Literature DB >> 19200808

The role of invariant line junctions in object and visual word recognition.

Marcin Szwed1, Laurent Cohen, Emilie Qiao, Stanislas Dehaene.   

Abstract

Object recognition relies heavily on invariant visual features such as the manner in which lines meet at vertices to form viewpoint-invariant junctions (e.g. T, L). We wondered whether these features also underlie readers' competence for fast recognition of printed words. Since reading is far too recent to have exerted any evolutionary pressure on brain evolution, visual word recognition might be based on pre-existing mechanisms common to all visual object recognition. In a naming task, we presented partially deleted pictures of objects and printed words in which either the vertices or the line midsegments were preserved. Subjects showed an identical pattern of behavior with both objects and words: they made fewer errors and were faster to respond when vertices were preserved. Our results suggest that vertex invariants are used for object recognition and that this evolutionarily ancient mechanism is being co-opted for reading.

Mesh:

Year:  2009        PMID: 19200808     DOI: 10.1016/j.visres.2009.01.003

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  14 in total

1.  Cortical representations of symbols, objects, and faces are pruned back during early childhood.

Authors:  Jessica F Cantlon; Philippe Pinel; Stanislas Dehaene; Kevin A Pelphrey
Journal:  Cereb Cortex       Date:  2010-05-10       Impact factor: 5.357

2.  Perceptual expertise with Chinese characters predicts Chinese reading performance among Hong Kong Chinese children with developmental dyslexia.

Authors:  Yetta Kwailing Wong; Christine Kong-Yan Tong; Ming Lui; Alan C-N Wong
Journal:  PLoS One       Date:  2021-01-22       Impact factor: 3.240

3.  Further evidence for a late locus of holistic word processing: Exploring vertex effect in the word composite task.

Authors:  Paulo Ventura; João Delgado; José C Guerreiro; Francisco Cruz; Vivienne Rosário; António Farinha-Fernandes; Miguel Domingues; Ana Margarida Sousa
Journal:  Atten Percept Psychophys       Date:  2020-10       Impact factor: 2.199

4.  A reliable and valid tool for measuring visual recognition ability with musical notation.

Authors:  Yetta Kwailing Wong; Kelvin F H Lui; Alan C-N Wong
Journal:  Behav Res Methods       Date:  2021-04

5.  The role of line junctions in object recognition: The case of reading musical notation.

Authors:  Yetta Kwailing Wong; Alan C-N Wong
Journal:  Psychon Bull Rev       Date:  2018-08

Review 6.  Learning to see words.

Authors:  Brian A Wandell; Andreas M Rauschecker; Jason D Yeatman
Journal:  Annu Rev Psychol       Date:  2011-07-29       Impact factor: 24.137

7.  Visual feature-tolerance in the reading network.

Authors:  Andreas M Rauschecker; Reno F Bowen; Lee M Perry; Alison M Kevan; Robert F Dougherty; Brian A Wandell
Journal:  Neuron       Date:  2011-09-08       Impact factor: 17.173

8.  Dyslexic Children Show Atypical Cerebellar Activation and Cerebro-Cerebellar Functional Connectivity in Orthographic and Phonological Processing.

Authors:  Xiaoxia Feng; Le Li; Manli Zhang; Xiujie Yang; Mengyu Tian; Weiyi Xie; Yao Lu; Li Liu; Nathalie N Bélanger; Xiangzhi Meng; Guosheng Ding
Journal:  Cerebellum       Date:  2017-04       Impact factor: 3.847

9.  Emergence of a compositional neural code for written words: Recycling of a convolutional neural network for reading.

Authors:  T Hannagan; A Agrawal; L Cohen; S Dehaene
Journal:  Proc Natl Acad Sci U S A       Date:  2021-11-16       Impact factor: 11.205

10.  Automatic top-down processing explains common left occipito-temporal responses to visual words and objects.

Authors:  Ferath Kherif; Goulven Josse; Cathy J Price
Journal:  Cereb Cortex       Date:  2010-04-22       Impact factor: 5.357

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