  Researchers across cognitive, neuro-, and computer sciences increasingly
reference human-like artificial intelligence and neuroAI. However, the scope
and use of the terms are often inconsistent. Contributed research ranges widely
from mimicking behaviour, to testing machine learning methods as neurally
plausible hypotheses at the cellular or functional levels, or solving
engineering problems. However, it cannot be assumed nor expected that progress
on one of these three goals will automatically translate to progress in others.
Here a simple rubric is proposed to clarify the scope of individual
contributions, grounded in their commitments to human-like behaviour, neural
plausibility, or benchmark/engineering goals. This is clarified using examples
of weak and strong neuroAI and human-like agents, and discussing the
generative, corroborate, and corrective ways in which the three dimensions
interact with one another. The author maintains that future progress in
artificial intelligence will need strong interactions across the disciplines,
with iterative feedback loops and meticulous validity tests, leading to both
known and yet-unknown advances that may span decades to come.
