From bird to sparrow: Learning-induced modulations in fine-grained semantic discrimination

被引:7
|
作者
De Meo, Rosanna [1 ,2 ]
Bourquin, Nathalie M. -P. [1 ,2 ]
Knebel, Jean-Francois [1 ,2 ,3 ,4 ]
Murray, Micah M. [1 ,2 ,3 ,4 ,5 ]
Clarke, Stephanie [1 ,2 ]
机构
[1] Vaudois Univ Hosp Ctr, Dept Clin Neurosci, Neuropsychol & Neurorehabil Serv, CH-1011 Lausanne, Switzerland
[2] Univ Lausanne, CH-1011 Lausanne, Switzerland
[3] Vaudois Univ Hosp Ctr, Dept Radiol, CH-1011 Lausanne, Switzerland
[4] Ctr Biomed Imaging CIBM Lausanne & Geneva, Electroencephalog Brain Mapping Core, CH-1011 Lausanne, Switzerland
[5] Vanderbilt Univ, Med Ctr, Dept Hearing & Speech Sci, Nashville, TN 37235 USA
基金
瑞士国家科学基金会;
关键词
EEG; Intra-categorical discrimination; Category; Auditory; Plasticity; HUMAN AUDITORY-CORTEX; ENVIRONMENTAL SOUNDS; HUMAN BRAIN; ELECTROPHYSIOLOGICAL EVIDENCE; NONHUMAN-PRIMATES; PREFRONTAL CORTEX; VOICE; PLASTICITY; SPEECH; RECOGNITION;
D O I
10.1016/j.neuroimage.2015.05.091
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recognition of environmental sounds is believed to proceed through discrimination steps from broad to more narrow categories. Very little is known about the neural processes that underlie fine-grained discrimination within narrow categories or about their plasticity in relation to newly acquired expertise. We investigated how the cortical representation of birdsongs is modulated by brief training to recognize individual species. During a 60-minute session, participants learned to recognize a set of birdsongs; they improved significantly their performance for trained (T) but not control species (C), which were counterbalanced across participants. Auditory evoked potentials (AEPs) were recorded during pre- and post-training sessions. Pre vs. post changes in AEPs were significantly different between T and C i) at 206-232 ms post stimulus onset within a cluster on the anterior part of the left superior temporal gyrus; ii) at 246-291 ms in the left middle frontal gyrus; and iii) 512-545 ms in the left middle temporal gyrus as well as bilaterally in the cingulate cortex. All effects were driven by weaker activity for T than C species. Thus, expertise in discriminating T species modulated early stages of semantic processing, during and immediately after the time window that sustains the discrimination between human vs. animal vocalizations. Moreover, the training-induced plasticity is reflected by the sharpening of a left lateralized semantic network, including the anterior part of the temporal convexity and the frontal cortex. Training to identify birdsongs influenced, however, also the processing of C species, but at a much later stage. Correct discrimination of untrained sounds seems to require an additional step which results from lower-level features analysis such as apperception. We therefore suggest that the access to objects within an auditory semantic category is different and depends on subject's level of expertise. More specifically, correct intra-categorical auditory discrimination for untrained items follows the temporal hierarchy and transpires in a late stage of semantic processing. On the other hand, correct categorization of individually trained stimuli occurs earlier, during a period contemporaneous with human vs. animal vocalization discrimination, and involves a parallel semantic pathway requiring expertise. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:163 / 173
页数:11
相关论文
共 18 条
  • [1] Fine-Grained Image Analysis With Deep Learning: A Survey
    Wei, Xiu-Shen
    Song, Yi-Zhe
    Mac Aodha, Oisin
    Wu, Jianxin
    Peng, Yuxin
    Tang, Jinhui
    Yang, Jian
    Belongie, Serge
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 44 (12) : 8927 - 8948
  • [2] Automatic fine-grained access control in SCADA by machine learning
    Zhou, Lu
    Su, Chunhua
    Li, Zhen
    Liu, Zhe
    Hancke, Gerhard P.
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 93 : 548 - 559
  • [3] Learning a discriminative region descriptor for fine-grained cultivar identification
    Yang, Chengzhuan
    Lyu, Wenkai
    Yu, Qian
    Jiang, Yunliang
    Zheng, Zhonglong
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2025, 229
  • [4] Few-Learning for Fine-Grained Vehicle Model Recognition
    Kezebou, Landry
    Oludare, Victor
    Panetta, Karen
    Agaian, Sos
    2021 IEEE VIRTUAL IEEE INTERNATIONAL SYMPOSIUM ON TECHNOLOGIES FOR HOMELAND SECURITY, 2021,
  • [5] Reconstructing fine-grained cognition from brain activity
    Anderson, John R.
    Betts, Shawn
    Fincham, Jon M.
    Hope, Ryan
    Walsh, Mathew W.
    NEUROIMAGE, 2020, 221
  • [6] Learning Fine-grained Semantics in Spoken Language Using Visual Grounding
    Wang, Xinsheng
    Tian, Tian
    Zhu, Jihua
    Scharenborg, Odette
    2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2021,
  • [7] Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained Recognition
    Zhang, Chuanyi
    Wang, Qiong
    Xie, Guosen
    Wu, Qi
    Shen, Fumin
    Tang, Zhenmin
    IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 24 : 1198 - 1209
  • [8] From Google Maps to a fine-grained catalog of street trees
    Branson, Steve
    Wegner, Jan Dirk
    Hall, David
    Lang, Nico
    Schindler, Konrad
    Perona, Pietro
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 135 : 13 - 30
  • [9] Fine-grained affect detection in learners' generated content using machine learning
    Kolog, Emmanuel Awuni
    Devine, Samuel Nii Odoi
    Ansong-Gyimah, Kwame
    Agjei, Richard Osei
    EDUCATION AND INFORMATION TECHNOLOGIES, 2019, 24 (06) : 3767 - 3783
  • [10] Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification
    Xie, Saining
    Yang, Tianbao
    Wang, Xiaoyu
    Lin, Yuanqing
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 2645 - 2654