Computational neuroscience attempts to build models of the brain that break cognition into basic elements. Here we study time perception in artificial brains, evolved over thousands of generations to judge the duration of tones, and compare the evolved brains’ behavioral characteristics to human subjects performing the same task. We observe substantial similarities in psychometric properties in human subjects and digital brains with very similar perception artifacts, but also see differences due to different selective pressures during training or evolution. Our findings suggests that digital experimentation using brains evolved within a computer can advance computational cognitive neuroscience by discovering new cognitive mechanisms and heuristics.

Issue Section:
Special Session: Artificial Perception: Machines with Lifelike Failings
artificial cognition, digital evolution, attentional entrainment, artificial intelligence
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© 2021 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2021
Massachusetts Institute of Technology
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Issue Section:
Special Session: Artificial Perception: Machines with Lifelike Failings