Literature DB >> 25163810

Does the semantic content of verbal categories influence categorical perception? An ERP study.

Martin Maier1, Philipp Glage2, Annette Hohlfeld3, Rasha Abdel Rahman2.   

Abstract

Accumulating evidence suggests that visual perception and, in particular, visual discrimination, can be influenced by verbal category boundaries. One issue that still awaits systematic investigation is the specific influence of semantic contents of verbal categories on categorical perception (CP). We tackled this issue with a learning paradigm in which initially unfamiliar, yet realistic objects were associated with either bare labels lacking explicit semantic content or labels that were accompanied by enriched semantic information about the specific meaning of the label. Two to three days after learning, the EEG was recorded while participants performed a lateralized oddball task. Newly acquired verbal category boundaries modulated low-level aspects of visual perception as early as 100-150 ms after stimulus onset, suggesting a genuine influence of language on perception. Importantly, this effect was not further influenced by enriched semantic category information, suggesting that bare labels and the associated minimal and predominantly perceptual information are sufficient for CP. Distinct effects of semantic knowledge independent of category boundaries were found subsequently, starting at about 200 ms, possibly reflecting selective attention to semantically meaningful visual features.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Categorical perception; Event related potentials; Top-down influences; Visual perception

Mesh:

Year:  2014        PMID: 25163810     DOI: 10.1016/j.bandc.2014.07.008

Source DB:  PubMed          Journal:  Brain Cogn        ISSN: 0278-2626            Impact factor:   2.310


  6 in total

1.  No matter how: Top-down effects of verbal and semantic category knowledge on early visual perception.

Authors:  Martin Maier; Rasha Abdel Rahman
Journal:  Cogn Affect Behav Neurosci       Date:  2019-08       Impact factor: 3.282

2.  Integrating unsupervised and reinforcement learning in human categorical perception: A computational model.

Authors:  Giovanni Granato; Emilio Cartoni; Federico Da Rold; Andrea Mattera; Gianluca Baldassarre
Journal:  PLoS One       Date:  2022-05-10       Impact factor: 3.752

3.  N170 Reveals the Categorical Perception Effect of Emotional Valence.

Authors:  Ruyi Qiu; Hailing Wang; Shimin Fu
Journal:  Front Psychol       Date:  2017-11-24

4.  Novel behavioral indicator of explicit awareness reveals temporal course of frontoparietal neural network facilitation during motor learning.

Authors:  Regan R Lawson; Jordan O Gayle; Lewis A Wheaton
Journal:  PLoS One       Date:  2017-04-14       Impact factor: 3.240

5.  Category systems for real-world scenes.

Authors:  Matt D Anderson; Erich W Graf; James H Elder; Krista A Ehinger; Wendy J Adams
Journal:  J Vis       Date:  2021-02-03       Impact factor: 2.240

6.  Group-Level EEG-Processing Pipeline for Flexible Single Trial-Based Analyses Including Linear Mixed Models.

Authors:  Romy Frömer; Martin Maier; Rasha Abdel Rahman
Journal:  Front Neurosci       Date:  2018-02-06       Impact factor: 4.677

  6 in total

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