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 inNeural computation Vol. 32; no. 5; pp. 912 - 968
Main Authors Mofrad, Asieh Abolpour, Yazidi, Anis, Hammer, Hugo L., Arntzen, Erik
Format Journal Article
LanguageEnglish
Published One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.05.2020
MIT Press Journals, The
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Online AccessGet full text
ISSN0899-7667
1530-888X
1530-888X
DOI10.1162/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.
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
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Cites_doi 10.1037/h0080017
10.1016/S0270-3092(84)80001-6
10.1901/jeab.1974.22-261
10.1007/BF03395903
10.1901/jeab.1987.48-317
10.1007/BF03395526
10.1044/jshr.1401.05
10.1007/BF03395456
10.1080/15021149.2012.11434412
10.7551/mitpress/2014.001.0001
10.1080/15021149.2011.11434397
10.1901/jeab.1991.55-125
10.1901/jeab.1996.65-643
10.1016/j.rasd.2012.11.002
10.1007/BF03395406
10.1007/BF03395907
10.1007/s40732-016-0184-1
10.1901/jeab.1986.46-243
10.1901/jeab.1990.53-345
10.1901/jaba.2010.43-615
10.1111/j.1756-8765.2008.01003.x
10.1007/s00354-015-0102-0
10.21037/atm.2018.05.32
10.1037/pne0000049
10.1007/BF03392865
10.1007/BF03395312
10.1901/jeab.2007.46-05
10.1007/BF03392000
10.1002/jaba.234
10.1007/BF01386390
10.1901/jeab.1982.37-23
10.1080/15021149.2009.11434316
10.1901/jeab.2009.92-233
10.1007/s40614-017-0125-6
10.1037/h0058252
10.1007/BF03395144
10.1038/nature14236
10.1002/bin.301
10.1002/bin.1655
10.1002/bin.334
10.3389/fpsyg.2017.01848
10.1177/0956797611430961
10.1016/0010-0277(88)90031-5
10.1901/jaba.2010.43-19
10.3389/fnhum.2017.00058
10.1002/jeab.326
10.1901/jaba.2010.43-181
10.1007/s40732-019-00337-6
10.1016/0270-4684(86)90003-0
10.1038/s41598-017-14740-y
10.1016/j.neuron.2018.10.002
10.1111/j.1467-9280.1991.tb00086.x
10.1007/BF03395227
10.1103/PhysRevX.4.031002
10.1109/TAC.2005.844079
10.1037/h0101280
10.1038/srep00400
10.3389/fnhum.2011.00113
10.11133/j.tpr.2013.63.1.005
10.1007/BF03393066
10.7551/mitpress/5237.001.0001
10.1901/jeab.1989.51-29
10.1007/BF03395091
10.1097/00001756-200102120-00043
10.1016/j.jcbs.2017.01.002
10.1901/jeab.1993.60-529
10.1007/BF03395720
10.1901/jaba.2010.43-131
10.3758/BF03196197
10.1007/BF03395833
10.1901/jeab.1991.56-519
10.1037/h0061626
10.1038/nature16961
10.7551/mitpress/9780262016353.001.0001
10.1038/s42256-019-0048-x
10.4324/9781315640099
10.1093/brain/124.4.647
10.1162/neco.1997.9.8.1735
10.1037/h0100204
10.1901/jaba.2011.44-819
10.1002/jeab.64
10.1002/bin.1526
10.1016/j.bbr.2014.11.031
10.1080/15021149.2013.11434452
10.1901/jeab.1989.51-385
10.1097/WNN.0b013e318192ccf0
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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
– ident: 2021032415093372100_B44
  doi: 10.11133/j.tpr.2013.63.1.005
– ident: 2021032415093372100_B49
– ident: 2021032415093372100_B10
– ident: 2021032415093372100_B87
  doi: 10.1007/BF03393066
– ident: 2021032415093372100_B65
  doi: 10.7551/mitpress/5237.001.0001
– ident: 2021032415093372100_B55
– ident: 2021032415093372100_B24
  doi: 10.1901/jeab.1989.51-29
– ident: 2021032415093372100_B14
  doi: 10.1007/BF03395091
– ident: 2021032415093372100_B7
– ident: 2021032415093372100_B32
  doi: 10.1097/00001756-200102120-00043
– ident: 2021032415093372100_B52
  doi: 10.1016/j.jcbs.2017.01.002
– ident: 2021032415093372100_B62
  doi: 10.1901/jeab.1993.60-529
– ident: 2021032415093372100_B2
  doi: 10.1007/BF03395720
– ident: 2021032415093372100_B9
– ident: 2021032415093372100_B17
– ident: 2021032415093372100_B45
  doi: 10.1901/jaba.2010.43-131
– ident: 2021032415093372100_B42
  doi: 10.3758/BF03196197
– ident: 2021032415093372100_B101
  doi: 10.1007/BF03395833
– ident: 2021032415093372100_B96
  doi: 10.1901/jeab.1991.56-519
– ident: 2021032415093372100_B99
  doi: 10.1037/h0061626
– ident: 2021032415093372100_B93
  doi: 10.1038/nature16961
– ident: 2021032415093372100_B79
– ident: 2021032415093372100_B47
  doi: 10.7551/mitpress/9780262016353.001.0001
– ident: 2021032415093372100_B81
  doi: 10.1038/s42256-019-0048-x
– ident: 2021032415093372100_B26
  doi: 10.4324/9781315640099
– ident: 2021032415093372100_B70
  doi: 10.1093/brain/124.4.647
– ident: 2021032415093372100_B53
  doi: 10.1162/neco.1997.9.8.1735
– ident: 2021032415093372100_B59
  doi: 10.1037/h0100204
– ident: 2021032415093372100_B58
  doi: 10.1901/jaba.2011.44-819
– ident: 2021032415093372100_B91
– ident: 2021032415093372100_B51
  doi: 10.1002/jeab.64
– ident: 2021032415093372100_B76
  doi: 10.1002/bin.1526
– ident: 2021032415093372100_B80
  doi: 10.1016/j.bbr.2014.11.031
– ident: 2021032415093372100_B85
– ident: 2021032415093372100_B88
  doi: 10.1080/15021149.2013.11434452
– ident: 2021032415093372100_B48
  doi: 10.1901/jeab.1989.51-385
– ident: 2021032415093372100_B21
  doi: 10.1097/WNN.0b013e318192ccf0
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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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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
Volume 32
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