An interplay of fusiform gyrus and hippocampus enables prototype and exemplar-based category learning

被引:16
|
作者
Lech, Robert K. [1 ,3 ]
Gunturkun, Onur [2 ,3 ]
Suchan, Boris [1 ,3 ]
机构
[1] Ruhr Univ Bochum, Dept Neuropsychol, Inst Cognit Neurosci, Univ Str 150, D-44780 Bochum, Germany
[2] Ruhr Univ Bochum, Dept Biopsychol, Inst Cognit Neurosci, Bochum, Germany
[3] Ruhr Univ Bochum, Int Grad Sch Neurosci, Bochum, Germany
关键词
Category learning; Exemplar-based learning; Prototype-based learning; Hippocampus; Fusiform gyrus; SINGLE NEURONS; LINEAR SEPARABILITY; CATEGORIZATION; MODEL; RECOGNITION; EXPERTISE; SYSTEMS; RULE; REPRESENTATION; DISSOCIATIONS;
D O I
10.1016/j.bbr.2016.05.049
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The aim of the present study was to examine the contributions of different brain structures to prototype and exemplar-based category learning using functional magnetic resonance imaging (fMRI). Twenty-eight subjects performed a categorization task in which they had to assign prototypes and exceptions to two different families. This test procedure usually produces different learning curves for prototype and exception stimuli. Our behavioral data replicated these previous findings by showing an initially superior performance for prototypes and typical stimuli and a switch from a prototype-based to an exemplar-based categorization for exceptions in the later learning phases. Since performance varied, we divided participants into learners and non-learners. Analysis of the functional imaging data revealed that the interaction of group (learners vs. non-learners) and block (Block 5 vs. Block 1) yielded an activation of the left fusiform gyrus for the processing of prototypes, and an activation of the right hippocampus for exceptions after learning the categories. Thus, successful prototype- and exemplar-based category learning is associated with activations of complementary neural substrates that constitute object-based processes of the ventral visual stream and their interaction with unique-cue representations, possibly based on sparse coding within the hippocampus. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:239 / 246
页数:8
相关论文
共 50 条
  • [21] Prototype or Exemplar Representations in the 5/5 Category Learning Task
    Chen, Fang
    Li, Peijuan
    Chen, Hao
    Seger, Carol A.
    Liu, Zhiya
    [J]. BEHAVIORAL SCIENCES, 2024, 14 (06)
  • [22] Prototype and exemplar accounts of category learning and attentional allocation: A reassessment
    Zaki, SR
    Nosofsky, RM
    Stanton, RD
    Cohen, AL
    [J]. JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION, 2003, 29 (06) : 1160 - 1173
  • [23] Local Metric Learning for Exemplar-Based Object Detection
    You, Xinge
    Li, Qiang
    Tao, Dacheng
    Ou, Weihua
    Gong, Mingming
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2014, 24 (08) : 1265 - 1276
  • [24] Learning Structured Dictionaries for Exemplar-based Voice Conversion
    Ding, Shaojin
    Liberatore, Christopher
    Gutierrez-Osuna, Ricardo
    [J]. 19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES, 2018, : 481 - 485
  • [25] GENESIS AND USE OF EXEMPLAR VS PROTOTYPE KNOWLEDGE IN ABSTRACT CATEGORY LEARNING
    ROBBINS, D
    BARRESI, J
    COMPTON, P
    FURST, A
    RUSSO, M
    SMITH, MA
    [J]. MEMORY & COGNITION, 1978, 6 (04) : 473 - 480
  • [26] Distinguishing rule- and exemplar-based generalization in learning systems
    Dasgupta, Ishita
    Grant, Erin
    Griffiths, Thomas L.
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022,
  • [27] Unsupervised Exemplar-Based Learning for Improved Document Image Classification
    Abuelwafa, Sherif
    Pedersoli, Marco
    Cheriet, Mohamed
    [J]. IEEE ACCESS, 2019, 7 : 133738 - 133748
  • [28] RULE-BASED AND EXEMPLAR-BASED CLASSIFICATION IN ARTIFICIAL GRAMMAR LEARNING
    MCANDREWS, MP
    MOSCOVITCH, M
    [J]. MEMORY & COGNITION, 1985, 13 (05) : 469 - 475
  • [29] Dynamic matching range in Exemplar-based Learning Classifier System
    Matsushima, Hiroyasu
    Hattori, Kiyohiko
    Sato, Hiroyuki
    Takadama, Keiki
    [J]. 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [30] AN EXEMPLAR-BASED LEARNING-MODEL FOR HYDROSYSTEMS PREDICTION AND CATEGORIZATION
    CHANG, FJ
    CHEN, L
    [J]. JOURNAL OF HYDROLOGY, 1995, 169 (1-4) : 229 - 241