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Related Experiment Video

Updated: Aug 17, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
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A rubric for human-like agents and NeuroAI.

Ida Momennejad1

  • 1Microsoft Research NYC, Reinforcement Learning Station, 300 Lafayette, New York, NY 10012, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|December 13, 2022
PubMed
Summary
This summary is machine-generated.

This study proposes a rubric to clarify the inconsistent use of "human-like artificial intelligence" and "neuroAI" terms. It distinguishes between mimicking behavior, neural plausibility, and engineering goals for clearer AI research.

Keywords:
artificial intelligencedeep learninghuman behaviourneuroAIneurosciencereinforcement learning

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computer Science

Background:

  • The terms 'human-like artificial intelligence' and 'neuroAI' are increasingly used across disciplines but with inconsistent scope.
  • Research contributions range from mimicking behavior to testing neural plausibility and solving engineering problems.
  • Progress in one area does not guarantee progress in others.

Purpose of the Study:

  • To propose a simple rubric for clarifying the scope of research contributions in artificial intelligence.
  • To differentiate between commitments to human-like behavior, neural plausibility, and engineering/computer science goals.
  • To provide a framework for understanding the interactions between these dimensions.

Main Methods:

  • Development of a clarifying rubric based on three core commitments: human-like behavior, neural plausibility, and benchmark/engineering goals.
  • Illustration of the rubric's application using examples of weak and strong neuroAI and human-like agents.
  • Discussion of the generative, corroborative, and corrective interactions among the three dimensions.

Main Results:

  • A clear distinction is established between different types of neuroAI and human-like agents based on their research goals.
  • The proposed rubric facilitates a more precise understanding of individual research contributions.
  • The study highlights the importance of interdisciplinary interactions and iterative feedback loops for advancing AI.

Conclusions:

  • Future progress in artificial intelligence necessitates strong interdisciplinary collaboration with rigorous validity testing.
  • The proposed rubric can enhance clarity and communication in the fields of AI, neuroscience, and cognitive science.
  • Meticulous validation and iterative feedback loops are crucial for achieving significant and potentially unforeseen advances in AI.