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The Effects of Semantic Priming on the Detection of Words: A Comparison of Different Types of Second-Level Cooccurrences
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
Lieferstatus: |
i.d.R. innert 7-14 Tagen versandfertig |
Veröffentlichung: |
April 2020
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Genre: |
Psychologie / Pädagogik |
ISBN: |
9783346153890 |
EAN-Code:
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9783346153890 |
Verlag: |
Grin Verlag |
Einband: |
Kartoniert |
Sprache: |
English
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Dimensionen: |
H 210 mm / B 148 mm / D 3 mm |
Gewicht: |
62 gr |
Seiten: |
32 |
Zus. Info: |
Paperback |
Bewertung: |
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Inhalt: |
Seminar paper from the year 2019 in the subject Psychology - Cognition, grade: 1,3, University of Wuppertal, language: English, abstract: In order to retrieve information more efficiently and quickly, our central nervous system makes use of implicit preactivation of associative neural networks. In this study, 78 participants were instructed to identify a sequence of word pairs consisting of either German words, nonwords or pseudowords within a lexical decision task.
The procedure was carried out under three different conditions: a) no associations within a word pair, b) connection through general second-level cooccurrences, and c) connection through lemmatized second-level cooccurrences. The analysis of variance revealed highly significant differences in reaction time and error rate between lemmatized second-level cooccurrence compared to general second-level cooccurrences. Both, error rate and reaction time, were lower for lemmatized second-level cooccurrences.
Stimuli consisting of words with second-level association had a positive effect on the reaction time and error rate.
This could be proven due to a stimulus onset asynchrony of 50ms, avoiding semantical competition that could cause inhibitory effects on the reaction time. Linear regression also revealed that lemmatized second-level cooccurrences had a greater influence on the reaction time up to the target and the error rate compared to general second-level cooccurrences. This information could be used to improve models that explain the process of word recognition by adding the influence of the lemmatized second-level cooccurrence. |
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