A Rubric for Human-like Agents and NeuroAI
This addresses a conceptual problem for researchers in cognitive, neuro-, and computer sciences by providing a framework to reduce confusion and enhance collaboration, though it is incremental as it builds on existing discussions.
The paper tackles the inconsistent use of terms like 'human-like AI' and 'neuroAI' by proposing a rubric to clarify contributions based on commitments to behavior, neural plausibility, or engineering goals, aiming to improve interdisciplinary progress in AI.
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.