预训练语言模型进行评估，发现无论模型大小，PLMs 的训练过程在人类认知发展的某个窗口期内表现出最高程度的一致性。在该窗口期之前，训练为模型提供了基础结构，使其能够迅速从经验中学习；而在该窗口期之后，训练似乎更多地用于工程目标而非科学目标。