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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.956074/full |
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| author | Hector Zenil Hector Zenil Hector Zenil James A. R. Marshall Jesper Tegnér |
| author_facet | Hector Zenil Hector Zenil Hector Zenil James A. R. Marshall Jesper Tegnér |
| author_sort | Hector Zenil |
| collection | DOAJ |
| description | 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 short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett’s Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats’ behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the “internal” decision process in humans and animals. |
| first_indexed | 2024-04-10T20:46:04Z |
| format | Article |
| id | doaj.art-974e951ea0c44daaa1f6fb3e5552f4e2 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-10T20:46:04Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-974e951ea0c44daaa1f6fb3e5552f4e22023-01-24T07:44:50ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-01-011610.3389/fncom.2022.956074956074Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental resultsHector Zenil0Hector Zenil1Hector Zenil2James A. R. Marshall3Jesper Tegnér4Machine Learning Group, Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United KingdomKellogg College, University of Oxford, Oxford, United KingdomOxford Immune Algorithmics Ltd., Oxford, United KingdomComplex Systems Modelling Research Group, Department of Computer Science, University of Sheffield, Sheffield, United KingdomLiving Systems Laboratory, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaBeing 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 short. By numerically approximating algorithmic (Kolmogorov) complexity (K), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett’s Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioral string. We apply our toolbox to three landmark studies of animal behavior of increasing sophistication and degree of environmental influence, including studies of foraging communication by ants, flight patterns of fruit flies, and tactical deception and competition (e.g., predator-prey) strategies. We find that ants harness the environmental condition in their internal decision process, modulating their behavioral complexity accordingly. Our analysis of flight (fruit flies) invalidated the common hypothesis that animals navigating in an environment devoid of stimuli adopt a random strategy. Fruit flies exposed to a featureless environment deviated the most from Levy flight, suggesting an algorithmic bias in their attempt to devise a useful (navigation) strategy. Similarly, a logical depth analysis of rats revealed that the structural complexity of the rat always ends up matching the structural complexity of the competitor, with the rats’ behavior simulating algorithmic randomness. Finally, we discuss how experiments on how humans perceive randomness suggest the existence of an algorithmic bias in our reasoning and decision processes, in line with our analysis of the animal experiments. This contrasts with the view of the mind as performing faulty computations when presented with randomized items. In summary, our formal toolbox objectively characterizes external constraints on putative models of the “internal” decision process in humans and animals.https://www.frontiersin.org/articles/10.3389/fncom.2022.956074/fullbehavioral biasesant behaviorbehavioral sequencescommunication complexitytradeoffs of complexity measuresShannon Entropy |
| spellingShingle | Hector Zenil Hector Zenil Hector Zenil James A. R. Marshall Jesper Tegnér Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results Frontiers in Computational Neuroscience behavioral biases ant behavior behavioral sequences communication complexity tradeoffs of complexity measures Shannon Entropy |
| title | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| title_full | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| title_fullStr | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| title_full_unstemmed | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| title_short | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| title_sort | approximations of algorithmic and structural complexity validate cognitive behavioral experimental results |
| topic | behavioral biases ant behavior behavioral sequences communication complexity tradeoffs of complexity measures Shannon Entropy |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.956074/full |
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