Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results

Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are s...

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Veröffentlicht in:	Frontiers in computational neuroscience Jg. 16; S. 956074
Hauptverfasser:	Zenil, Hector, Marshall, James A. R., Tegnér, Jesper
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Research Foundation 24.01.2023 Frontiers Media S.A
Schlagworte:	Algorithms Animal behavior Animal cognition ant behavior Approximation Artificial intelligence behavioral biases Behavioral sciences behavioral sequences Cognitive ability communication complexity Decision making Entropy Environmental effects Flight Information theory Navigation behavior Neuroscience Prey Probability Shannon Entropy tradeoffs of complexity measures Shannon Entropy ant behavior behavioral sequences communication complexity tradeoffs of complexity measures behavioral biases
ISSN:	1662-5188, 1662-5188
Online-Zugang:	Volltext
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