Differential hippocampal and prefrontal-striatal contributions to instance-based and rule-based learning

被引:10
|
作者
Doeller, Christian F.
Opitz, Bertram
Krick, Christoph M.
Mecklinger, Axel
Reith, Wolfgang
机构
[1] UCL, Inst Cognit Neurosci, London WC1N 3AR, England
[2] UCL, Dept Anat & Dev Biol, London WC1N 3AR, England
[3] Univ Saarland, Dept Psychol, Expt Neuropsychol Unit, D-6600 Saarbrucken, Germany
[4] Saarland Univ Hosp, Dept Neuroradiol, Homburg, Germany
关键词
fMRI; instance-based learning; prefrontal cortex; rule-based learning; striatum;
D O I
10.1016/j.neuroimage.2006.02.006
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
It is a topic of current interest whether learning in humans relies on the acquisition of abstract rule knowledge (rule-based learning) or whether it depends on superficial item-specific information (instance-based learning). Here, we identified brain regions that mediate either of the two learning mechanisms by combining fMRI with an experimental protocol shown to be able to dissociate both learning mechanisms. Subjects had to learn object-position conjunctions in several trials and blocks. In a learning condition, either objects (Experiment 1) or positions (Experiment 2) were held constant within-blocks. In contrast to a control condition in which object-position conjunctions were trial-unique, a performance increase within and across-blocks was observed in the learning condition of both experiments. We hypothesized that within-block learning mainly relies on instance-based processes, whereas across-block learning might depend on rule-based mechanisms. A within-block parametric fMRI analysis revealed a learning-related increase of lateral prefrontal and striatal activity and a learning-related decrease of hippocampal activity in both experiments. By contrast, across-block learning was associated with an activation modulation in distinct prefrontal-striatal brain regions, but not in the hippocampus. These data indicate that hippocampal and prefrontal-striatal brain regions differentially contribute to instance-based and rule-based learning. (c) 2006 Elsevier Inc. All rights reserved.
引用
收藏
页码:1802 / 1816
页数:15
相关论文
共 50 条
  • [1] Differential hippocampal and prefrontal-striatal contributions to instance-based and rule-based learning
    Doeller, CF
    Opitz, B
    Krick, CM
    Mecklinger, A
    Reith, W
    JOURNAL OF COGNITIVE NEUROSCIENCE, 2005, : 103 - 103
  • [2] Unifying instance-based and rule-based induction
    Domingos, P
    MACHINE LEARNING, 1996, 24 (02) : 141 - 168
  • [3] Two tests of instance-based and abstract rule-based accounts of invariant learning
    Ward, G
    Churchill, EF
    ACTA PSYCHOLOGICA, 1998, 99 (03) : 235 - 253
  • [4] Prefrontal contributions to rule-based and information-integration category learning
    Schnyer, David M.
    Maddox, W. Todd
    Ell, Shawn
    Davis, Sarah
    Pacheco, Clenni
    Verfaellie, Mieke
    NEUROPSYCHOLOGIA, 2009, 47 (13) : 2995 - 3006
  • [5] Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool
    Dutt, Varun
    Gonzalez, Cleotilde
    COMPUTERS IN HUMAN BEHAVIOR, 2012, 28 (04) : 1227 - 1240
  • [6] Instance-based learning by searching
    Fuchs, M
    INTELLIGENT INFORMATION SYSTEMS, (IIS'97) PROCEEDINGS, 1997, : 189 - 193
  • [7] INSTANCE-BASED LEARNING ALGORITHMS
    AHA, DW
    KIBLER, D
    ALBERT, MK
    MACHINE LEARNING, 1991, 6 (01) : 37 - 66
  • [8] Possibilistic instance-based learning
    Hüllermeier, E
    ARTIFICIAL INTELLIGENCE, 2003, 148 (1-2) : 335 - 383
  • [9] DISTANCE METRICS FOR INSTANCE-BASED LEARNING
    SALZBERG, S
    LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 1991, 542 : 398 - 408
  • [10] Instance-based Learning for Hybrid Planning
    Pandey, Ashutosh
    Schmerl, Bradley
    Garlan, David
    2017 IEEE 2ND INTERNATIONAL WORKSHOPS ON FOUNDATIONS AND APPLICATIONS OF SELF* SYSTEMS (FAS*W), 2017, : 64 - 69