Yanqing Chen
Papers
1
Total Citations
9
H-Index
1
About
Yanqing Chen investigates the computational origins of mental imagery, bridging cognitive science and artificial intelligence. His central research explores how neural networks can develop internal visual representations without direct sensory input, addressing a fundamental question in cognitive neuroscience. Chen's most-cited work (2016) proposes that imagery emerges from learned associations formed through sensory experience, demonstrated through a spiking neural network controlling a robot that learns visual sequences to perform mental rotation tasks. This research extends the theory that imagery arises when perceptual representations are activated in the absence of external stimuli, offering a mechanistic account of how the brain might simulate visual experiences. With 9 citations, this work contributes to understanding the neural basis of imagination and has implications for developing more human-like AI systems. Chen's approach integrates computational modeling with robotics, providing testable hypotheses about how associative learning enables internal visual reasoning. His research sits at the intersection of cognitive modeling, computational neuroscience, and embodied AI, offering insights into how biological and artificial systems might achieve the remarkable capacity for mental imagery.
Research Focus
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Top Papers
- 1