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...
Uloženo v:
| Vydáno v: | Frontiers in computational neuroscience Ročník 16; s. 956074 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Research Foundation
24.01.2023
Frontiers Media S.A |
| Témata: | |
| ISSN: | 1662-5188, 1662-5188 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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 (
), we establish an objective tool to characterize behavioral complexity. Next, we approximate structural (Bennett's Logical Depth) complexity (
) 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. 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. Being able to objectively characterise the intrinsic complexity of behavioural 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 characterise behavioural complexity. Next, we approximate structural (Bennett’s Logical Depth) complexity (LD) to assess the amount of computation required for generating a behavioural string. We apply our toolbox to three landmark studies of animal behaviour 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 behavioural 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’ behaviour 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 randomised items. In summary, our formal toolbox objectively characterises external constraints on putative models of the “internal” decision process in humans and animals. 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.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. 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. |
| Author | Zenil, Hector Tegnér, Jesper Marshall, James A. R. |
| AuthorAffiliation | 4 Complex Systems Modelling Research Group, Department of Computer Science, University of Sheffield , Sheffield , United Kingdom 3 Oxford Immune Algorithmics Ltd. , Oxford , United Kingdom 5 Living Systems Laboratory, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology , Thuwal , Saudi Arabia 2 Kellogg College, University of Oxford , Oxford , United Kingdom 1 Machine Learning Group, Department of Chemical Engineering and Biotechnology, University of Cambridge , Cambridge , United Kingdom |
| AuthorAffiliation_xml | – name: 4 Complex Systems Modelling Research Group, Department of Computer Science, University of Sheffield , Sheffield , United Kingdom – name: 5 Living Systems Laboratory, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology , Thuwal , Saudi Arabia – name: 3 Oxford Immune Algorithmics Ltd. , Oxford , United Kingdom – name: 1 Machine Learning Group, Department of Chemical Engineering and Biotechnology, University of Cambridge , Cambridge , United Kingdom – name: 2 Kellogg College, University of Oxford , Oxford , United Kingdom |
| Author_xml | – sequence: 1 givenname: Hector surname: Zenil fullname: Zenil, Hector – sequence: 2 givenname: James A. R. surname: Marshall fullname: Marshall, James A. R. – sequence: 3 givenname: Jesper surname: Tegnér fullname: Tegnér, Jesper |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36761393$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1Ustu2zAQFIoUzaP9gF4KAb30YpcUX-KlQBD0ESBAL-2ZWFNLm4YkqiRlOH9f2k6KJEBPJJczw1nuXFZnYxixqt5TsmSs1Z_daMOwbEjTLLWQRPFX1QWVslkI2rZnT_bn1WVKW0JkIwV5U50zqSRlml1U2-tpimHvB8g-jKkOroZ-HaLPm8HbGsauTjnONs8R-ro8N_W49_m-3kHvO8hYauvRZ7_DxQo3sPPhAMT9hNEPOOZyiJjmPqe31WsHfcJ3D-tV9fvb1183PxZ3P7_f3lzfLSzXLC-Udo46aFUnnHNaaK4VY51ynDsC1umuQ0daYpEpARaoIs6tQFGkuoPGsqvq9qTbBdiaqdiAeG8CeHMshLg2ELO3PRqtOGpBEYjlvAMA6qRbMRRCNI5jU7S-nLSmeTVgZ0tDpb1nos9vRr8x67AzWhOu5EHg04NADH9mTNkMPlnsexgxzMk0SgnZNFzpAv34AroNcxzLVxWUbFvGiWYF9eGpo39WHkdaAOoEsDGkFNEZ6_NxusWg7w0l5hAecwyPOYTHnMJTmPQF81H8_5y_FmLNnA |
| CitedBy_id | crossref_primary_10_3390_ani13071174 crossref_primary_10_3390_philosophies10020037 |
| Cites_doi | 10.1016/j.cognition.2014.11.038 10.1098/rstb.1988.0065 10.1007/978-0-387-49820-1 10.1080/13506285.2014.950365 10.3390/e20080551 10.1073/pnas.36.1.48 10.1016/j.amc.2011.10.006 10.1016/j.dcn.2013.12.006 10.1214/aos/1016218223 10.3390/e14112173 10.1007/s11023-011-9262-y 10.1103/PhysRevLett.89.068102 10.1038/nrn2787 10.1016/j.cell.2015.01.045 10.2307/25470707 10.1090/S0025-5718-1983-0689479-6 10.1371/journal.pone.0000443 10.1038/s42256-018-0005-0 10.1371/journal.pone.0089948 10.1002/j.1538-7305.1962.tb00480.x 10.1038/4580 10.1163/000579511X568562 10.3390/e11040836 10.3233/COM-13019 10.1007/BF02344866 10.1002/cplx.20388 10.3758/s13428-015-0574-3 10.1145/321495.321506 10.1007/s11229-007-9237-y 10.1016/0168-1591(91)90170-3 10.1016/S0019-9958(64)90223-2 10.7717/peerj-cs.23 10.1371/journal.pone.0096223 10.1007/978-3-319-43784-2_7 10.1371/journal.pcbi.1005408 10.1098/rsos.180399 10.1016/j.cell.2014.08.037 10.1007/978-3-642-27737-5_707-1 10.1111/1365-2656.12562 10.1017/CBO9780511809477 10.1016/j.pnpbp.2012.08.020 10.1016/j.neunet.2021.09.011 10.1186/1752-0509-3-56 10.3390/e22060612 10.3758/s13428-013-0416-0 10.1016/j.physa.2014.02.060 |
| ContentType | Journal Article |
| Copyright | Copyright © 2023 Zenil, Marshall and Tegnér. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2023 Zenil, Marshall and Tegnér. 2023 Zenil, Marshall and Tegnér |
| Copyright_xml | – notice: Copyright © 2023 Zenil, Marshall and Tegnér. – notice: 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Copyright © 2023 Zenil, Marshall and Tegnér. 2023 Zenil, Marshall and Tegnér |
| DBID | AAYXX CITATION NPM 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.3389/fncom.2022.956074 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Local Electronic Collection Information Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | PubMed CrossRef Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology |
| EISSN | 1662-5188 |
| ExternalDocumentID | oai_doaj_org_article_974e951ea0c44daaa1f6fb3e5552f4e2 PMC9904762 36761393 10_3389_fncom_2022_956074 |
| Genre | Journal Article |
| GroupedDBID | --- 29H 2WC 53G 5GY 5VS 88I 8FE 8FH 9T4 AAFWJ AAYXX ABUWG ACGFO ACGFS ADBBV ADMLS ADRAZ AEGXH AENEX AFFHD AFKRA AFPKN AIAGR ALMA_UNASSIGNED_HOLDINGS AOIJS ARCSS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ CCPQU CITATION CS3 DIK DWQXO E3Z F5P GNUQQ GROUPED_DOAJ GX1 HCIFZ HYE KQ8 LK8 M2P M48 M7P M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RNS RPM TR2 ACXDI C1A IAO IEA IHR IPNFZ ISR NPM RIG 3V. 7XB 8FK PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c493t-79ff1fa87d5fff95949733d7f44f0acf9ddef080ce375aca170ffba71e19da2c3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000926396100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1662-5188 |
| IngestDate | Fri Oct 03 12:38:08 EDT 2025 Tue Nov 04 02:07:12 EST 2025 Thu Oct 02 05:39:54 EDT 2025 Fri Jul 25 12:05:02 EDT 2025 Wed Feb 19 02:24:52 EST 2025 Sat Nov 29 03:37:08 EST 2025 Tue Nov 18 22:23:14 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Shannon Entropy ant behavior behavioral sequences communication complexity tradeoffs of complexity measures behavioral biases |
| Language | English |
| License | Copyright © 2023 Zenil, Marshall and Tegnér. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c493t-79ff1fa87d5fff95949733d7f44f0acf9ddef080ce375aca170ffba71e19da2c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Edited by: Sergio Iglesias-Parro, University of Jaén, Spain Reviewed by: Fabíola Keesen, Federal University of Rio de Janeiro, Brazil; Andrew James Jonathan MacIntosh, Kyoto University, Japan |
| OpenAccessLink | https://doaj.org/article/974e951ea0c44daaa1f6fb3e5552f4e2 |
| PMID | 36761393 |
| PQID | 2768834093 |
| PQPubID | 4424409 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_974e951ea0c44daaa1f6fb3e5552f4e2 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9904762 proquest_miscellaneous_2775622479 proquest_journals_2768834093 pubmed_primary_36761393 crossref_citationtrail_10_3389_fncom_2022_956074 crossref_primary_10_3389_fncom_2022_956074 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-24 |
| PublicationDateYYYYMMDD | 2023-01-24 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-24 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Lausanne |
| PublicationTitle | Frontiers in computational neuroscience |
| PublicationTitleAlternate | Front Comput Neurosci |
| PublicationYear | 2023 |
| Publisher | Frontiers Research Foundation Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Research Foundation – name: Frontiers Media S.A |
| References | Reznikova (B34) 2011; 148 Gauvrit (B17); 46 Sabatini (B38) 2000; 38 Soler-Toscano (B55) 2014; 9 Solomonoff (B40) 1964; 7 Rushen (B36) 1991; 28 Tegnér (B41) 2009; 3 Friston (B10) 2010; 11 Wang (B44) 2014; 9 Kahneman (B22) 1982 Zenil (B45) 2017 Gauvrit (B18); 13 Zenil (B53) 2015; 1 Levin (B25) 1974; 10 Costa (B5) 2002; 89 Kolmogorov (B24) 1965; 1 Zenil (B50) 2020 Tononi (B43) 2008; 215 Bennett (B2) 1988 Tervo (B42) 2014; 159 Ha (B19) 2018 Hernández-Orozco (B21) 2018; 5 Gauvrit (B13) 2015; 48 Friedman (B9) 2000; 28 Haroush (B20) 2015; 160 Chaitin (B4) 1969; 16 Friston (B11) 2007; 159 Mullally (B30) 2014; 9 Zenil (B51) 2019; 1 Soler-Toscano (B39) 2013; 2 Auger-Méthé (B1) 2016; 85 Radó (B32) 1962; 41 Zenil (B49) 2018; 20 Delahaye (B6) 2007 Maynard Smith (B29) 1988; 319 Maye (B28) 2007; 2 Rao (B33) 1999; 2 Li (B26) 2008 Kempe (B23); 136 Ryabko (B37) 2009; 11 Zenil (B52); 22 Nash (B31) 1950; 36 Zenil (B46) 2020; 22 Gauvrit (B16) Manor (B27) 2012; 45 Delahaye (B7) 2012; 219 Brady (B3) 1983; 40 Gauvrit (B14) 2014; 22 Zenil (B48); 14 Reznikova (B35) 2012 Zenil (B47); 1 Gauvrit (B15); 22 Fahlman (B8) 1983 Zenil (B54) 2014; 404 Friston (B12) 2021; 144 |
| References_xml | – volume: 136 start-page: 247 ident: B23 article-title: Structure emerges faster during cultural transmission in children than in adults. publication-title: Cognition doi: 10.1016/j.cognition.2014.11.038 – volume: 319 start-page: 557 year: 1988 ident: B29 article-title: The evolution of aggression: Can selection generate variability? publication-title: Philos. Trans. R. Soc. Lond. B Biol. Sci. doi: 10.1098/rstb.1988.0065 – year: 1983 ident: B8 article-title: Massively parallel architectures for A.I.: Netl, thistle, and boltzmann machines publication-title: Proceedings of the National Conference on Artificial Intelligence – year: 2008 ident: B26 publication-title: An Introduction to kolmogorov complexity and its applications. doi: 10.1007/978-0-387-49820-1 – volume: 22 start-page: 1084 year: 2014 ident: B14 article-title: Natural scene statistics mediate the perception of image complexity. publication-title: Vis. Cogn. doi: 10.1080/13506285.2014.950365 – volume: 20 year: 2018 ident: B49 article-title: A review of graph and network complexity from an algorithmic information perspective. publication-title: Entropy doi: 10.3390/e20080551 – volume: 36 start-page: 48 year: 1950 ident: B31 article-title: Equilibrium points in n-person games. publication-title: Proc. Natl. Acad. Sci. U.S.A. doi: 10.1073/pnas.36.1.48 – volume: 219 start-page: 63 year: 2012 ident: B7 article-title: Numerical evaluation of algorithmic complexity for short strings: A glance into the innermost structure of randomness. publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2011.10.006 – volume: 9 start-page: 12 year: 2014 ident: B30 article-title: Learning to remember: The early ontogeny of episodic memory publication-title: Dev. Cogn. Neurosci. doi: 10.1016/j.dcn.2013.12.006 – volume: 28 start-page: 337 year: 2000 ident: B9 article-title: Additive logistic regression: A statistical view of boosting (with discussion and a rejoinder by the authors). publication-title: Ann. Stat. doi: 10.1214/aos/1016218223 – volume: 14 start-page: 2173 ident: B48 article-title: Life as thermodynamic evidence of algorithmic structure in natural environments. publication-title: Entropy doi: 10.3390/e14112173 – volume: 22 start-page: 149 ident: B52 article-title: Empirical encounters with computational irreducibility and unpredictability. publication-title: Minds Mach. doi: 10.1007/s11023-011-9262-y – volume: 89 year: 2002 ident: B5 article-title: Multiscale entropy analysis of complex physiologic time series. publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.89.068102 – volume: 11 start-page: 127 year: 2010 ident: B10 article-title: The free-energy principle: A unified brain theory? publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2787 – volume: 160 start-page: 1233 year: 2015 ident: B20 article-title: Neuronal prediction of opponent’s behavior during cooperative social interchange in primates. publication-title: Cell doi: 10.1016/j.cell.2015.01.045 – volume: 215 start-page: 216 year: 2008 ident: B43 article-title: Consciousness as integrated information: A provisional manifesto. publication-title: Biol. Bullet. doi: 10.2307/25470707 – volume: 40 start-page: 647 year: 1983 ident: B3 article-title: The determination of the value of Rado’s noncomputable function Sigma(k) for four-state Turing machines. publication-title: Math. Comput. doi: 10.1090/S0025-5718-1983-0689479-6 – volume: 2 year: 2007 ident: B28 article-title: Order in spontaneous behavior. publication-title: PLoS One doi: 10.1371/journal.pone.0000443 – volume: 1 start-page: 58 year: 2019 ident: B51 article-title: Causal deconvolution by algorithmic generative models. publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-018-0005-0 – volume: 9 year: 2014 ident: B44 article-title: Brain entropy mapping using fMRI. publication-title: PLoS One doi: 10.1371/journal.pone.0089948 – volume: 10 start-page: 206 year: 1974 ident: B25 article-title: Laws of information conservation (non-growth) and aspects of the foundation of probability theory, Problems in Form. publication-title: Transmission – year: 2020 ident: B50 publication-title: Methods and applications of algorithmic complexity – volume: 41 start-page: 877 year: 1962 ident: B32 article-title: On non-computable functions. publication-title: Bell Syst. Tech. J. doi: 10.1002/j.1538-7305.1962.tb00480.x – volume: 2 start-page: 79 year: 1999 ident: B33 article-title: Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. publication-title: Nat. Neurosci. doi: 10.1038/4580 – year: 2012 ident: B35 article-title: Ants and Bits publication-title: IEEE information theory society, newsletter – year: 2007 ident: B6 article-title: On the Kolmogorov-Chaitin complexity for short sequences publication-title: Randomness and Complexity – volume: 148 start-page: 405 year: 2011 ident: B34 article-title: Numerical competence in animals, with an insight from ants. publication-title: Behaviour doi: 10.1163/000579511X568562 – volume: 11 start-page: 836 year: 2009 ident: B37 article-title: The use of ideas of information theory for studying “Language” and intelligence in ants. publication-title: Entropy doi: 10.3390/e11040836 – volume: 2 start-page: 125 year: 2013 ident: B39 article-title: Correspondence and independence of numerical evaluations of algorithmic information measures. publication-title: Computability doi: 10.3233/COM-13019 – volume: 38 start-page: 617 year: 2000 ident: B38 article-title: Analysis of postural sway using entropy measures of signal complexity. publication-title: Med. Biol. Eng. Comput. doi: 10.1007/BF02344866 – start-page: 227 year: 1988 ident: B2 article-title: Logical depth and physical complexity publication-title: The universal turing machine–a half-century survey – volume: 1 start-page: 26 ident: B47 article-title: Image information content characterization and classification by physical complexity. publication-title: Complexity doi: 10.1002/cplx.20388 – volume: 48 start-page: 314 year: 2015 ident: B13 article-title: Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method. publication-title: Behav. Res. Methods doi: 10.3758/s13428-015-0574-3 – volume: 16 start-page: 145 year: 1969 ident: B4 article-title: On the length of programs for computing finite binary sequences: Statistical considerations. publication-title: J. ACM doi: 10.1145/321495.321506 – volume: 159 start-page: 417 year: 2007 ident: B11 article-title: Free energy and the brain. publication-title: Synthese doi: 10.1007/s11229-007-9237-y – volume: 28 start-page: 381 year: 1991 ident: B36 article-title: Problems associated with the interpretation of physiological data in the assessment of animal welfare. publication-title: Appl. Anim. Behav. Sci. doi: 10.1016/0168-1591(91)90170-3 – volume: 7 start-page: 1 year: 1964 ident: B40 article-title: A formal theory of inductive inference: Parts 1 and 2. publication-title: Inform. Control doi: 10.1016/S0019-9958(64)90223-2 – volume: 1 year: 2015 ident: B53 article-title: Two-dimensional Kolmogorov complexity and validation of the coding theorem method by compressibility. publication-title: PeerJ Comput. Sci. doi: 10.7717/peerj-cs.23 – volume: 9 year: 2014 ident: B55 article-title: Calculating Kolmogorov complexity from the output frequency distributions of small turing machines publication-title: PLoS One doi: 10.1371/journal.pone.0096223 – volume: 22 start-page: 1084 ident: B15 article-title: Natural scene statistics mediate the perception of image complexity. publication-title: Vis. Cogn. doi: 10.1080/13506285.2014.950365 – start-page: 117 ident: B16 article-title: The Information-theoretic and algorithmic approach to human, animal and artificial cognition, forthcoming publication-title: Representation and reality: Humans, animals and machines doi: 10.1007/978-3-319-43784-2_7 – volume: 13 ident: B18 article-title: Human behavioral complexity peaks at age 25. publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1005408 – volume: 5 year: 2018 ident: B21 article-title: Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity. publication-title: R. Soc. Open Sci. doi: 10.1098/rsos.180399 – volume: 159 start-page: 21 year: 2014 ident: B42 article-title: Behavioral variability through stochastic choice and its gating by anterior cingulate cortex. publication-title: Cell doi: 10.1016/j.cell.2014.08.037 – year: 2018 ident: B19 article-title: World models. publication-title: arXiv – year: 2017 ident: B45 article-title: Cognition and the algorithmic nature of the mind publication-title: Encyclopedia of complexity and systems science doi: 10.1007/978-3-642-27737-5_707-1 – volume: 85 start-page: 1411 year: 2016 ident: B1 article-title: Evaluating random search strategies in three mammals from distinct feeding guilds. publication-title: J. Anim. Ecol. doi: 10.1111/1365-2656.12562 – year: 1982 ident: B22 publication-title: Judgment under uncertainty: Heuristics and biases. doi: 10.1017/CBO9780511809477 – volume: 45 start-page: 287 year: 2012 ident: B27 article-title: Physiologic complexity and aging: Implications for physical function and rehabilitation. publication-title: Prog. Neuro Psychopharmacol. Biol. Psychiatry doi: 10.1016/j.pnpbp.2012.08.020 – volume: 1 start-page: 1 year: 1965 ident: B24 article-title: Three approaches to the quantitative definition of information. publication-title: Problems Inform. Transm. – volume: 144 start-page: 573 year: 2021 ident: B12 article-title: World model learning and inference. publication-title: Neural Netw. doi: 10.1016/j.neunet.2021.09.011 – volume: 3 year: 2009 ident: B41 article-title: Computational disease modeling – fact or fiction? publication-title: BMC Syst. Biol. doi: 10.1186/1752-0509-3-56 – volume: 22 year: 2020 ident: B46 article-title: A review of methods for estimating algorithmic complexity: Options. challenges, and new directions. publication-title: Entropy doi: 10.3390/e22060612 – volume: 46 start-page: 732 ident: B17 article-title: Algorithmic complexity for short binary strings applied to psychology: A primer. publication-title: Behav. Res. Methods doi: 10.3758/s13428-013-0416-0 – volume: 404 start-page: 341 year: 2014 ident: B54 article-title: Correlation of automorphism group size and topological properties with program-size complexity evaluations of graphs and complex networks. publication-title: Phys. A Stat. Mech. Appl. doi: 10.1016/j.physa.2014.02.060 |
| SSID | ssj0062650 |
| Score | 2.302166 |
| Snippet | Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving... Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 956074 |
| SubjectTerms | 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 |
| SummonAdditionalLinks | – databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagcODCqzwWCjIS4oAUmvixjk9oQVQcUNUDSL1Zju1pU22T0mxR-feMHW_aRagXrokdOZmx_X3jyTeEvK0ZVIo3VRG0Z0hQJBRNxeaF4KphjQq8bpLO7De1v18fHuqDHHAbclrlek1MC7XvXYyR7zLExTVHNsI_nv0sYtWoeLqaS2jcJneiSgJPqXsH65UYsbrMJ5lIxPQudDFBBNk--xBZgRIbe1GS7P8Xzvw7XfLa_rP34H9H_pDcz8iTLkZXeURuhe4x2V50yLpPf9N3NOWCpiD7NjlZRKnxy3b8r3GgPVC7PMKHro5PW0dt5-koPBtFO2hKSw-XiOcp-m0bYwh0SksqroQA6PVyAhR5_sVyNTwhP_a-fP_8tchVGQonNF8VSgNUYGvlJQBoqYVWnHsFQkBpHWhcMAFxqAtcSetspUqAxqoqVNpb5vhTstX1XXhOqLDSWwF1CMiLgNV2zlTptfQIirTVekbKtX2My5LlsXLG0iB1iSY1yaQmmtSMJp2R91OXs1Gv46bGn6LRp4ZRajtd6M-PTJ65BglXQBgabOmE8NbaCubQ8CClZCACm5GdtdlNnv-DubL5jLyZbuPMjccxtgv9RWyjEHwyofA9n40eNo0k6ughNsfeasP3Noa6eadrj5M6OMILgTvci5uH9ZLcww8RU-kKJnbIFvpMeEXuul-rdjh_nabRHxneLV0 priority: 102 providerName: ProQuest |
| Title | Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36761393 https://www.proquest.com/docview/2768834093 https://www.proquest.com/docview/2775622479 https://pubmed.ncbi.nlm.nih.gov/PMC9904762 https://doaj.org/article/974e951ea0c44daaa1f6fb3e5552f4e2 |
| Volume | 16 |
| WOSCitedRecordID | wos000926396100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: DOA dateStart: 20070101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M~E dateStart: 20070101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M7P dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: BENPR dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: PIMPY dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1662-5188 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062650 issn: 1662-5188 databaseCode: M2P dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Pb9MwFLZg48AFAeNHYVRGQhyQwhL_qONjhzaBxKoIgVROluP4saAuRWuHxn_Ps52GFiG4cPEhtiXb79n-vvj5MyEvSgaF4nWRed0wJCgSsrpgk0xwVbNaeV7WUWf2vZrNyvlcV1tPfYWYsCQPnAbuCPGuRxTgbe6EaKy1BUyg5l5KyUD4uPoi6tmQqbQGI0qX_RkmUjB9BF0IDUGez14HPqDEzi4Uxfr_hDB_D5Tc2nlO75I7PWSk09TUe-SG7-6Tg2mHdPniB31JYxBn_Dt-QL5Og0b4dZsuJK7oEqhdfFletuvzi9ZR2zU0KcYGtQ0a48n9NQJxig7XBvJPh3ii7NcNfrr9DgBFgn61WK8ekE-nJx_fvM365xQyJzRfZ0oDFGBL1UgA0FILrThvFAgBuXWgcaUDHErnuZLW2ULlALVVhS90Y5njD8let-z8Y0KFlY0VUHqPhAZYaSdM5Y2WDaIZbbUekXwzvMb1WuPhyYuFQc4RLGKiRUywiEkWGZFXQ5VvSWjjb4WPg82GgkEjO35AzzG955h_ec6IHG4sbvqJuzIM6VfJkfTyEXk-ZOOUC-cotvPLq1BGIWpkQmE_HyUHGVoSBPAQVGNtteM6O03dzena8yjrjbhA4Nb05H_07Sm5jcMVIuUyJg7JHnqWf0Zuue_rdnU5JjfVvByT_eOTWfVhHGcOpmesCqnCdL96d1Z9_gns9yYX |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQYILrwJdKGAk4IAUmtjOOj4gtDyqVl1WPRSpt9SJPW2qbVI2W2j_FL-RsfNoF6HeeuCaOJHtfDOez558Q8jrhEEkeRYFVhmGBCWGIIvYMBBcZiyTlieZ15kdy8kk2dtTO0vkd_cvjEur7Hyid9Smyt0e-TrDuDjhyEb4x5Mfgasa5U5XuxIaDSy27fkvpGz1h60v-H3fMLbxdffzZtBWFQhyofg8kAogAp1IEwOAipVQknMjQQgIdQ4KDR4wjsotl7HOdSRDgEzLyEbKaJZzfO8NchPDCJb4VMGdzvMjN4jbk1MkfmodSpeQwnCVfO9YiBQLa58vEfCvuPbv9MxL693Gvf9tpu6Tu21kTUeNKTwgS7Z8SFZGpZ5Xx-f0LfW5rv4QYYUcjZyU-lnR_LdZ0wqonh7gIOaHx0VOdWloI6zrREmoT7u3Z8hXKNpl4fZIaJ92FVwIHdDL5RLozNan03n9iHy_llE_JstlVdpVQoWOjRaQWIu8D1iih0yGRsUGgz6llRqQsMNDmreS7K4yyDRFauYglHoIpQ5CaQOhAXnXP3LS6JFc1fiTA1nf0EmJ-wvV7CBtPVOKhNJimG11mAthtNYRDCHjNo5jBsKyAVnrYJa2_q1OLzA2IK_62-iZ3HGTLm116tpIDK6ZkDjOJw2i-544nUDkHvi0XMD6QlcX75TFoVc_x_BJ4Ar-9OpuvSS3N3e_jdPx1mT7GbmDk-LSBgMm1sgy4sc-J7fyn_Oinr3wJkzJ_nVbwh8phI5K |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLVKQYgNr_IYKGAkYIGUTmI743iB0EAZUbUazQKk7oIT-7ZB06RMptD-Gl_HtfNoB6HuumCbOJGdnPs48c25hLxKGESSZ1FglWFIUGIIsoiNAsFlxjJpeZJ5ndk9OZ0m-_tqtkZ-d__CuLLKzid6R22q3H0jHzLMixOObIQPoS2LmG1P3h__CFwHKbfT2rXTaCCya89-IX2r3-1s47t-zdjk05ePn4O2w0CQC8WXgVQAEehEmhgAVKyEkpwbCUJAqHNQaPyAOVVuuYx1riMZAmRaRjZSRrOc432vkevSiZb7ssFZFwWQJ8TtLiqSQDWE0hWnMIyYW46RSLESB327gH_luH-Xal6IfZM7__NTu0tutxk3HTcmco-s2fI-2RiXelkdndE31NfA-s2FDfJ97CTWT4vmf86aVkD1_AAXsTw8KnKqS0MbwV0nVkJ9Ob49RR5D0V4L9-2E9uVYwbkAAr3YRoEubH0yX9YPyNcrWfVDsl5WpX1MqNCx0QISa5EPAkv0iMnQqNhgMqi0UgMSdthI81aq3XUMmadI2RycUg-n1MEpbeA0IG_7S44bnZLLBn9wgOsHOolxf6BaHKStx0qRaFpMv60OcyGM1jqCEWTcxnHMQFg2IJsd5NLW79XpOd4G5GV_Gj2W24bSpa1O3BiJSTcTEtf5qEF3PxOnH4icBK-WK7hfmerqmbI49KromFYJjOxPLp_WC3ITDSDd25nuPiW38Jm4asKAiU2yjvCxz8iN_OeyqBfPvTVT8u2qDeEPINGXBw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Approximations+of+algorithmic+and+structural+complexity+validate+cognitive-behavioral+experimental+results&rft.jtitle=Frontiers+in+computational+neuroscience&rft.au=Zenil%2C+Hector&rft.au=Marshall%2C+James+A+R&rft.au=Tegn%C3%A9r%2C+Jesper&rft.date=2023-01-24&rft.issn=1662-5188&rft.eissn=1662-5188&rft.volume=16&rft.spage=956074&rft_id=info:doi/10.3389%2Ffncom.2022.956074&rft_id=info%3Apmid%2F36761393&rft.externalDocID=36761393 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1662-5188&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1662-5188&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1662-5188&client=summon |