Decision theory, reinforcement learning, and the brain
Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. He...
Saved in:
| Published in | Cognitive, affective, & behavioral neuroscience Vol. 8; no. 4; pp. 429 - 453 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer-Verlag
01.12.2008
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1530-7026 1531-135X 1531-135X |
| DOI | 10.3758/CABN.8.4.429 |
Cover
| Abstract | Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making. |
|---|---|
| AbstractList | Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making. Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making. |
| Author | Dayan, Peter Daw, Nathaniel D. |
| Author_xml | – sequence: 1 givenname: Peter surname: Dayan fullname: Dayan, Peter email: dayan@gatsby.ucl.ac.uk organization: Gatsby Computational Neuroscience Unit, University College London – sequence: 2 givenname: Nathaniel D. surname: Daw fullname: Daw, Nathaniel D. email: nathaniel.daw@nyu.edu organization: Center for Neural Science, Department of Psychology and Center for Neuroeconomics, New York University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19033240$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkTtPwzAUhS1URB-wMaOIgakpjh-JM5bylCpYQGKzHOe6pEqdYidC_fcktKKoYroevnvuOcdD1LOVBYTOIzyhCRfXs-nN80RM2ISR9AgNIk6jMKL8vffzxmGCSdxHQ--XGGNGGDlB_SjFlBKGByi-BV34orJB_QGV24wDB4U1ldOwAlsHJShnC7sYB8rmHRNkThX2FB0bVXo4280Reru_e509hvOXh6fZdB5qxkUdGkq4ylXKGGSANaYEDIuEplwoxWIBJhMmETrVCc8MkNgoDkmcUyA8z_KEjlC41W3sWm2-VFnKtStWym1khGWXX2qVWSkkk23-lr_a8mtXfTbga7kqvIayVBaqxss4FTQhpBO-PACXVeNsm0USzBhnPO7ULnZQk60g35_e1dcCZAtoV3nvwEhd1Kpu66zblspfj90f_fE4Plg6jHSA7yrwLWYX4PZO_-W_Adykn28 |
| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2018_05_074 crossref_primary_10_1371_journal_pone_0160245 crossref_primary_10_1007_s00221_024_06805_y crossref_primary_10_1007_s12559_017_9511_3 crossref_primary_10_1016_j_cobeha_2020_11_002 crossref_primary_10_1523_JNEUROSCI_4458_09_2010 crossref_primary_10_3389_fpsyt_2017_00001 crossref_primary_10_1016_j_neuroimage_2012_04_024 crossref_primary_10_1186_1471_2202_14_S1_P288 crossref_primary_10_1016_j_tics_2020_06_011 crossref_primary_10_1371_journal_pcbi_1002918 crossref_primary_10_1097_PSY_0000000000000640 crossref_primary_10_1371_journal_pcbi_1005503 crossref_primary_10_1007_s00422_012_0512_8 crossref_primary_10_1371_journal_pcbi_1002235 crossref_primary_10_3389_fnhum_2022_938501 crossref_primary_10_1016_j_brainres_2011_05_053 crossref_primary_10_1016_j_isci_2024_111182 crossref_primary_10_1080_15427560_2016_1238372 crossref_primary_10_1016_j_neubiorev_2020_01_025 crossref_primary_10_1017_S003329171700174X crossref_primary_10_1073_pnas_1613844114 crossref_primary_10_1109_ACCESS_2020_3027355 crossref_primary_10_1002_eat_24156 crossref_primary_10_1111_cogs_12910 crossref_primary_10_7554_eLife_50469 crossref_primary_10_1038_s42003_020_01438_7 crossref_primary_10_3390_s21237931 crossref_primary_10_1016_j_celrep_2023_112931 crossref_primary_10_1016_j_cortex_2020_02_024 crossref_primary_10_1016_j_biopsych_2020_02_017 crossref_primary_10_1038_ncomms16033 crossref_primary_10_1016_j_cognition_2011_10_009 crossref_primary_10_7554_eLife_66917 crossref_primary_10_1016_j_sapharm_2011_06_004 crossref_primary_10_1016_j_anbehav_2018_08_011 crossref_primary_10_1016_j_tics_2022_09_003 crossref_primary_10_1016_j_tics_2011_11_018 crossref_primary_10_1371_journal_pone_0018539 crossref_primary_10_1016_j_neuroimage_2020_117212 crossref_primary_10_1146_annurev_devpsych_121318_085229 crossref_primary_10_1016_j_cogsys_2018_07_005 crossref_primary_10_3389_fncom_2017_00029 crossref_primary_10_1007_s00213_009_1735_9 crossref_primary_10_1016_j_beproc_2019_05_009 crossref_primary_10_1523_JNEUROSCI_4389_13_2014 crossref_primary_10_1016_j_neuron_2019_05_042 crossref_primary_10_1371_journal_pcbi_1005768 crossref_primary_10_1016_j_neuron_2020_01_036 crossref_primary_10_1371_journal_pcbi_1004442 crossref_primary_10_3389_fpsyg_2021_619855 crossref_primary_10_1109_ACCESS_2019_2922150 crossref_primary_10_1038_s41593_024_01711_6 crossref_primary_10_1523_JNEUROSCI_0989_14_2014 crossref_primary_10_2139_ssrn_4152649 crossref_primary_10_3389_fpsyg_2015_01948 crossref_primary_10_1016_S2215_0366_15_00360_0 crossref_primary_10_1038_srep01048 crossref_primary_10_1016_j_neuron_2018_03_042 crossref_primary_10_1371_journal_pcbi_1007265 crossref_primary_10_1007_s10614_022_10249_3 crossref_primary_10_1073_pnas_2113961119 crossref_primary_10_1080_13546805_2014_932685 crossref_primary_10_1098_rstb_2013_0481 crossref_primary_10_1126_sciadv_abh2059 crossref_primary_10_1016_j_neuroscience_2016_07_025 crossref_primary_10_1073_pnas_1205131109 crossref_primary_10_3389_fpsyg_2017_00312 crossref_primary_10_1016_j_cognition_2009_07_005 crossref_primary_10_1111_cogs_13088 crossref_primary_10_3758_s13415_014_0277_8 crossref_primary_10_1371_journal_pone_0053344 crossref_primary_10_1007_s11571_017_9458_9 crossref_primary_10_1016_j_tins_2020_09_004 crossref_primary_10_1038_nrn_2016_56 crossref_primary_10_1111_nyas_14524 crossref_primary_10_3758_s13423_021_01986_x crossref_primary_10_1038_s41467_023_42813_2 crossref_primary_10_7554_eLife_82531 crossref_primary_10_1002_aaai_12116 crossref_primary_10_1371_journal_pone_0239616 crossref_primary_10_1038_s41562_021_01213_6 crossref_primary_10_1080_0144929X_2024_2329616 crossref_primary_10_7554_eLife_63055 crossref_primary_10_1523_JNEUROSCI_2201_12_2013 crossref_primary_10_3389_fnbot_2023_1298176 crossref_primary_10_1016_j_neubiorev_2011_04_011 crossref_primary_10_1038_s41467_024_51393_8 crossref_primary_10_1038_s41467_018_08121_w crossref_primary_10_1080_1350178X_2015_1024883 crossref_primary_10_1073_pnas_1906662116 crossref_primary_10_1186_1744_9081_9_3 crossref_primary_10_1001_jamapsychiatry_2022_0051 crossref_primary_10_1016_j_pneurobio_2011_08_010 crossref_primary_10_3389_fpsyg_2015_00353 crossref_primary_10_1016_j_neuron_2016_02_018 crossref_primary_10_1016_j_tics_2019_07_012 crossref_primary_10_1038_npp_2010_163 crossref_primary_10_1111_tops_12415 crossref_primary_10_7554_eLife_80926 crossref_primary_10_1016_j_cognition_2016_08_015 crossref_primary_10_1016_j_cognition_2023_105603 crossref_primary_10_1109_TBDATA_2020_2988273 crossref_primary_10_1016_j_neuroscience_2022_08_015 crossref_primary_10_1016_j_tics_2010_05_008 crossref_primary_10_1073_pnas_1014938108 crossref_primary_10_1155_2012_937860 crossref_primary_10_3389_fnins_2021_704728 crossref_primary_10_1016_j_cognition_2017_11_001 crossref_primary_10_3389_fnins_2014_00101 crossref_primary_10_32388_1KC9TH_2 crossref_primary_10_32388_1KC9TH_3 crossref_primary_10_1371_journal_pcbi_1007475 crossref_primary_10_1016_j_neubiorev_2016_08_013 crossref_primary_10_1016_j_celrep_2024_115152 crossref_primary_10_3389_fpsyg_2018_00579 crossref_primary_10_1016_j_asoc_2018_01_042 crossref_primary_10_1162_CPSY_a_00002 crossref_primary_10_3389_fnins_2022_805658 crossref_primary_10_1109_JPROC_2020_2979233 crossref_primary_10_1016_j_cobeha_2019_04_009 crossref_primary_10_1016_j_cobeha_2016_08_002 crossref_primary_10_1093_cercor_bht218 crossref_primary_10_1162_jocn_a_01238 crossref_primary_10_1038_s41467_021_25419_4 crossref_primary_10_1016_j_bbi_2017_09_011 crossref_primary_10_3902_jnns_27_165 crossref_primary_10_1016_j_cognition_2014_11_009 crossref_primary_10_1162_NECO_a_00103 crossref_primary_10_1038_s41467_024_48338_6 crossref_primary_10_1152_jn_00395_2015 crossref_primary_10_1016_j_bbr_2013_02_004 crossref_primary_10_7554_eLife_55872 crossref_primary_10_1016_j_neuroimage_2025_121166 crossref_primary_10_1007_s10071_011_0387_4 crossref_primary_10_1371_journal_pone_0233308 crossref_primary_10_1016_j_neuron_2012_09_034 crossref_primary_10_1016_j_neuron_2019_11_018 crossref_primary_10_1038_s41598_022_06383_5 crossref_primary_10_1038_nn_2304 crossref_primary_10_3389_fnhum_2022_809616 crossref_primary_10_3390_app14177843 crossref_primary_10_7554_eLife_29908 crossref_primary_10_1007_s41465_024_00303_3 crossref_primary_10_1186_s40359_024_01952_x crossref_primary_10_15252_embr_201438993 crossref_primary_10_1016_j_conb_2011_02_009 crossref_primary_10_1101_sqb_2018_83_038166 crossref_primary_10_3390_e24101484 crossref_primary_10_1016_j_actpsy_2022_103511 crossref_primary_10_1017_S1355617725000013 crossref_primary_10_3390_brainsci10010055 crossref_primary_10_1007_s40636_024_00300_3 crossref_primary_10_3390_drones7120690 crossref_primary_10_3389_fnins_2023_1195388 crossref_primary_10_1016_j_jort_2024_100841 crossref_primary_10_12968_bjnn_2016_12_1_30 crossref_primary_10_1007_s11625_021_00989_w crossref_primary_10_3389_fncom_2022_1060101 crossref_primary_10_1016_j_jmp_2021_102544 crossref_primary_10_1038_s44159_024_00385_y crossref_primary_10_3758_s13420_022_00569_7 crossref_primary_10_1038_s41598_020_62970_4 crossref_primary_10_3758_s13423_018_1446_5 crossref_primary_10_1371_journal_pcbi_1010805 crossref_primary_10_1002_hbm_25019 crossref_primary_10_1016_j_biopsych_2012_03_033 crossref_primary_10_1002_mpr_1410 crossref_primary_10_1007_s00422_015_0669_z crossref_primary_10_1016_j_yhbeh_2017_05_002 crossref_primary_10_1027_1864_1105_a000315 crossref_primary_10_54097_ehss_v8i_4673 crossref_primary_10_1073_pnas_2010890117 crossref_primary_10_1016_j_celrep_2023_112523 crossref_primary_10_1038_s41598_023_27662_9 crossref_primary_10_3390_bs3030501 crossref_primary_10_1038_s41598_018_31985_3 crossref_primary_10_1007_s42113_021_00112_3 crossref_primary_10_1109_TRO_2020_2992987 crossref_primary_10_1177_02783649231167210 crossref_primary_10_1016_j_neubiorev_2016_09_010 crossref_primary_10_1038_s41386_022_01374_6 crossref_primary_10_1177_0023830920948552 crossref_primary_10_1016_j_ynstr_2022_100507 crossref_primary_10_1038_nn_4506 crossref_primary_10_1146_annurev_neuro_071714_033928 crossref_primary_10_1016_j_conb_2019_09_011 crossref_primary_10_3390_diagnostics12081835 crossref_primary_10_1371_journal_pcbi_1012331 crossref_primary_10_1016_j_jbi_2021_103913 crossref_primary_10_1162_NECO_a_00494 crossref_primary_10_1038_s41467_024_46921_5 crossref_primary_10_1038_s41562_019_0681_8 crossref_primary_10_1124_pharmrev_121_000508 crossref_primary_10_1016_j_jneumeth_2020_108912 crossref_primary_10_1016_j_neunet_2009_03_019 crossref_primary_10_1016_j_tree_2016_01_012 crossref_primary_10_1016_j_cognition_2018_06_008 crossref_primary_10_1093_cercor_bhab391 crossref_primary_10_1007_s11571_010_9109_x crossref_primary_10_1098_rstb_2012_0037 crossref_primary_10_1073_pnas_1712479114 crossref_primary_10_1038_s41583_024_00807_z crossref_primary_10_1002_oby_23792 crossref_primary_10_1007_s42113_024_00229_1 crossref_primary_10_1073_pnas_1211606110 crossref_primary_10_1016_j_cub_2017_02_026 crossref_primary_10_1016_j_resconrec_2020_105027 crossref_primary_10_1038_npp_2016_95 crossref_primary_10_1016_j_tics_2011_04_002 crossref_primary_10_1177_00027642231207073 crossref_primary_10_1016_j_copsyc_2017_09_011 crossref_primary_10_32388_1KC9TH crossref_primary_10_1111_cogs_12271 crossref_primary_10_1371_journal_pone_0108142 crossref_primary_10_1016_j_cogpsych_2017_03_002 crossref_primary_10_1038_s41467_018_04397_0 crossref_primary_10_1016_j_conb_2012_05_011 crossref_primary_10_1016_j_neubiorev_2017_02_003 crossref_primary_10_1016_j_isci_2021_102826 crossref_primary_10_1523_JNEUROSCI_2432_12_2012 crossref_primary_10_7554_eLife_49834 crossref_primary_10_1038_nn_3956 crossref_primary_10_1016_j_cogsys_2015_12_012 crossref_primary_10_1145_3505557 crossref_primary_10_3389_fnbeh_2016_00200 crossref_primary_10_1371_journal_pbio_3002031 crossref_primary_10_1080_17470919_2019_1668846 crossref_primary_10_1523_JNEUROSCI_5806_12_2014 crossref_primary_10_1162_NECO_a_00758 crossref_primary_10_1016_j_bbr_2017_09_030 crossref_primary_10_1177_2167702614562040 crossref_primary_10_1371_journal_pcbi_1007730 crossref_primary_10_1167_jov_24_5_17 crossref_primary_10_1177_1745691618792261 crossref_primary_10_1016_j_dr_2021_100979 crossref_primary_10_1038_s41467_024_44880_5 crossref_primary_10_1038_s41583_025_00916_3 crossref_primary_10_1016_j_neubiorev_2024_105877 crossref_primary_10_1093_bjps_axu013 crossref_primary_10_1016_j_neuron_2019_01_055 crossref_primary_10_1080_13546805_2010_548678 crossref_primary_10_1016_j_isci_2023_106761 crossref_primary_10_1016_j_neubiorev_2017_02_014 crossref_primary_10_1016_j_neuron_2013_09_007 crossref_primary_10_1016_j_nlm_2013_09_012 crossref_primary_10_1523_JNEUROSCI_3669_14_2015 crossref_primary_10_1086_682371 crossref_primary_10_1016_j_tics_2013_09_001 crossref_primary_10_1038_s41467_019_13135_z crossref_primary_10_1073_pnas_2009641117 crossref_primary_10_31887_DCNS_2016_18_1_asirigu crossref_primary_10_1016_j_neuropharm_2013_05_033 crossref_primary_10_1162_neco_2010_03_09_980 crossref_primary_10_1016_j_tics_2015_07_010 crossref_primary_10_1038_s42003_020_0786_7 crossref_primary_10_1126_science_1169405 crossref_primary_10_1002_brb3_70168 crossref_primary_10_2139_ssrn_4014051 crossref_primary_10_1073_pnas_1310272111 crossref_primary_10_1038_s41467_023_39536_9 crossref_primary_10_1038_s41593_021_01007_z crossref_primary_10_7554_eLife_55490 crossref_primary_10_3389_fnhum_2016_00550 crossref_primary_10_1016_j_neubiorev_2015_08_017 crossref_primary_10_1523_JNEUROSCI_6316_10_2011 crossref_primary_10_1007_s10551_016_3058_1 crossref_primary_10_1016_j_isci_2021_102127 crossref_primary_10_1016_j_bpsc_2016_05_001 crossref_primary_10_1016_j_biopsych_2012_05_010 crossref_primary_10_1146_annurev_psych_113011_143807 crossref_primary_10_1523_JNEUROSCI_1164_17_2017 crossref_primary_10_4161_cib_21474 crossref_primary_10_1038_s41386_019_0454_0 crossref_primary_10_3758_s13423_018_1554_2 crossref_primary_10_1073_pnas_1706693114 crossref_primary_10_1016_j_jclepro_2022_131142 crossref_primary_10_7554_eLife_21492 crossref_primary_10_1016_j_neuron_2017_07_039 crossref_primary_10_1371_journal_pcbi_1003015 crossref_primary_10_1007_s00213_020_05454_7 crossref_primary_10_1016_S2215_0366_14_70275_5 crossref_primary_10_1371_journal_pcbi_1011678 crossref_primary_10_1126_sciadv_aax8783 crossref_primary_10_1016_j_ibneur_2022_05_006 crossref_primary_10_1007_s10071_023_01769_y crossref_primary_10_1080_23273798_2015_1077979 crossref_primary_10_1016_j_neuron_2012_03_037 crossref_primary_10_1016_j_neuron_2015_03_019 crossref_primary_10_1371_journal_pone_0089494 crossref_primary_10_1155_2014_238357 crossref_primary_10_1016_j_neuron_2014_12_015 crossref_primary_10_1016_j_cognition_2018_11_004 crossref_primary_10_1016_j_tics_2018_08_008 |
| ContentType | Journal Article |
| Copyright | Psychonomic Society, Inc. 2008 Copyright Springer Science & Business Media Dec 2008 |
| Copyright_xml | – notice: Psychonomic Society, Inc. 2008 – notice: Copyright Springer Science & Business Media Dec 2008 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 4T- 4U- 7TK K9. NAPCQ 7X8 ADTOC UNPAY |
| DOI | 10.3758/CABN.8.4.429 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Docstoc University Readers Neurosciences Abstracts ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Premium MEDLINE - Academic Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Premium University Readers Neurosciences Abstracts Docstoc MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic ProQuest Health & Medical Complete (Alumni) MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Anatomy & Physiology Chemistry Psychology |
| EISSN | 1531-135X |
| EndPage | 453 |
| ExternalDocumentID | 10.3758/cabn.8.4.429 1655323101 19033240 10_3758_CABN_8_4_429 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Review |
| GroupedDBID | --- -55 -5G -BR -EM -~C -~X .-4 .GJ 04C 06D 0R~ 123 186 199 203 29F 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 30V 3V. 4.4 406 408 40E 53G 5GY 7RV 7X7 88E 8AO 8FI 8FJ 8UJ 95- 95. 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYJJ AAYQN AAYTO AAYZH AAZMS ABAKF ABDZT ABECU ABFTV ABHLI ABIVO ABJOX ABJUD ABKCH ABMQK ABNWP ABPLI ABQBU ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABXPI ACAOD ACBXY ACDTI ACGFO ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACPIV ACPRK ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEAQA AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETCA AEVLU AEXYK AFFNX AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHBYD AHKAY AHMBA AHSBF AHYZX AI. AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR AOCGG ARMRJ ASPBG AVWKF AXYYD AYQZM AZFZN AZQEC BAWUL BENPR BGNMA BKEYQ BMSDO BPHCQ BVXVI C1A CAG CCPQU COF CSCUP DDRTE DNIVK DPUIP DU5 DWQXO EBD EBLON EBS EIHBH EIOEI EJD EMOBN ESBYG EX3 F5P FEDTE FERAY FIGPU FINBP FNLPD FRRFC FSGXE FYUFA GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 H13 HF~ HG6 HMCUK HMJXF HRMNR HVGLF HZ~ IKXTQ ITM IWAJR J-C JBSCW JZLTJ KOV LLZTM M1P M2M M4Y N9A NAPCQ NEJ NHB NPVJJ NQJWS NU0 O9- O93 O9G O9J OK1 P9L PF- PQQKQ PROAC PSQYO PSYQQ PT4 QII R9I ROL RPV RSV S16 S1Z S27 S3B SBS SHX SISQX SJN SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZN T13 TN5 TSG TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW VH1 VQA W48 WK8 WOW YYQ Z83 ZMTXR ZOVNA ZUP ZY4 AAPKM AAYXX ABFSG ACSTC ADXHL AETEA AEZWR AFHIU AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION ABBRH ABDBE ABRTQ AFDZB AFOHR CGR CUY CVF ECM EIF NPM PHGZM PHGZT PJZUB PPXIY 4T- 4U- 7TK K9. 7X8 ADTOC PUEGO UNPAY |
| ID | FETCH-LOGICAL-c458t-f325ada944ebe0c032ef418c358aa468efb8f78c9c75bfe26fa5e76d3e25dbd73 |
| IEDL.DBID | AGYKE |
| ISSN | 1530-7026 1531-135X |
| IngestDate | Tue Sep 23 05:43:27 EDT 2025 Wed Oct 01 13:07:02 EDT 2025 Tue Oct 07 05:59:49 EDT 2025 Mon Jul 21 06:05:47 EDT 2025 Thu Apr 24 23:08:00 EDT 2025 Wed Oct 01 00:47:30 EDT 2025 Fri Feb 21 02:31:28 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Lateral Intraparietal Area Temporal Difference Model Belief State Markov Decision Problem Ective State |
| Language | English |
| License | http://www.springer.com/tdm cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c458t-f325ada944ebe0c032ef418c358aa468efb8f78c9c75bfe26fa5e76d3e25dbd73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://link.springer.com/content/pdf/10.3758/CABN.8.4.429.pdf |
| PMID | 19033240 |
| PQID | 204454569 |
| PQPubID | 976343 |
| PageCount | 25 |
| ParticipantIDs | unpaywall_primary_10_3758_cabn_8_4_429 proquest_miscellaneous_69837227 proquest_journals_204454569 pubmed_primary_19033240 crossref_citationtrail_10_3758_CABN_8_4_429 crossref_primary_10_3758_CABN_8_4_429 springer_journals_10_3758_CABN_8_4_429 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20081200 2008-12-01 2008-Dec 20081201 |
| PublicationDateYYYYMMDD | 2008-12-01 |
| PublicationDate_xml | – month: 12 year: 2008 text: 20081200 |
| PublicationDecade | 2000 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: United States |
| PublicationTitle | Cognitive, affective, & behavioral neuroscience |
| PublicationTitleAbbrev | Cognitive, Affective, & Behavioral Neuroscience |
| PublicationTitleAlternate | Cogn Affect Behav Neurosci |
| PublicationYear | 2008 |
| Publisher | Springer-Verlag Springer Nature B.V |
| Publisher_xml | – name: Springer-Verlag – name: Springer Nature B.V |
| References | SahaniM.DayanP.Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicityNeural Computation200315225522791451152110.1162/089976603322362356 CohenJ. D.McClureS. M.YuA. J.Should I stay or should I go? How the human brain manages the trade-off between exploitation and explorationPhilosophical Transactions of the Royal Society B200736293394210.1098/rstb.2007.2098 RoitmanJ. D.ShadlenM. N.Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time taskJournal of Neuroscience2002229475948912417672 DawN. D.NivY.DayanP.Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral controlNature Neuroscience20058170417111628693210.1038/nn1560 RossS.Introduction to stochastic dynamic programming: Probability and mathematical1983Orlando, FLAcademic Press ShadlenM. N.NewsomeW. T.Motion perception: Seeing and decidingProceedings of the National Academy of Sciences19969362863310.1073/pnas.93.2.628 YangT.ShadlenM. N.Probabilistic reasoning by neuronsNature2007447107510801754602710.1038/nature05852 BrittenK. H.ShadlenM. N.NewsomeW. T.MovshonJ. A.The analysis of visual motion: A comparison of neuronal and psychophysical performanceJournal of Neuroscience199212474547651464765 NealR. M.HintonG. E.JordanM. I.A view of the EM algorithm that justifies incremental, sparse, and other variantsLearning in graphical models1998Norwell, MAKluwer355368 DawN. D.CourvilleA. C.TouretzkyD. S.Representation and timing in theories of the dopamine systemNeural Computation200618163716771676451710.1162/neco.2006.18.7.1637 DoyaK.Metalearning and neuromodulationNeural Networks2002154955061237150710.1016/S0893-6080(02)00044-8 KördingK. [P.]Decision theory: What “should”the nervous system do?Science20073186066101796255410.1126/science.1142998 HarunoM.KurodaT.DoyaK.ToyamaK.KimuraM.SamejimaK.A neural correlate of reward-based behavioral learning in caudate nucleus:A functional magnetic resonance imagresearch ing study of a stochastic decision taskJournal of Neuroscience200424166016651497323910.1523/JNEUROSCI.3417-03.2004 GoldJ. I.ShadlenM. N.Neural computations that underlie decisions about sensory stimuliTrends in Cognitive Sciences2001510161116473110.1016/S1364-6613(00)01567-9 RaoR. P. N.Bayesian computation in recurrent neural circuitsNeural Computation2004161381500602110.1162/08997660460733976 CostaR. M.BalleineB. W.DoyaK.O’DohertyJ.SakagamiM.Plastic corticostriatal circuits for action learning: What’s dopamine got to do with it?Reward and decision making in corticobasal ganglia networks2007New YorkNew York Academy of Sciences172191Annals of the New York Academy of Sciences BogaczR.BrownE.MoehlisJ.HolmesP.CohenJ. D.The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasksPsychology Review200611370076510.1037/0033-295X.113.4.700 GoldJ. I.ShadlenM. N.The neural basis of decision makingAnnual Review of Neuroscience2007305355741760052510.1146/annurev.neuro.29.051605.113038 BergerJ. O.Statistical decision theory and Bayesian analysis1985New YorkSpringer O’DohertyJ. P.DayanP.FristonK.CritchleyH.DolanR. J.Temporal difference models and reward-related learning in the human brainNeuron2003383293371271886510.1016/S0896-6273(03)00169-7 WickensJ. R.HorvitzJ. C.CostaR. M.KillcrossS.Dopaminergic mechanisms in actions and habitsJournal of Neuroscience200727818181831767096410.1523/JNEUROSCI.1671-07.2007 StockerA. A.SimoncelliE. P.Noise characteristics and prior expectations in human visual speed perceptionNature Neuroscience200695785851654751310.1038/nn1669 JaakkolaT.JordanM. I.SinghS. P.On the convergence of stochastic iterative dynamic programming algorithmsNeural Computation199461185120110.1162/neco.1994.6.6.1185 SchultzW.Getting formal with dopamine and rewardNeuron2002362412631238378010.1016/S0896-6273(02)00967-4 DawN. D.O’DohertyJ. P.DayanP.SeymourB.DolanR. J.Cortical substrates for exploratory decisions in humansNature20064418768791677889010.1038/nature04766 EverittB. J.RobbinsT. W.Neural systems of reinforcement for drug addiction: From actions to habits to compulsionNature Neuroscience20058148114891625199110.1038/nn1579 SuttonR. S.PorterB. W.MooneyR. J.Integrated architectures for learning, planning, and reacting based on approximating dynamic programmingProceedings of the Seventh International Conference on Machine Learning1990San FranciscoMorgan Kaufmann216224 DeardenR.FriedmanN.RussellS.Bayesian Q-learningProceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence1998Menlo Park, CAAAAI Press761768 BellmanR. E.Dynamic programming1957Princeton, NJPrinceton University Press ShadlenM. N.BrittenK. H.NewsomeW. T.MovshonJ. A.A computational analysis of the relationship between neuronal and behavioral responses to visual motionJournal of Neuroscience199616148615108778300 BartoA. G.HoukJ. C.DavisJ. L.BeiserD. G.Adaptive critics and the basal gangliaModels of information processing in the basal ganglia1995Cambridge, MAMIT Press215232 WittmannB. C.DawN. D.SeymourB.DolanR. J.Striatal activity underlies novelty-based choice in humansNeuron2008589679731857908510.1016/j.neuron.2008.04.027 YuilleA. J.BülthoffH. H.KnillD. C.RichardsW.Bayesian decision theory and psychophysicsPerception as Bayesian inference1996CambridgeCambridge University Press123161 BalleineB. W.DelgadoM. R.HikosakaO.The role of the dorsal striatum in reward and decision-makingJournal of Neuroscience200727816181651767095910.1523/JNEUROSCI.1554-07.2007 SmithP. L.RatcliffR.Psychology and neurobiology of simple decisionsTrends in Neurosciences2004271611681503688210.1016/j.tins.2004.01.006 TrommershäuserJ.MaloneyL. T.LandyM. S.Statistical decision theory and trade-offs in the control of motor responseSpatial Vision2003162552751285895110.1163/156856803322467527 DempsterA. P.LairdN. M.RubinD. B.Maximum likelihood from incomplete data via the EM algorithmJournal of the Royal Statistical Society: Series B197739138 RatcliffR.McKoonG.The diffusion decision model: Theory and data for two-choice decision tasksNeural Computation2008208739221808599110.1162/neco.2008.12-06-420 RatcliffR.RouderJ.Modeling response times for twochoice decisionsPsychological Science1998934735610.1111/1467-9280.00067 TrommershäuserJ.MaloneyL. T.LandyM. S.Statistical decision theory and the selection of rapid, goal-directed movementsJournal of the Optical Society of America A2003201419143310.1364/JOSAA.20.001419 NgA.HaradaD.RussellS.Policy invariance under reward transformations: Theory and application to reward shapingProceedings of the Sixteenth International Conference on Machine Learning1999San FranciscoMorgan Kaufmann278287 BertsekasD. P.Dynamic programming and optimal control2007Belmont, MAAthena Scientific GlimcherP. W.Decisions, uncertainty, and the brain: The science of neuroeconomics2004Cambridge, MAMIT Press, Bradford Books ParkerA. J.NewsomeW. T.Sense and the single neuron: Probing the physiology of perceptionAnnual Review of Neuroscience199821227277953049710.1146/annurev.neuro.21.1.227 DayanP.AbbottL. F.Theoretical neuroscience: Computational and mathematical modeling of neural systems2005Cambridge, MAMIT Press Montague[P.] R.Why choose this book?: How we make decisions2006New YorkDutton LengyelM.DayanP.PlattJ.KollerD.SingerY.RoweisS.Hippocampal contributions to control: The third wayAdvances in neural information processing systems 202008Cambridge, MAMIT Press889896 WickensJ. [R.]Striatal dopamine in motor activation and reward-mediated learning: Steps towards a unifying modelJournal of Neural Transmission199080931240726910.1007/BF01245020 BeckJ. M.PougetA.Exact inferences in a neural implementation of a hidden Markov modelNeural Computation200719134413611738126910.1162/neco.2007.19.5.1344 Watkins, C. (1989). Learning from delayed rewards. Unpublished doctoral thesis, University of Cambridge. WhiteleyL.SahaniM.Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomesJournal of Vision200881151848480810.1167/8.3.2 ShadlenM. N.HanksT. D.ChurchlandA. K.KianiR.YangT.DoyaK.IshiiS.PougetA.RaoR. P.The speed and accuracy of a simple perceptual decision: A mathematical primerBayesian brain: Probabilistic approaches to neural coding2007Cambridge, MAMIT Press209238 DickinsonA.BalleineB.GallistelC.The role of learning in motivationStevens’s handbook of experimental psychology2002New YorkWiley497533 YuA. J.SchölkopfB.PlattJ.HofmannT.Optimal change-detection and spiking neuronsAdvances in neural information processing systems 192007Cambridge, MAMIT Press15451552 BattagliaP. W.JacobsR. A.AslinR. N.Bayesian integration of visual and auditory signals for spatial localizationJournal of the Optical Society of America A2003201391139710.1364/JOSAA.20.001391 DeneveS.Bayesian spiking neurons I: InferenceNeural Computation200820911171804500210.1162/neco.2008.20.1.91 JacobsR. A.Optimal integration of texture and motion cues to depthVision Research199939362136291074613210.1016/S0042-6989(99)00088-7 WilliamsR. J.Simple statistical gradient-following algorithms for connectionist reinforcement learningMachine Learning19928229256 ErnstM. O.BanksM. S.Humans integrate visual and haptic information in a statistically optimal fashionNature20024154294331180755410.1038/415429a TrommershäuserJ.LandyM. S.MaloneyL. T.Humans rapidly estimate expected gain in movement planningPsychological Science2006179819881717643110.1111/j.1467-9280.2006.01816.x ZemelR. S.DayanP.PougetA.Probabilistic interpretation of population codesNeural Computation199810403430947248810.1162/089976698300017818 SuriR. E.SchultzW.Learning of sequential movements by neural network model with dopamine-like reinforcement signalExperimental Brain Research199812135035410.1007/s002210050467 BaxterJ.BartlettP. L.Infinite-horizon policy-gradient estimationJournal of Artificial Intelligence Research20011531935010.1016/S0954-1810(01)00028-0 McClureS. M.GilzenratM. S.CohenJ. D.WeissY.Schö 11807554 - Nature. 2002 Jan 24;415(6870):429-33 8730992 - Vis Neurosci. 1996 Jan-Feb;13(1):87-100 17670959 - J Neurosci. 2007 Aug 1;27(31):8161-5 16547513 - Nat Neurosci. 2006 Apr;9(4):578-85 17435119 - Ann N Y Acad Sci. 2007 May;1104:172-91 17962554 - Science. 2007 Oct 26;318(5850):606-10 12371507 - Neural Netw. 2002 Jun-Jul;15(4-6):495-506 11164731 - Trends Cogn Sci. 2001 Jan 1;5(1):10-16 1464765 - J Neurosci. 1992 Dec;12(12):4745-65 14511521 - Neural Comput. 2003 Oct;15(10):2255-79 15006021 - Neural Comput. 2004 Jan;16(1):1-38 16731515 - Neuron. 2006 Jun 1;50(5):781-9 17395573 - Philos Trans R Soc Lond B Biol Sci. 2007 May 29;362(1481):933-42 18085991 - Neural Comput. 2008 Apr;20(4):873-922 15190354 - Nature. 2004 Jun 10;429(6992):664-7 16776597 - Annu Rev Neurosci. 2006;29:565-98 16767089 - Nat Neurosci. 2006 Jul;9(7):956-63 15036882 - Trends Neurosci. 2004 Mar;27(3):161-8 17546027 - Nature. 2007 Jun 28;447(7148):1075-80 17982449 - Nat Neurosci. 2007 Dec;10(12):1625-33 11488378 - Psychol Rev. 2001 Jul;108(3):550-92 16617339 - Nat Neurosci. 2006 May;9(5):690-6 15087550 - Science. 2004 Apr 16;304(5669):452-4 12383780 - Neuron. 2002 Oct 10;36(2):241-63 10421364 - Nature. 1999 Jul 15;400(6741):233-8 14973239 - J Neurosci. 2004 Feb 18;24(7):1660-5 14724638 - Nature. 2004 Jan 15;427(6971):244-7 18045002 - Neural Comput. 2008 Jan;20(1):91-117 8774460 - J Neurosci. 1996 Mar 1;16(5):1936-47 2407269 - J Neural Transm Gen Sect. 1990;80(1):9-31 12868646 - J Opt Soc Am A Opt Image Sci Vis. 2003 Jul;20(7):1419-33 16764517 - Neural Comput. 2006 Jul;18(7):1637-77 9054347 - Science. 1997 Mar 14;275(5306):1593-9 18055018 - Brain Res Rev. 2008 Aug;58(2):322-39 16778890 - Nature. 2006 Jun 15;441(7095):876-9 17600525 - Annu Rev Neurosci. 2007;30:535-74 12631569 - Cereb Cortex. 2003 Apr;13(4):400-8 16286932 - Nat Neurosci. 2005 Dec;8(12):1704-11 18579085 - Neuron. 2008 Jun 26;58(6):967-73 17603481 - Nat Neurosci. 2007 Aug;10(8):1020-8 8008189 - Neuroscience. 1994 Mar;59(2):229-43 17057707 - Nat Neurosci. 2006 Nov;9(11):1432-8 12858951 - Spat Vis. 2003;16(3-4):255-75 12383783 - Neuron. 2002 Oct 10;36(2):299-308 9746140 - Exp Brain Res. 1998 Aug;121(3):350-4 17381269 - Neural Comput. 2007 May;19(5):1344-61 17925267 - Prog Brain Res. 2007;165:509-19 9472488 - Neural Comput. 1998 Feb 15;10(2):403-30 10746132 - Vision Res. 1999 Oct;39(21):3621-9 17072591 - Psychopharmacology (Berl). 2007 Apr;191(3):391-431 12868643 - J Opt Soc Am A Opt Image Sci Vis. 2003 Jul;20(7):1391-7 15486304 - Science. 2004 Oct 15;306(5695):503-7 17014301 - Psychol Rev. 2006 Oct;113(4):700-65 17176431 - Psychol Sci. 2006 Nov;17(11):981-8 16563737 - Curr Opin Neurobiol. 2006 Apr;16(2):199-204 7442286 - J Theor Biol. 1980 Aug 21;85(4):673-90 12718865 - Neuron. 2003 Apr 24;38(2):329-37 17670964 - J Neurosci. 2007 Aug 1;27(31):8181-3 16251991 - Nat Neurosci. 2005 Nov;8(11):1481-9 18484808 - J Vis. 2008 Mar 06;8(3):2.1-15 12417672 - J Neurosci. 2002 Nov 1;22(21):9475-89 16508903 - Pharmacopsychiatry. 2006 Feb;39 Suppl 1:S80-7 12371511 - Neural Netw. 2002 Jun-Jul;15(4-6):549-59 9530497 - Annu Rev Neurosci. 1998;21:227-77 8778300 - J Neurosci. 1996 Feb 15;16(4):1486-510 8570606 - Proc Natl Acad Sci U S A. 1996 Jan 23;93(2):628-33 12467598 - Neuron. 2002 Dec 5;36(5):955-68 12371510 - Neural Netw. 2002 Jun-Jul;15(4-6):535-47 |
| References_xml | – reference: ShadlenM. N.HanksT. D.ChurchlandA. K.KianiR.YangT.DoyaK.IshiiS.PougetA.RaoR. P.The speed and accuracy of a simple perceptual decision: A mathematical primerBayesian brain: Probabilistic approaches to neural coding2007Cambridge, MAMIT Press209238 – reference: JazayeriM.MovshonJ. A.Optimal representation of sensory information by neural populationsNature Neuroscience200696906961661733910.1038/nn1691 – reference: McNamaraJ.HoustonA.The application of statistical decision theory to animal behaviourJournal of Theoretical Biology198085673690744228610.1016/0022-5193(80)90265-9 – reference: ChrismanL.Reinforcement learning with perceptual aliasing: The perceptual distinctions approachProceedings of the 10th National Conference on Artificial Intelligence1992Menlo Park, CAAAAI Press – reference: JacobsR. A.Optimal integration of texture and motion cues to depthVision Research199939362136291074613210.1016/S0042-6989(99)00088-7 – reference: TrommershäuserJ.MaloneyL. T.LandyM. S.Statistical decision theory and the selection of rapid, goal-directed movementsJournal of the Optical Society of America A2003201419143310.1364/JOSAA.20.001419 – reference: BrittenK. H.ShadlenM. N.NewsomeW. T.MovshonJ. A.The analysis of visual motion: A comparison of neuronal and psychophysical performanceJournal of Neuroscience199212474547651464765 – reference: RatcliffR.McKoonG.The diffusion decision model: Theory and data for two-choice decision tasksNeural Computation2008208739221808599110.1162/neco.2008.12-06-420 – reference: NgA.HaradaD.RussellS.Policy invariance under reward transformations: Theory and application to reward shapingProceedings of the Sixteenth International Conference on Machine Learning1999San FranciscoMorgan Kaufmann278287 – reference: ParkerA. J.NewsomeW. T.Sense and the single neuron: Probing the physiology of perceptionAnnual Review of Neuroscience199821227277953049710.1146/annurev.neuro.21.1.227 – reference: RescorlaR.WagnerA.BlackA. H.ProkasyW. F.A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcementClassical conditioning II: Current research and theory1972New YorkAppleton-Century-Crofts6499 – reference: WittmannB. C.DawN. D.SeymourB.DolanR. J.Striatal activity underlies novelty-based choice in humansNeuron2008589679731857908510.1016/j.neuron.2008.04.027 – reference: DeardenR.FriedmanN.RussellS.Bayesian Q-learningProceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence1998Menlo Park, CAAAAI Press761768 – reference: SchultzW.DayanP.MontagueP. R.A neural substrate of prediction and rewardScience199727515931599905434710.1126/science.275.5306.1593 – reference: GoldJ. I.ShadlenM. N.Banburismus and the brain: Decoding the relationship between sensory stimuli, decisions, and rewardNeuron2002362993081238378310.1016/S0896-6273(02)00971-6 – reference: RoitmanJ. D.ShadlenM. N.Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time taskJournal of Neuroscience2002229475948912417672 – reference: SmithP. L.RatcliffR.Psychology and neurobiology of simple decisionsTrends in Neurosciences2004271611681503688210.1016/j.tins.2004.01.006 – reference: DawN. D.O’DohertyJ. P.DayanP.SeymourB.DolanR. J.Cortical substrates for exploratory decisions in humansNature20064418768791677889010.1038/nature04766 – reference: BertsekasD. P.TsitsiklisJ. N.Neuro-dynamic programming1996Belmont, MAAthena Scientific – reference: TrommershäuserJ.LandyM. S.MaloneyL. T.Humans rapidly estimate expected gain in movement planningPsychological Science2006179819881717643110.1111/j.1467-9280.2006.01816.x – reference: WaldA.Sequential analysis1947New YorkWiley – reference: KördingK. [P.]Decision theory: What “should”the nervous system do?Science20073186066101796255410.1126/science.1142998 – reference: O’DohertyJ. [P.]DayanP.SchultzJ.DeichmannR.FristonK.DolanR. J.Dissociable roles of ventral and dorsal striatum in instrumental conditioningScience20043044524541508755010.1126/science.1094285 – reference: KakadeS.DayanP.Dopamine: Generalization and bonusesNeural Networks2002155495591237151110.1016/S0893-6080(02)00048-5 – reference: DawN. D.DoyaK.The computational neurobiology of learning and rewardCurrent Opinion in Neurobiology2006161992041656373710.1016/j.conb.2006.03.006 – reference: ShadlenM. N.NewsomeW. T.Motion perception: Seeing and decidingProceedings of the National Academy of Sciences19969362863310.1073/pnas.93.2.628 – reference: BattagliaP. W.JacobsR. A.AslinR. N.Bayesian integration of visual and auditory signals for spatial localizationJournal of the Optical Society of America A2003201391139710.1364/JOSAA.20.001391 – reference: GoldJ. I.ShadlenM. N.The neural basis of decision makingAnnual Review of Neuroscience2007305355741760052510.1146/annurev.neuro.29.051605.113038 – reference: RatcliffR.RouderJ.Modeling response times for twochoice decisionsPsychological Science1998934735610.1111/1467-9280.00067 – reference: SchultzW.Getting formal with dopamine and rewardNeuron2002362412631238378010.1016/S0896-6273(02)00967-4 – reference: DempsterA. P.LairdN. M.RubinD. B.Maximum likelihood from incomplete data via the EM algorithmJournal of the Royal Statistical Society: Series B197739138 – reference: DayanP.SejnowskiT. J.Exploration bonuses and dual controlMachine Learning199625522 – reference: RaoR. P. N.Bayesian computation in recurrent neural circuitsNeural Computation2004161381500602110.1162/08997660460733976 – reference: YangT.ShadlenM. N.Probabilistic reasoning by neuronsNature2007447107510801754602710.1038/nature05852 – reference: SahaniM.DayanP.Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicityNeural Computation200315225522791451152110.1162/089976603322362356 – reference: NealR. M.HintonG. E.JordanM. I.A view of the EM algorithm that justifies incremental, sparse, and other variantsLearning in graphical models1998Norwell, MAKluwer355368 – reference: RedgraveP.GurneyK.ReynoldsJ.What is reinforced by phasic dopamine signals?Brain Research Reviews2008583223391805501810.1016/j.brainresrev.2007.10.007 – reference: Watkins, C. (1989). Learning from delayed rewards. Unpublished doctoral thesis, University of Cambridge. – reference: EverittB. J.RobbinsT. W.Neural systems of reinforcement for drug addiction: From actions to habits to compulsionNature Neuroscience20058148114891625199110.1038/nn1579 – reference: UsherM.McClellandJ. L.The time course of perceptual choice: The leaky, competing accumulator modelPsychological Review20011085505921148837810.1037/0033-295X.108.3.550 – reference: PykeG. H.Optimal foraging theory: A critical reviewAnnual Review of Ecology & Systematics19841552357510.1146/annurev.es.15.110184.002515 – reference: ShadlenM. N.BrittenK. H.NewsomeW. T.MovshonJ. A.A computational analysis of the relationship between neuronal and behavioral responses to visual motionJournal of Neuroscience199616148615108778300 – reference: DawN. D.NivY.DayanP.Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral controlNature Neuroscience20058170417111628693210.1038/nn1560 – reference: McClureS. M.GilzenratM. S.CohenJ. D.WeissY.SchölkopfB.PlattJ.An exploration-exploitation model based on norepinephrine and dopamine activityAdvances in neural information processing systems 182006Cambridge, MAMIT Press867874 – reference: MaW. J.BeckJ. M.LathamP. E.PougetA.Bayesian inference with probabilistic population codesNature Neuroscience20069143214381705770710.1038/nn1790 – reference: SuriR. E.SchultzW.Learning of sequential movements by neural network model with dopamine-like reinforcement signalExperimental Brain Research199812135035410.1007/s002210050467 – reference: WangX.-J.Probabilistic decision making by slow reverberation in cortical circuitsNeuron2002369559681246759810.1016/S0896-6273(02)01092-9 – reference: BartoA. G.HoukJ. C.DavisJ. L.BeiserD. G.Adaptive critics and the basal gangliaModels of information processing in the basal ganglia1995Cambridge, MAMIT Press215232 – reference: KaelblingL. P.Learning in embedded systems1993Cambridge, MAMIT Press – reference: JoelD.NivY.RuppinE.Actor-critic models of the basal ganglia: New anatomical and computational perspectivesNeural Networks2002155355471237151010.1016/S0893-6080(02)00047-3 – reference: StockerA. A.SimoncelliE. P.Noise characteristics and prior expectations in human visual speed perceptionNature Neuroscience200695785851654751310.1038/nn1669 – reference: TrommershäuserJ.MaloneyL. T.LandyM. S.Statistical decision theory and trade-offs in the control of motor responseSpatial Vision2003162552751285895110.1163/156856803322467527 – reference: YoshidaW.IshiiS.Resolution of uncertainty in prefrontal cortexNeuron2006507817891673151510.1016/j.neuron.2006.05.006 – reference: HymanS. E.MalenkaR. C.NestlerE. J.Neural mechanisms of addiction: The role of reward-related learning and memoryAnnual Review of Neuroscience2006295655981677659710.1146/annurev.neuro.29.051605.113009 – reference: BogaczR.BrownE.MoehlisJ.HolmesP.CohenJ. D.The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasksPsychology Review200611370076510.1037/0033-295X.113.4.700 – reference: ZemelR. S.DayanP.PougetA.Probabilistic interpretation of population codesNeural Computation199810403430947248810.1162/089976698300017818 – reference: O’DohertyJ. P.DayanP.FristonK.CritchleyH.DolanR. J.Temporal difference models and reward-related learning in the human brainNeuron2003383293371271886510.1016/S0896-6273(03)00169-7 – reference: KördingK. P.WolpertD. M.Bayesian integration in sensorimotor learningNature20044272442471472463810.1038/nature02169 – reference: BrittenK. H.NewsomeW. T.ShadlenM. N.CelebriniS.MovshonJ. A.A relationship between behavioral choice and the visual responses of neurons in macaque mtVisual Neuroscience19961387100873099210.1017/S095252380000715X – reference: ErnstM. O.BanksM. S.Humans integrate visual and haptic information in a statistically optimal fashionNature20024154294331180755410.1038/415429a – reference: SuttonR. S.PorterB. W.MooneyR. J.Integrated architectures for learning, planning, and reacting based on approximating dynamic programmingProceedings of the Seventh International Conference on Machine Learning1990San FranciscoMorgan Kaufmann216224 – reference: WickensJ. [R.]Striatal dopamine in motor activation and reward-mediated learning: Steps towards a unifying modelJournal of Neural Transmission199080931240726910.1007/BF01245020 – reference: MangelM.ClarkC. W.Dynamic modeling in behavioral ecology1989Princeton, NJPrinceton University Press – reference: WangX.-J.Toward a prefrontal microcircuit model for cognitive deficits in schizophreniaPharmacopsychiatry200639(Suppl. 1)S80S871650890310.1055/s-2006-931501 – reference: KillcrossS.CoutureauE.Coordination of actions and habits in the medial prefrontal cortex of ratsCerebral Cortex2003134004081263156910.1093/cercor/13.4.400 – reference: WhiteleyL.SahaniM.Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomesJournal of Vision200881151848480810.1167/8.3.2 – reference: BerridgeK. C.The debate over dopamine’s role in reward: The case for incentive saliencePsychopharmacology20071913914311707259110.1007/s00213-006-0578-x – reference: YuilleA. J.BülthoffH. H.KnillD. C.RichardsW.Bayesian decision theory and psychophysicsPerception as Bayesian inference1996CambridgeCambridge University Press123161 – reference: WickensJ. R.HorvitzJ. C.CostaR. M.KillcrossS.Dopaminergic mechanisms in actions and habitsJournal of Neuroscience200727818181831767096410.1523/JNEUROSCI.1671-07.2007 – reference: RossS.Introduction to stochastic dynamic programming: Probability and mathematical1983Orlando, FLAcademic Press – reference: DeneveS.Bayesian spiking neurons I: InferenceNeural Computation200820911171804500210.1162/neco.2008.20.1.91 – reference: DickinsonA.BalleineB.GallistelC.The role of learning in motivationStevens’s handbook of experimental psychology2002New YorkWiley497533 – reference: BellmanR. E.Dynamic programming1957Princeton, NJPrinceton University Press – reference: CostaR. M.BalleineB. W.DoyaK.O’DohertyJ.SakagamiM.Plastic corticostriatal circuits for action learning: What’s dopamine got to do with it?Reward and decision making in corticobasal ganglia networks2007New YorkNew York Academy of Sciences172191Annals of the New York Academy of Sciences – reference: GreenD. M.SwetsJ. A.Signal detection theory and psychophysics1966New YorkWiley – reference: YuA. J.SchölkopfB.PlattJ.HofmannT.Optimal change-detection and spiking neuronsAdvances in neural information processing systems 192007Cambridge, MAMIT Press15451552 – reference: FristonK. J.TononiG.ReekeG. N.SpornsO.EdelmanG. M.Value-dependent selection in the brain: Simulation in a synthetic neural modelNeuroscience199459229243800818910.1016/0306-4522(94)90592-4 – reference: DayanP.AbbottL. F.Theoretical neuroscience: Computational and mathematical modeling of neural systems2005Cambridge, MAMIT Press – reference: AinslieG.Breakdown of will2001CambridgeCambridge University Press – reference: CohenJ. D.McClureS. M.YuA. J.Should I stay or should I go? How the human brain manages the trade-off between exploitation and explorationPhilosophical Transactions of the Royal Society B200736293394210.1098/rstb.2007.2098 – reference: BerryD. A.FristedtB.Bandit problems: Sequential allocation of experiments1985LondonChapman & Hall(Monographs on Statistics and Applied Probability) – reference: HarunoM.KurodaT.DoyaK.ToyamaK.KimuraM.SamejimaK.A neural correlate of reward-based behavioral learning in caudate nucleus:A functional magnetic resonance imagresearch ing study of a stochastic decision taskJournal of Neuroscience200424166016651497323910.1523/JNEUROSCI.3417-03.2004 – reference: PutermanM. L.Markov decision processes: Discrete stochastic dynamic programming2005New YorkWiley Interscience – reference: JaakkolaT.JordanM. I.SinghS. P.On the convergence of stochastic iterative dynamic programming algorithmsNeural Computation199461185120110.1162/neco.1994.6.6.1185 – reference: GittinsJ. C.Multi-armed bandit allocation indices1989New YorkWiley – reference: LengyelM.DayanP.PlattJ.KollerD.SingerY.RoweisS.Hippocampal contributions to control: The third wayAdvances in neural information processing systems 202008Cambridge, MAMIT Press889896 – reference: PlattM. L.GlimcherP. W.Neural correlates of decision variables in parietal cortexNature19994002332381042136410.1038/22268 – reference: Montague[P.] R.Why choose this book?: How we make decisions2006New YorkDutton – reference: GoldJ. I.ShadlenM. N.Neural computations that underlie decisions about sensory stimuliTrends in Cognitive Sciences2001510161116473110.1016/S1364-6613(00)01567-9 – reference: DayJ. J.RoitmanM. F.WightmanR. M.CarelliR. M.Associative learning mediates dynamic shifts in dopamine signaling in the nucleus accumbensNature Neuroscience200710102010281760348110.1038/nn1923 – reference: BalleineB. W.DelgadoM. R.HikosakaO.The role of the dorsal striatum in reward and decision-makingJournal of Neuroscience200727816181651767095910.1523/JNEUROSCI.1554-07.2007 – reference: McClureS. M.LaibsonD. I.LoewensteinG.CohenJ. D.Separate neural systems value immediate and delayed monetary rewardsScience20043065035071548630410.1126/science.1100907 – reference: DoyaK.Metalearning and neuromodulationNeural Networks2002154955061237150710.1016/S0893-6080(02)00044-8 – reference: MontagueP. R.DayanP.SejnowskiT. J.A framework for mesencephalic dopamine systems based on predictive Hebbian learningJournal of Neuroscience199616193619478774460 – reference: DawN. D.CourvilleA. C.TouretzkyD. S.Representation and timing in theories of the dopamine systemNeural Computation200618163716771676451710.1162/neco.2006.18.7.1637 – reference: BergerJ. O.Statistical decision theory and Bayesian analysis1985New YorkSpringer – reference: KableJ. W.GlimcherP. W.The neural correlates of subjective value during intertemporal choiceNature Neuroscience200710162516331798244910.1038/nn2007 – reference: SuttonR. S.Learning to predict by the methods of temporal differencesMachine Learning19883944 – reference: BeckJ. [M.]MaW. J.LathamP. E.PougetA.Probabilistic population codes and the exponential family of distributionsProgress in Brain Research20071655095191792526710.1016/S0079-6123(06)65032-2 – reference: BeckJ. M.PougetA.Exact inferences in a neural implementation of a hidden Markov modelNeural Computation200719134413611738126910.1162/neco.2007.19.5.1344 – reference: SuttonR. S.BartoA. G.Reinforcement learning: An introduction1998Cambridge, MAMIT Press – reference: GlimcherP. W.Decisions, uncertainty, and the brain: The science of neuroeconomics2004Cambridge, MAMIT Press, Bradford Books – reference: LoC.-C.WangX.-J.Cortico-basal ganglia circuit mechanism for a decision threshold in reaction time tasksNature Neuroscience200699569631676708910.1038/nn1722 – reference: BaxterJ.BartlettP. L.Infinite-horizon policy-gradient estimationJournal of Artificial Intelligence Research20011531935010.1016/S0954-1810(01)00028-0 – reference: WilliamsR. J.Simple statistical gradient-following algorithms for connectionist reinforcement learningMachine Learning19928229256 – reference: KrebsJ.KacelnikA.TaylorP.Test of optimal sampling by foraging great titsNature1978275273110.1038/275027a0 – reference: BertsekasD. P.Dynamic programming and optimal control2007Belmont, MAAthena Scientific – reference: SeymourB.O’DohertyJ. P.DayanP.KoltzenburgM.JonesA. K.DolanR. J.Temporal difference models describe higher-order learning in humansNature20044296646671519035410.1038/nature02581 – reference: 17176431 - Psychol Sci. 2006 Nov;17(11):981-8 – reference: 14511521 - Neural Comput. 2003 Oct;15(10):2255-79 – reference: 11164731 - Trends Cogn Sci. 2001 Jan 1;5(1):10-16 – reference: 12383780 - Neuron. 2002 Oct 10;36(2):241-63 – reference: 15087550 - Science. 2004 Apr 16;304(5669):452-4 – reference: 12371510 - Neural Netw. 2002 Jun-Jul;15(4-6):535-47 – reference: 9054347 - Science. 1997 Mar 14;275(5306):1593-9 – reference: 8008189 - Neuroscience. 1994 Mar;59(2):229-43 – reference: 9530497 - Annu Rev Neurosci. 1998;21:227-77 – reference: 16286932 - Nat Neurosci. 2005 Dec;8(12):1704-11 – reference: 14973239 - J Neurosci. 2004 Feb 18;24(7):1660-5 – reference: 12383783 - Neuron. 2002 Oct 10;36(2):299-308 – reference: 12631569 - Cereb Cortex. 2003 Apr;13(4):400-8 – reference: 17381269 - Neural Comput. 2007 May;19(5):1344-61 – reference: 11807554 - Nature. 2002 Jan 24;415(6870):429-33 – reference: 15006021 - Neural Comput. 2004 Jan;16(1):1-38 – reference: 16764517 - Neural Comput. 2006 Jul;18(7):1637-77 – reference: 8778300 - J Neurosci. 1996 Feb 15;16(4):1486-510 – reference: 17395573 - Philos Trans R Soc Lond B Biol Sci. 2007 May 29;362(1481):933-42 – reference: 9746140 - Exp Brain Res. 1998 Aug;121(3):350-4 – reference: 17057707 - Nat Neurosci. 2006 Nov;9(11):1432-8 – reference: 17982449 - Nat Neurosci. 2007 Dec;10(12):1625-33 – reference: 18055018 - Brain Res Rev. 2008 Aug;58(2):322-39 – reference: 10746132 - Vision Res. 1999 Oct;39(21):3621-9 – reference: 12371507 - Neural Netw. 2002 Jun-Jul;15(4-6):495-506 – reference: 12718865 - Neuron. 2003 Apr 24;38(2):329-37 – reference: 18484808 - J Vis. 2008 Mar 06;8(3):2.1-15 – reference: 12858951 - Spat Vis. 2003;16(3-4):255-75 – reference: 16767089 - Nat Neurosci. 2006 Jul;9(7):956-63 – reference: 9472488 - Neural Comput. 1998 Feb 15;10(2):403-30 – reference: 12371511 - Neural Netw. 2002 Jun-Jul;15(4-6):549-59 – reference: 2407269 - J Neural Transm Gen Sect. 1990;80(1):9-31 – reference: 17546027 - Nature. 2007 Jun 28;447(7148):1075-80 – reference: 8570606 - Proc Natl Acad Sci U S A. 1996 Jan 23;93(2):628-33 – reference: 18085991 - Neural Comput. 2008 Apr;20(4):873-922 – reference: 16617339 - Nat Neurosci. 2006 May;9(5):690-6 – reference: 16731515 - Neuron. 2006 Jun 1;50(5):781-9 – reference: 16776597 - Annu Rev Neurosci. 2006;29:565-98 – reference: 10421364 - Nature. 1999 Jul 15;400(6741):233-8 – reference: 18579085 - Neuron. 2008 Jun 26;58(6):967-73 – reference: 11488378 - Psychol Rev. 2001 Jul;108(3):550-92 – reference: 16778890 - Nature. 2006 Jun 15;441(7095):876-9 – reference: 16547513 - Nat Neurosci. 2006 Apr;9(4):578-85 – reference: 15036882 - Trends Neurosci. 2004 Mar;27(3):161-8 – reference: 16251991 - Nat Neurosci. 2005 Nov;8(11):1481-9 – reference: 15190354 - Nature. 2004 Jun 10;429(6992):664-7 – reference: 16508903 - Pharmacopsychiatry. 2006 Feb;39 Suppl 1:S80-7 – reference: 17925267 - Prog Brain Res. 2007;165:509-19 – reference: 12868646 - J Opt Soc Am A Opt Image Sci Vis. 2003 Jul;20(7):1419-33 – reference: 12467598 - Neuron. 2002 Dec 5;36(5):955-68 – reference: 17603481 - Nat Neurosci. 2007 Aug;10(8):1020-8 – reference: 18045002 - Neural Comput. 2008 Jan;20(1):91-117 – reference: 8730992 - Vis Neurosci. 1996 Jan-Feb;13(1):87-100 – reference: 15486304 - Science. 2004 Oct 15;306(5695):503-7 – reference: 12868643 - J Opt Soc Am A Opt Image Sci Vis. 2003 Jul;20(7):1391-7 – reference: 17962554 - Science. 2007 Oct 26;318(5850):606-10 – reference: 17435119 - Ann N Y Acad Sci. 2007 May;1104:172-91 – reference: 12417672 - J Neurosci. 2002 Nov 1;22(21):9475-89 – reference: 17670964 - J Neurosci. 2007 Aug 1;27(31):8181-3 – reference: 14724638 - Nature. 2004 Jan 15;427(6971):244-7 – reference: 17670959 - J Neurosci. 2007 Aug 1;27(31):8161-5 – reference: 16563737 - Curr Opin Neurobiol. 2006 Apr;16(2):199-204 – reference: 17600525 - Annu Rev Neurosci. 2007;30:535-74 – reference: 17014301 - Psychol Rev. 2006 Oct;113(4):700-65 – reference: 1464765 - J Neurosci. 1992 Dec;12(12):4745-65 – reference: 17072591 - Psychopharmacology (Berl). 2007 Apr;191(3):391-431 – reference: 7442286 - J Theor Biol. 1980 Aug 21;85(4):673-90 – reference: 8774460 - J Neurosci. 1996 Mar 1;16(5):1936-47 |
| SSID | ssj0004242 |
| Score | 2.443622 |
| SecondaryResourceType | review_article |
| Snippet | Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and... |
| SourceID | unpaywall proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 429 |
| SubjectTerms | Algorithms Animals Bayes Theorem Behavioral Science and Psychology Brain - physiology Cognition Cognitive Psychology Connections between Computational and Neurobiological Perspectives on Decision Making Cost-Benefit Analysis Decision Making Decision Theory Exploratory Behavior Human subjects Humans Markov Chains Models, Psychological Models, Statistical Neurosciences Problem Solving Psychology Reinforcement (Psychology) Signal Detection, Psychological Uncertainty |
| SummonAdditionalLinks | – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Pb9MwFH6C7jB2YKMDVmCQA_TCkqX-kTjHUpgmJCoOVCony3HsHShetaZC5a_nOYm7jYGEuCXykxM_P_t9tp_fB_AaPUZJU2HiXBEaM6tNLATFV4ZoucqoZcxfTv40zc5n7OOcz7sNt1WIdg9Hku2dBp-lydWny8r6IU4R4J5Oxu-miUhYgrNpggX3YSfjiMV7sDObfh5_bZOkpnGeNnxr-DyKR5TP28j3pgqtShequO2T7gDNG4eke7C7dku1-aEWixt-6GwfZGhBG37yLVnXZaJ__pbc8f-beAAPO4gajVubegT3jOvD4djh8vz7JhpGTdBosxvfh91JIIzrw4PtZLo5hOx9R94TNVclNyfRlWmStOpmPzLq2CouTiLlKi8TlZ6s4jHMzj58mZzHHUdDrBkXdWwp4apSBWNoDalOKTGWjYSmXCjFMmFsKWwudKFzXlpDMqu4ybOKGsKrssrpE-i5S2eOIDKkwukzLXJdUKa4FZZYga4EMZ0SiPMG8Db0ktRdAnPPo7GQuJDxOpNeZ1JIJlFnA3izlV62iTv-Ivc8dLjshu9KkpQxDy2x9NW2FLXpD1OUM5frlcwKXNoTkg_gaWsl118pUurTHA5gGHr5uuY__8Jwa1R3_tXbaRB89q-CL6BXX63NMeKlunzZDYpfhuYSUw priority: 102 providerName: Unpaywall |
| Title | Decision theory, reinforcement learning, and the brain |
| URI | https://link.springer.com/article/10.3758/CABN.8.4.429 https://www.ncbi.nlm.nih.gov/pubmed/19033240 https://www.proquest.com/docview/204454569 https://www.proquest.com/docview/69837227 https://link.springer.com/content/pdf/10.3758/CABN.8.4.429.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 8 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1531-135X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004242 issn: 1530-7026 databaseCode: AGYKE dateStart: 20010101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1531-135X dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0004242 issn: 1530-7026 databaseCode: U2A dateStart: 20010301 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1RU9QwEN6R4wF4UDkUDxTzoPcirTVJ2_SxHiADw-mDNwNPmTRNfPAsDHc3zvnr2aTtHXoyw2Mn2zTdTXa_TTa7AO_QYhQsEiZIFWUBt9oEQjB85IiWy4RZzt3l5IthcjriZ5fxZeMoTtpo9_ZI0mtq51ciqP04yD8PQxHyEDXoGqz7TFsdWM-_XJ0fL29CUl8uB5dxFKToXtSh7ivv_22EVpDlvVPRLdiYVTdq_luNx_cMz8kz-NoOuY43-RnOpkWo__yTzfHx__QcnjYYlOT1pNmGJ6bqwk5eof_9a076xEeF-u32LmwM2opwXdhcaMv5DiRHTXUe4u9Czg_JrfFZWLXfcCRNOYofh0RVpaMhhatG8QJGJ8ffB6dBU4Qh0DwW08AyGqtSZZyjuCMdMWos_yQ0i4VSPBHGFsKmQmc6jQtraGJVbNKkZIbGZVGm7CV0quvKvAJiaIn6McpSnTGuYisstQJtBYI2JRDI9eBDKxWpmwzlrlDGWKKn4vglHb-kkFwiv3rwfkF9U2fmeIBuvxWwbNbnRNKIc4cdsfXtohW56U5LVGWuZxOZZOi7U5r2YLeeFcuvZBFzeQx70G-luuz5_0PoLybRyli1KqqWcO-xPe7Dpg9c8XE1r6EzvZ2ZN4iOpsVBsyQOYG1Ec3waDb_lV3e5_wpq |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT-MwEB7xOJQ9ICgLW54-QC9LStZ2EudYCqi8eqISN8txbC4lRbTVqv-esZu0oO5KHCNPHGsmHn9jj-cDOMUVI2OhMEGiKAu41SYQguEjR7Scx8xy7i4nP_bibp_fPUfPZaA4qrLdqyNJ76ldXImg9qLTvuy1RIu30IOuwrorXeVq5fdpe3EPknqyHJzEYZBgcDFLdF96--sStIQrP52J_oDapHhT079qMPi07NxswWaJF0l7ZuBtWDFFHXbaBcbKr1PSJD6D02-N16HWqdjb6rAx92zTHYivSiYd4u8tTs_Ju_EVU7XfHCQldcTLOVFF7mRI5pgjfkL_5vqp0w1KwoRA80iMA8topHKVco6mCXXIqLH8j9AsEkrxWBibCZsIneokyqyhsVWRSeKcGRrlWZ6wXVgrhoX5BcTQHH1ZmCY6ZVxFVlhqBfp1BFhKIOhqwO9Kh1KX1cQdqcVAYlThNC6dxqWQXKLGG3A2l36bVdH4j9xBZQ5ZzqWRpCHnDudh68m8FbXpTjZUYYaTkYxTjLMpTRqwN7Ph4itpyFzNwQY0K6Muev73EJpzky-NVausqAT3v9vjCdS6T48P8uG2d38AGz7hxOfDHMLa-H1ijhDVjLNj_yt_ALwI7sE |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3NT9swFH_amDTggLYyoLANH7ZeRkqwncQ5lrKKwVbtsErcLMexuRRT0Vao_z3PTtIydZM4Rn52rPfs59-z3wfAFzwxChYLE2WKsohbbSIhGH5yRMtlyiznPjj51zC9HPGrm-Sm9qqcNt7uzZNkFdPgszS52emktH6LMwS4p_3e-bAruryL2vQ1vOE-RQKu5RHtrWIiaSicgxs6jjI0NCqn97Xefx9Haxjz2fvoNmzO3UQtHtV4_OwIGryDnRo7kl4l7PfwyrgW7PYc2s13C9IhwZszXJO3YLPfVHJrwdZSyy12Ib2oq-qQEMO4OCEPJmRP1eGikNRlJG5PiHKlpyGFryLxAUaD73_6l1FdPCHSPBGzyDKaqFLlnKOYYh0zaiw_E5olQimeCmMLYTOhc50lhTU0tSoxWVoyQ5OyKDO2Bxvu3pkDIIaWqNfiPNM54yqxwlIrUMcj2FICAVgbvjU8lLrOLO4LXIwlWhie49JzXArJJXK8DV-X1JMqo8Z_6I4acch6X00ljTn3mA9bj5etyE3_yqGcuZ9PZZqjzU1p1ob9Soarv-Qx8_kH29BphLoa-d9T6CxFvjZXrQrXEB6-dMRjePv7YiB__hheH8FW8D0JrjEfYWP2MDefEODMis9hJT8BXmTy_Q |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Pb9MwFH6C7jB2YKMDVmCQA_TCkqX-kTjHUpgmJCoOVCony3HsHShetaZC5a_nOYm7jYGEuCXykxM_P_t9tp_fB_AaPUZJU2HiXBEaM6tNLATFV4ZoucqoZcxfTv40zc5n7OOcz7sNt1WIdg9Hku2dBp-lydWny8r6IU4R4J5Oxu-miUhYgrNpggX3YSfjiMV7sDObfh5_bZOkpnGeNnxr-DyKR5TP28j3pgqtShequO2T7gDNG4eke7C7dku1-aEWixt-6GwfZGhBG37yLVnXZaJ__pbc8f-beAAPO4gajVubegT3jOvD4djh8vz7JhpGTdBosxvfh91JIIzrw4PtZLo5hOx9R94TNVclNyfRlWmStOpmPzLq2CouTiLlKi8TlZ6s4jHMzj58mZzHHUdDrBkXdWwp4apSBWNoDalOKTGWjYSmXCjFMmFsKWwudKFzXlpDMqu4ybOKGsKrssrpE-i5S2eOIDKkwukzLXJdUKa4FZZYga4EMZ0SiPMG8Db0ktRdAnPPo7GQuJDxOpNeZ1JIJlFnA3izlV62iTv-Ivc8dLjshu9KkpQxDy2x9NW2FLXpD1OUM5frlcwKXNoTkg_gaWsl118pUurTHA5gGHr5uuY__8Jwa1R3_tXbaRB89q-CL6BXX63NMeKlunzZDYpfhuYSUw |
| 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=Decision+theory%2C+reinforcement+learning%2C+and+the+brain&rft.jtitle=Cognitive%2C+affective%2C+%26+behavioral+neuroscience&rft.au=Dayan%2C+Peter&rft.au=Daw%2C+Nathaniel+D&rft.date=2008-12-01&rft.pub=Springer+Nature+B.V&rft.issn=1530-7026&rft.eissn=1531-135X&rft.volume=8&rft.issue=4&rft.spage=429&rft_id=info:doi/10.3758%2FCABN.8.4.429&rft.externalDBID=HAS_PDF_LINK&rft.externalDocID=1655323101 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1530-7026&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1530-7026&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1530-7026&client=summon |