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Related Experiment Video

Updated: Nov 19, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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Published on: June 3, 2013

Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies.

James A Reggia^1,2, Garrett E Katz¹, Gregory P Davis¹

¹Department of Computer Science, University of Maryland, College Park, MD, United States.

Frontiers in Robotics and AI

|January 27, 2021

Summary

Researchers developed a humanoid robot using imitation learning and causal reasoning to understand consciousness. Its top-down control of working memory may be a key computational correlate for conscious machines.

Keywords:

artificial consciousness cognitive phenomenology cognitive robots computational explanatory gap imitation learning machine consciousness neural network gating mechanisms working memory

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Last Updated: Nov 19, 2025

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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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Published on: November 24, 2015

Area of Science:

Robotics
Cognitive Science
Artificial Intelligence

Background:

The creation of conscious machines is a significant scientific challenge.
Understanding consciousness requires identifying its computational correlates.

Purpose of the Study:

To explore the development of a conscious machine through humanoid robot research.
To identify computational correlates of consciousness in artificial systems.

Main Methods:

Developing a humanoid robot capable of imitation learning.
Implementing cause-effect reasoning for intention inference.
Focusing on top-down cognitive control of working memory.

Main Results:

The robot learns tasks by imitating human demonstrations, inferring intentions rather than just actions.
Top-down control of working memory, including gating mechanisms, is identified as a potential correlate of consciousness.
Initial results show promise in converting the cognitive system to a neurocomputational form.

Conclusions:

Developing neurocognitive control systems for robots is a viable approach to studying consciousness.
This research offers a credible path toward creating phenomenally conscious machines.