Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes
Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imita...
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Published in | Neural computation Vol. 32; no. 5; pp. 912 - 968 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.05.2020
MIT Press Journals, The |
Subjects | |
Online Access | Get full text |
ISSN | 0899-7667 1530-888X 1530-888X |
DOI | 10.1162/neco_a_01274 |
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Abstract | Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research. |
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AbstractList | Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research. Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research. |
Author | Yazidi, Anis Hammer, Hugo L. Arntzen, Erik Mofrad, Asieh Abolpour |
Author_xml | – sequence: 1 givenname: Asieh Abolpour surname: Mofrad fullname: Mofrad, Asieh Abolpour email: asieh.abolpour-mofrad@oslomet.no organization: Department of Computer Science, Oslo Metropolitan University, Oslo 0167, Norway asieh.abolpour-mofrad@oslomet.no – sequence: 2 givenname: Anis surname: Yazidi fullname: Yazidi, Anis email: Anis.Yazidi@oslomet.no organization: Department of Computer Science, Oslo Metropolitan University, Oslo 0167, Norway Anis.Yazidi@oslomet.no – sequence: 3 givenname: Hugo L. surname: Hammer fullname: Hammer, Hugo L. email: Hugo.Hammer@oslomet.no organization: Department of Computer Science, Oslo Metropolitan University, Oslo 0167, Norway Hugo.Hammer@oslomet.no – sequence: 4 givenname: Erik surname: Arntzen fullname: Arntzen, Erik email: erik.arntzen@equivalence.net organization: Department of Behavioral Science, Oslo Metropolitan University, Oslo 0167, Norway erik.arntzen@equivalence.net |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32186999$$D View this record in MEDLINE/PubMed |
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References | 2021032415093372100_B70 2021032415093372100_B72 2021032415093372100_B71 2021032415093372100_B74 2021032415093372100_B73 2021032415093372100_B76 2021032415093372100_B75 2021032415093372100_B78 2021032415093372100_B77 2021032415093372100_B79 2021032415093372100_B61 2021032415093372100_B60 2021032415093372100_B63 2021032415093372100_B62 2021032415093372100_B65 2021032415093372100_B64 2021032415093372100_B67 2021032415093372100_B66 2021032415093372100_B69 2021032415093372100_B68 2021032415093372100_B90 2021032415093372100_B92 2021032415093372100_B91 2021032415093372100_B94 2021032415093372100_B93 2021032415093372100_B96 2021032415093372100_B95 2021032415093372100_B10 2021032415093372100_B98 2021032415093372100_B97 2021032415093372100_B12 2021032415093372100_B11 2021032415093372100_B99 2021032415093372100_B14 2021032415093372100_B13 2021032415093372100_B16 2021032415093372100_B15 2021032415093372100_B18 2021032415093372100_B17 2021032415093372100_B19 2021032415093372100_B81 2021032415093372100_B80 2021032415093372100_B83 2021032415093372100_B82 2021032415093372100_B85 2021032415093372100_B84 2021032415093372100_B87 2021032415093372100_B86 2021032415093372100_B89 2021032415093372100_B88 2021032415093372100_B30 2021032415093372100_B32 2021032415093372100_B31 2021032415093372100_B34 2021032415093372100_B33 2021032415093372100_B36 2021032415093372100_B35 2021032415093372100_B38 2021032415093372100_B37 2021032415093372100_B39 2021032415093372100_B3 2021032415093372100_B2 2021032415093372100_B5 2021032415093372100_B4 2021032415093372100_B7 2021032415093372100_B6 2021032415093372100_B9 2021032415093372100_B8 2021032415093372100_B21 2021032415093372100_B20 2021032415093372100_B23 2021032415093372100_B22 2021032415093372100_B25 2021032415093372100_B24 2021032415093372100_B1 2021032415093372100_B27 2021032415093372100_B26 2021032415093372100_B29 2021032415093372100_B28 2021032415093372100_B50 2021032415093372100_B52 2021032415093372100_B51 2021032415093372100_B54 2021032415093372100_B53 2021032415093372100_B56 2021032415093372100_B55 2021032415093372100_B58 2021032415093372100_B57 2021032415093372100_B59 2021032415093372100_B100 2021032415093372100_B41 2021032415093372100_B40 2021032415093372100_B43 2021032415093372100_B104 2021032415093372100_B42 2021032415093372100_B103 2021032415093372100_B45 2021032415093372100_B102 2021032415093372100_B44 2021032415093372100_B101 2021032415093372100_B47 2021032415093372100_B46 2021032415093372100_B107 2021032415093372100_B49 2021032415093372100_B106 2021032415093372100_B48 2021032415093372100_B105 |
References_xml | – ident: 2021032415093372100_B103 doi: 10.1037/h0080017 – ident: 2021032415093372100_B66 doi: 10.1016/S0270-3092(84)80001-6 – ident: 2021032415093372100_B19 – ident: 2021032415093372100_B11 – ident: 2021032415093372100_B86 – ident: 2021032415093372100_B89 doi: 10.1901/jeab.1974.22-261 – ident: 2021032415093372100_B12 doi: 10.1007/BF03395903 – ident: 2021032415093372100_B38 doi: 10.1901/jeab.1987.48-317 – ident: 2021032415093372100_B82 doi: 10.1007/BF03395526 – ident: 2021032415093372100_B31 – ident: 2021032415093372100_B84 doi: 10.1044/jshr.1401.05 – ident: 2021032415093372100_B54 doi: 10.1007/BF03395456 – ident: 2021032415093372100_B1 doi: 10.1080/15021149.2012.11434412 – ident: 2021032415093372100_B75 doi: 10.7551/mitpress/2014.001.0001 – ident: 2021032415093372100_B5 doi: 10.1080/15021149.2011.11434397 – ident: 2021032415093372100_B74 doi: 10.1901/jeab.1991.55-125 – ident: 2021032415093372100_B94 doi: 10.1901/jeab.1996.65-643 – ident: 2021032415093372100_B20 – ident: 2021032415093372100_B67 doi: 10.1016/j.rasd.2012.11.002 – ident: 2021032415093372100_B60 doi: 10.1007/BF03395406 – ident: 2021032415093372100_B36 doi: 10.1007/BF03395907 – ident: 2021032415093372100_B104 doi: 10.1007/s40732-016-0184-1 – ident: 2021032415093372100_B30 doi: 10.1901/jeab.1986.46-243 – ident: 2021032415093372100_B37 doi: 10.1901/jeab.1990.53-345 – ident: 2021032415093372100_B105 doi: 10.1901/jaba.2010.43-615 – ident: 2021032415093372100_B64 doi: 10.1111/j.1756-8765.2008.01003.x – ident: 2021032415093372100_B63 doi: 10.1007/s00354-015-0102-0 – ident: 2021032415093372100_B107 doi: 10.21037/atm.2018.05.32 – ident: 2021032415093372100_B83 – ident: 2021032415093372100_B35 doi: 10.1037/pne0000049 – ident: 2021032415093372100_B46 doi: 10.1007/BF03392865 – ident: 2021032415093372100_B25 doi: 10.1007/BF03395312 – ident: 2021032415093372100_B29 doi: 10.1901/jeab.2007.46-05 – ident: 2021032415093372100_B15 doi: 10.1007/BF03392000 – ident: 2021032415093372100_B40 doi: 10.1002/jaba.234 – ident: 2021032415093372100_B33 doi: 10.1007/BF01386390 – ident: 2021032415093372100_B90 doi: 10.1901/jeab.1982.37-23 – ident: 2021032415093372100_B43 doi: 10.1080/15021149.2009.11434316 – ident: 2021032415093372100_B28 doi: 10.1901/jeab.2009.92-233 – ident: 2021032415093372100_B71 doi: 10.1007/s40614-017-0125-6 – ident: 2021032415093372100_B73 doi: 10.1037/h0058252 – ident: 2021032415093372100_B13 – ident: 2021032415093372100_B27 doi: 10.1007/BF03395144 – ident: 2021032415093372100_B69 doi: 10.1038/nature14236 – ident: 2021032415093372100_B3 doi: 10.1002/bin.301 – ident: 2021032415093372100_B23 doi: 10.1002/bin.1655 – ident: 2021032415093372100_B97 doi: 10.1002/bin.334 – ident: 2021032415093372100_B102 doi: 10.3389/fpsyg.2017.01848 – ident: 2021032415093372100_B61 – ident: 2021032415093372100_B98 – ident: 2021032415093372100_B34 doi: 10.1177/0956797611430961 – ident: 2021032415093372100_B41 doi: 10.1016/0010-0277(88)90031-5 – ident: 2021032415093372100_B39 doi: 10.1901/jaba.2010.43-19 – ident: 2021032415093372100_B8 doi: 10.3389/fnhum.2017.00058 – ident: 2021032415093372100_B16 – ident: 2021032415093372100_B56 doi: 10.1002/jeab.326 – ident: 2021032415093372100_B100 doi: 10.1901/jaba.2010.43-181 – ident: 2021032415093372100_B72 doi: 10.1007/s40732-019-00337-6 – ident: 2021032415093372100_B92 doi: 10.1016/0270-4684(86)90003-0 – ident: 2021032415093372100_B68 doi: 10.1038/s41598-017-14740-y – ident: 2021032415093372100_B78 – ident: 2021032415093372100_B18 doi: 10.1016/j.neuron.2018.10.002 – ident: 2021032415093372100_B95 doi: 10.1111/j.1467-9280.1991.tb00086.x – ident: 2021032415093372100_B6 doi: 10.1007/BF03395227 – ident: 2021032415093372100_B77 doi: 10.1103/PhysRevX.4.031002 – ident: 2021032415093372100_B50 – ident: 2021032415093372100_B106 doi: 10.1109/TAC.2005.844079 – ident: 2021032415093372100_B4 doi: 10.1037/h0101280 – ident: 2021032415093372100_B22 doi: 10.1038/srep00400 – ident: 2021032415093372100_B57 doi: 10.3389/fnhum.2011.00113 – 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Snippet | Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the... |
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StartPage | 912 |
SubjectTerms | Agents (artificial intelligence) Equivalence Letter Machine learning Mathematical models Modelling Neural networks Simulation |
Title | Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes |
URI | https://direct.mit.edu/neco/article/doi/10.1162/neco_a_01274 https://www.ncbi.nlm.nih.gov/pubmed/32186999 https://www.proquest.com/docview/2895737210 https://www.proquest.com/docview/2379023952 |
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